esa-sceva/satcom-chunk-collection huggingface dataset:
# An Unsupervised Behavioral Modeling and Alerting System Based on Passive Sensing for Elderly Care
## 4 IT Architecture  
Figure 2 shows the IT Architecture of our solution. The structure comprehends four sub-components:  
Figure 1: Example of sensor network deployment.  
* _Private Homes_: the deployed household(s) of elderly people, which serves as the data source of the system.
* _End-users_: patients, family members, and healthcare professionals (i.e., doctors, medical staff, formal caregivers) can access different services, such as entire or partial behavioral record of individual patient or cohort, through a web interface, according to the corresponding permission policies.
* _Server Infrastructure_: It stores raw sensor data and semantic activity patterns after data processing. Respective privacy and security measures, according to _General Data Protection Regulation_ (GDPR) are implemented.
* _IBM Cloud_: offers the cloud-based data analysis service, which access raw sensor data from the server, and send back analytic results to the server, through a secure gateway.
###### Abstract
Chile has incorporated natural gas, renewable energy, and energy-saving policies to mitigate these risks, emphasizing solar and wind power to enhance energy security [10; 11; 12]. Chile has significant renewable energy potential, particularly in solar and wind as well as hydroelectric resources in its central and southern regions [13; 14]. However, its reliance on hydropower makes it vulnerable to climate variability. It requires a strategy to optimally integrate these resources to sustain a high share of renewable electricity and improve resilience against extreme climatic events. Similar patterns have been observed in other drought-prone regions such as the western United States, Australia, and Spain. In the United States, prolonged droughs have reduced hydropower production, increasing fossil fuel use and emissions [3; 15]. Australia has prioritized solar and wind energy to mitigate hydropower limitations [16; 17], while Spain has adopted advanced reservoir management and energy diversification strategies to stabilize supply during water scarcity [18; 19]. These countries' experiences illustrate the importance of implementing proactive energy policies and technological advancements to enhance resilience in the face of extreme drought conditions. Given these challenges, Chile's National Electric System (SEN) must identify optimal solutions to address persistent vulnerabilities while aligning with energy transition goals [20]. Evaluating strategies to combine renewable resources effectively is critical to ensuring a stable and sustainable energy matrix. This study aims to provide a comprehensive bibliometric analysis of global research trends on renewable energy strategies for addressing the impacts of severe droughs and climate change. Specifically, the objective is to analyze the scientific landscape of renewable energy solutions that contribute to mitigating the effects of extreme droughs on electrical systems, identifying key trends and influential studies in the field. To complement this, the study examines the evolution of academic discussions on renewable energy solutions in response to extreme drought conditions, explores international research collaborations and knowledge networks, highlights leading contributors in the field, and assesses the role of renewable energy strategies in electricity generation as an adaptation measure to mitigate extreme climate-related events. The bibliometric analysis covers 2005-2024, revealing a steady increase in research, particularly since 2018, highlighting the global focus on sustainable energy solutions. The remainder of this paper is structured as follows: Section 2 details the bibliometric methodology; Section 3 presents the results and discussion, including publication trends, keyword co-occurrence, and collaboration networks; and Section 4 concludes the study, summarizing key findings and research recommendations.
###### Abstract  
The spatial relationship between transport networks and retail store locations is an important topic in studies related to commercial activities. Much effort has been made to study physical street networks, but they are seldom empirically discussed with considerations of transport flow networks from a temporal perspective. By using Beijing's bus and subway smart card data (SCD) and point of interest (POI) data, this study examined the location patterns of various retail stores and their daily dynamic relationships with three weighted centrality indices in the networks of public transport flows: degree, betweenness, and closeness. The results indicate that most types of retail stores are highly correlated with weighted centrality indices. For the network constructed by total public transport flows in the week, supermarkets, convenience stores, electronics stores, and specialty stores had the highest weighted degree value. By contrast, building material stores and shopping malls had the weighted closeness and weighted betweenness values, respectively. From a temporal perspective, most retail types' largest correlations on weekdays occurred during the after-work period of 19:00 to 21:00. On weekends, shopping malls and electronics stores changed their favorite periods to the daytime, while specialty stores favored the daytime on both weekdays and weekends. In general, the higher store type level of the shopping malls correlates more to weighted closeness or betweenness, and the lower-level store type of convenience stores correlates more to weighted degree. This study provides a temporal analysis that surpasses previous studies on street centrality and can help with urban commercial planning.  
