esa-sceva/satcom-chunk-collection huggingface dataset:
# Auditing Accessibility of Pavements and Points of Interest in Urban Areas: The 'Seek & Go' Tool +
## 7 Models and Document Schemas  
The \"Seek & Go' application facilitates two primary types of JSON-driven models. The first model pertains to survey schemas, which are generated using the Survey Creator--a visual designer that enables experts to devise surveys and forms. The Survey Creator produces a survey configuration in JSON format, which can subsequently be used by the SurveyJS library to exhibit the survey within the application. While it is not possible to integrate the Survey Creator into the 'Seek & Go' application, its results (JSON configurations) can be utilized with the open-source SurveyJS library. Initially, the application provided support for eighteen surveys, encompassing restrooms, staircases, bus stops, obstacles, pedestrian crossings, and pavements, among others. Across the various survey types, more than four hundred questions could be defined. Each JSON configuration is converted into a contemporary form that supports rules based on user responses. The application corresponds to the graphical element (e.g., pedestrian crossing with the corresponding survey). Example surveys are featured in Figure 4.  
The second model pertains to the various schemas established to facilitate the application's underlying business logic. This includes seven distinct MongoDB collections, as follows:  
1. Connections' collection, which houses documents detailing the interrelationships among different map elements, resulting in a linked graph.
2. Entrances' collection, which retains data concerning the entry POIs.
3. POIs' collection, which encompasses several distinct categories, such as museums and churches.
4. Segments' collection, which serves as the central collection and comprises the requisite nested configurations for obstacles and pedestrian crossings.
5. Surveys' collection, where all responses to completed surveys are stored.
6. Templates' collection, which houses the configuration documents from the Survey Creator.
7. Users' collection, which contains documents relating to the system's users (e.g., credentials, privileges).  
Figure 4: Every survey is the rendered output of a JSON configuration and is related to a graphical representation on the map (**left**). Questions are mainly accessibility oriented (**right**).
# Securing Relay Networks with Artificial Noise: An Error Performance-Based Approach +
## 3 AN Maximizing Relay's SER Performance
### Relation between Phase Selection and Power Allocation  
Having determined the optimal phase selection and the SER-maximizing power distribution, it is interesting to examine their interplay in the SER performance, which we illustrate in Figures 5-7. Figure 5 depicts the SER performance using a deterministic power level \(|z|^{2}=\overline{P}\) and the optimal power allocation. The line denoted as \"non-informative\" represents the non-informative SER performance. In the