complex network; POI; smart card data; public transport flows; KDE; weighted centrality
## 5 Thermal Characterization
### Dynamic Self-Heating and Thermal Impedance Modeling in InP DHBTs
#### 5.1.1 Thermal Impedance Extraction Method from Low-Frequency S-Parameters  
Figure 8 shows the magnitude and the phases of the \(Y_{12}\)-parameters of one of the HBT geometries (0.7 \(\times\) 5 \(\upmu\)m\({}^{2}\)) under test, depicting two distinct zones over the entire frequency range, separated by a threshold thermal frequency (\(f_{TH}\)) around 0.1 GHz. This frequency denotes a phase shift at the minimum amplitude of the \(Y_{12}\)-parameter. The first region (frequencies higher than \(f_{TH}\)) reflects only static self-heating and the behavior in this region is purely electrical. In the other region, at frequencies below \(f_{TH}\), dynamic self-heating is dominant and electro-thermal networks can be used to extract the thermal impedance. The  
Figure 7: Measured (symbol) and simulated (line) photocurrent of UTC-PD with an active area of (**a**) 5 \(\times\) 15 \(\upmu\)m\({}^{2}\) (**b**) 5 \(\times\) 30 \(\upmu\)m\({}^{2}\) (**c**) 7 \(\times\) 25 \(\upmu\)m\({}^{2}\).  
extracted \(Y\) parameters are used to calculate the normalized thermal impedance using the following [14]:  
\[Z_{TH,norm}(\omega)=\left(\frac{Y_{22}(\omega)-Y_{22}^{AC}}{Y_{22}^{DC}-Y_{22}^{AC }}\right)\left(\frac{I_{C}+V_{BE}Y_{12}^{DC}+V_{CE}Y_{22}^{DC}}{I_{C}+V_{BE}Y_{ 12}(\omega)+V_{CE}Y_{22}(\omega)}\right) \tag{6}\]  
where \(Y_{22}^{AC}\) corresponds to \(Y_{22}\) without dynamic self-heating, i.e., when the thermal capacitance, \(C_{TH}\), is zero, \(Y_{12}^{DC}\) and \(Y_{22}^{DC}\) are the \(Y\) parameters in the DC condition (i.e., \(\omega\)\(\rightarrow\)0). Here, the units of all \(Y\) parameters are in S and \(I_{C}\), \(V_{BE}\) and \(V_{CE}\) are expressed in A, V and V, respectively. The extraction of \(Y_{22 AC}\) has been performed using the HiCuM model simulation, described in the preceding sections, of the \(Y_{22}\) parameters with \(C_{TH}\) set to zero.
## 5 Evaluation: A Healthcare Scenario
### Implementation of the Attack Path Generation
#### 5.2.2. Information and Software  
* **Hospital information management system (A8):** Care 2X software is a patient medical record and staff management system. It supports web-based platforms and a simple user interface. The patient medical record includes patients' identifiable and treatment information;
* **SpaceCom and SpaceStation (A9):** This is the software that operates the infusion pump and resides either on the pump or the space station. Generally, the pump is attached to the space station. We have considered Braun's Infusion System 871305U, which is linked with the SpaceCom 012U000050. SpaceCom is responsible to update two critical functions, i.e., drug library and pump configuration. Drug libraries can prevent incorrect dosing of drugs;
* **Device usage information:** This includes the amount of time used by the patient from the device and the relevant programme data.
## 3 Methods  
### Model Constraint Method  
#### 3.1.1 Model Building  
The quality of the depth migration model directly determines the results of pre-stack depth migration imaging. Currently, the ray tracing-based tomographic velocity inversion method is one of the key modeling techniques for pre-stack depth migration imaging. It mainly uses migration and tomographic iteration for velocity inversion to recover high- and low-frequency information in the velocity field and is widely used in industry for velocity model building.  
The tomographic velocity inversion process includes modeling and model updating. First, the model is discretized into a grid, and ray tracing is performed for each reflection  
Figure 1: Kirchhoff integral pre-stack depth migration processing flow.  
point in the grid. Each ray path can be represented by an equation, and the entire model's ray paths [32] can be expressed as  
\[t_{u}=\sum_{p=1}^{p}\sum_{j=1}^{J}\sum_{m=1}^{M}d_{pjm}\cdot s_{pjm} \tag{2}\]  
where \(P\), \(J\), and \(M\) are the number of grid points; \(t_{u}\) is the seismic wave travel time; \(dpjm\) is the ray path length at the \(pjm\)-th grid point in the model; and \(s_{pjm}\) is the slowness at the \(pjm\)-th grid point. The travel time difference is given by  
\[\Delta t_{u}=\sum_{p=1}^{p}\sum_{j=1}^{J}\sum_{m=1}^{M}d_{pjm}\cdot\Delta s_{ pjm} \tag{3}\]  
where \(\Delta t_{u}\) is the seismic wave travel time difference, and \(\Delta s_{pjm}\) is the slowness error at the \(pjm\)-th grid point. By updating the velocity model using the travel time difference \(\Delta t_{u}\), iterative tomographic velocity modeling can be achieved.
# Research on Variable-Step-Size Adaptive Filter Algorithm with a Momentum Term
## 2 Algorithm Principle
### Principle of Variable-Step LMS Algorithm Based on Incidental Momentum Term  
The momentum \"accumulates\" historical gradients. Therefore, when the weight iteration algorithm includes a momentum term, the weight increment at previous moments will affect the weight change at the current moment. A larger forgetting factor indicates that the weight increment at previous moments has a greater impact on the weight change at the current moment. This discussion only focuses on the impact of the weight increment at the previous moment on the weight increment at the current moment. The new weight algorithm proposed is obtained as follows:  
\[\left\{\begin{array}{rl}\Delta w(n)^{\prime}&=\Delta w(n)+\theta\Delta w(n-1) \\ &=\mu e(n)X(N)+\theta\Delta w(n-1)\\ w(n)&=w(n-1)+\Delta w(n)^{\prime}\end{array}\right. \tag{13}\]  
where \(\theta\) is the forgetting factor. Obviously, if the weight increment \(\Delta w(n)\) at the current moment is in the same direction as the weight increment \(\Delta w(n-1)\) at the previous moment, the magnitude of the weight increment will increase. If they are in the opposite direction, the magnitude of the weight increment will decline. Based on the above discussions, the process of the variable-step LMS algorithm with a momentum term can be obtained as follows:  
1. Determine the parameter: the number of taps for the filter.
2. Initialize the initial value of the filter.
3. Algorithm operation process:  
Filter output: \(y(n)=w^{T}(n-1)X(N)\)  
Error signal: \(e(n)=d(n)-y(n)\)  
Step size adjustment: \(\mu(n)1=\frac{\alpha\big{[}1-\exp(-\beta|e(n)|^{2})\big{]}}{\gamma+\left\|X(N )\right\|^{2}}\)  
Weight coefficient update: \(\Delta w(n)=\mu e(n)X(N)+\theta\Delta w(n-1)\)  
\(w(n)=w(n-1)+\Delta w(n)\)
## 4 Issues of Opportunistic Routing in Asynchronous WSN
### Sender Waiting Time  
Increasing the sender waiting time is also a large source of energy consumption in data transmission. Generally, in an asynchronous duty-cycled WSN, a sender node starts transmission without knowing the wakeup time of the receiver node. Hence, it keeps sending preambles until the receiver wakes up and receives the packet, and therefore this duration must be greater than the sleep interval. This will increase the energy consumption of the sender node, as well as keeping the channel busy so that, in the meantime, a potential transmission can take place between other node pairs. Moreover, in such situations, if a collision occurs and packets fail to reach their destination, all of these efforts and energy will have been spent in vain.
## 2 Related Work
### Gumbel-Softmax  
DARTS with Ensemble Gumbel-Softmax [19] applied Gumbel distribution into Softmax to sample binary variables from architecture distribution. After that, the authors replaced \"argmax\", which is discontinuous, with Softmax to generate Gumbel-Softmax. In this study, the architectures generated in this manner are ensembled to enhance sampling performance. Temperature is also applied in this process, serving as a single hyperparameter. It is not used to regulate the competition among architecture parameters during the search. Instead, the focus is on sampling with binary codes. Ensembling the results of these architectures improves the performance in sampling. The research findings demonstrate that the proposed approach achieves similar performance to DARTS on the CIFAR-10 dataset while reducing the resource requirements from 4 GPU days to 1.5 GPU days.
## 2 Literature Review
### On Developments and Applications of Singular Spectrum Analysis  
Singular Spectrum Analysis was put forward by [PERSON] and [PERSON] [8; 9]. At first, it was widely used because of its effectiveness in reducing residuals. Later, [PERSON] et al. [10; 11] applied this new SSA method in dealing with time series data and showed good results, especially for short, noisy, chaotic signals. At the same time, [PERSON] and [PERSON] [12] used SSA to analyze the time series of global surface air temperatures for the past 135 years. They found out that SSA can generate a secular warming trend and a small number of oscillatory modes; after the residual is separated, the original pattern of the series can be better explained. In 2002, [PERSON] et al. [13] reviewed different SSA methods used in the area of climate variables prediction. The various steps, advantages, and disadvantages were all discussed. In 2007, [PERSON] and [PERSON] [14] analyzed the problem of applying SSA to time series with missing data. Their proposed algorithms result in the extraction of additive components of time series with simultaneous filling in of the missing data. SSA has also been applied to the area of finance and economics. [PERSON] and [PERSON] [15] reviewed recent developments in the theoretical and methodological aspects of SSA in the area of economic and financial time series, and also present some new results. Again, [PERSON] et al. [16] applied univariate and multivariate singular spectrum analysis for predicting the value of changes in the daily pound/dollar exchange rate. A comparison to other models was presented, and the results showed that SSA is superior to other benchmarking models. Also, [PERSON] et al. [17] developed the multivariate SSA (MSSA) technique and demonstrated that MSSA can be a powerful method for time series analysis and forecasting. UK Industrial Production series were used to illustrate the main findings, and the result showed better accuracy compared with the autoregressive integrated moving average (ARIMA) and vector autoregressive (VAR) models.  
During recent years, the SSA algorithm has been widely used in various industries, and [PERSON] et al. [18] even upgraded it as multi-scaled SSA (MSSSA) in making short-term forecasts for certain objects, such as wind power, which may have many chaotic components.
# Voice Pathology Detection and Classification Using Convolutional Neural Network Model
## 5 Conclusions  
This work investigates the possibility of improving the accuracy of voice pathology detection in a search for robust solutions. The main problem hindering progress in this research field is the limited availability of reliable testing samples. Most of the related studies use conventional dysphonic voice features which are easy to predict and clinically interpretable. The Saarbruecken Voice Database (SVD) dataset was selected for this work because it has complex and challenging dysphonic voice pattern analysis features. Subsequently, this paper introduces a novel and real-time system for voice pathology detection using a deep learning Convolutional Neural Network (CNN) model. The model has been implemented in a Voice Pathology Detection system. The development methodology of the Voice Pathology Detection system comprises dataset preparation, learning process, training and validation, and inference process stages. Initially, we apply the SVD dataset in a pre-trained CNN model to set the relative prediction accuracy of the proposed model. This paper aimed to carry out a preliminary study which would clarify whether the use of the deep learning CNN in voice pathology detection, would prove worthy of further exploration. The main contribution of this work is modelling and tuning a deep learning CNN and ResNet34 layers to provide accurate voice pathology detection results. The deep learning CNN of the Voice Pathology Detection system achieves high prediction accuracy results of up to 94.54% accuracy on training data and 95.41% accuracy on testing data. The limited number of samples in general, the limited number of healthy persons compared with the pathology patient and the availability of unique cases in the SVD are the main reasons preventing further improvement of the results. Future work should consider extracting enhanced dataset dimensions and features quality including a new combination of vowels and separating genders. Furthermore, testing different types of CNN and training models might further improve the voice pathology detection approach. Future work could include the application of this method to esophageal voices [46, 47, 48] as patients that had a larynx cancer often have a low intelligibility voice and this reduces a lot their social communication skills. Any contribution to this topic of the esophageal speech will be very helpful for patients with a laryngectomy. Finally, utilization of the proposed system in the real-clinical application is promising through providing a fast-automatic diagnosis and treatment solutions within 3 s to achieve the classification accuracy.  
Formal analysis, M.S.M. and B.G.-Z.; Funding acquisition, I.O.; Investigation, H.A.; Methodology, K.H.A.; Supervision, M.K.A.G.; Validation, S.A.M.; Visualization, F.T.A.-D.; Writing--review & editing, M.A.M. All authors have read and agreed to the published version of the manuscript.  
This research received funding from Basque Country Government.  
The authors declare no conflict of interest.
# Quantum Private Comparison Based on Four-Particle Cluster State
## 4 Security Analysis
### External Attacks
#### 4.1.3 Trojan Horse Attacks  
Trojan horse attacks mainly consist of two types: the delay photon attack and the invisible photon eavesdropping attack. These attacks are common in two-way quantum communication systems that use bidirectional quantum channels for transmitting quantum sequences. Since our protocol transmits quantum sequences over such a bidirectional quantum channel, it becomes vulnerable to Trojan horse attacks. To address these potential risks, the implementation of a wavelength quantum filter (WQF) and a photon number splitter (PNS) is advised [52].
# Intra-Domain Transfer Learning for Fault Diagnosis with Small Samples
## 3 Experiments and Results  
To validate the proposed intra-domain transfer learning strategy and the fault diagnosis model, we select a bearing dataset and a gearbox dataset for a case study comparing various transfer strategies. We also carry out extensive experiments to empirically analyze the impact of training sample scarcity on diagnostic accuracy.
## 2 Methods
### Data Sets
#### 2.2.2 NBI-Zoom Data Set  
For this work, 159 endoscopic NBI-zoom videos from 51 patients were collected. The videos were unaltered except for resizing, i.e., no optically bad videos or frames were removed prior to analysis. Of these patients, 17 were diagnosed with dysplasia in BE and 34 patients had a Non-Dysplastic Barrett's Esophagus (NDBE), as confirmed by histopathology. For each imaged region within the BE of a patient, a biopsy was obtained for histopathological assessment. In total, 59 videos of our data set contain dysplasia and 100 videos show NDBE only. All videos were resized to 256 \(\times\) 256 pixels for computational efficiency (original video resolution: 720 \(\times\) 576 pixels). The average duration of each NBI-video was 10 seconds. These short videos consisted solely of one tissue type, either NDBE or neoplastic BE. From the endoscopic videos, frames were sampled at 5 frames per second and assigned the label of the corresponding video. In this work, all videos of a single patient are always designated to the same set (training or validation). Two examples of regions with a dysplastic lesion and without a lesion are shown in Figures 1a and 1b, respectively.  
Figure 1: (**a**) Two examples of dysplastic lesions. (**b**) Two examples of non-dysplastic BE.
# An Analytic Transform Kernel Derivation Method for Video Codecs
## 2 Proposed Common Sparse Unified Matrix
### Unified DST-3 Matrix (**U**)
First, \(\mathbf{U}\) is multiplied by the \(\mathbf{A,\,B,}\) and \(\mathbf{C}\) unit-element matrices as in (17) in sequential order, thus leading to 32-point DST-4, 16-point DST-4, and 16-point DST-3 transform kernel matrices. Using the resulting matrices, the 16-point DST-3 matrix at the bottom right of the generated matrix is obtained. Finally, the 16-point DCT-2 transform kernel can be obtained using (6) and a 16-point DST-3. Figure 4 shows the overall process of generation of the 16-point DST-3 transform kernel. Figure 5 shows the proposed derivation of the \(N\)-point DCT-2 transform kernel. First, the value of \(N\) is equal to 2. If the condition is satisfied, \(\mathbf{S}_{3,2}\) is obtained by the selection of a \(2\times 2\) block of the right-bottom part of the \(\mathbf{U}\) matrix; otherwise, \(\mathbf{S}_{3,\,N}\) is achieved by the selection of the \(N\times N\) block of the bottom right of the matrix obtained after the multiplication of \(\mathbf{U,\,and\,\,\,E_{64}\). \(\mathbf{E}_{64}\) represents the unit-element matrices \(\mathbf{A,\,B,\,C,\,D,}\) and \(\mathbf{E}\). \(\mathbf{E}_{64,\,N}\) can be obtained by multiplying the unit-element matrices \(\mathbf{A,\,B,\,C,\,D,}\) and \(\mathbf{E}\), depending on \(N\). For example, \(E_{64,\,N}\) is calculated as \(\mathbf{A}\times\mathbf{B}\times\mathbf{C}\times\mathbf{D}\), \(\mathbf{A}\times\mathbf{B}\times\mathbf{C}\), \(\mathbf{A}\times\mathbf{B}\), and \(\mathbf{A}\) for \(N=32\), \(N=16\), \(N=8\), and \(N=4\), respectively. Finally, the \(N\)-point DCT-2 transform kernel was obtained using (6). Figure 5 shows an illustration of the proposed DCT-2 transform kernel derivation. Figure 4: Proposed method to obtain the 16-point DST-3 transform kernel.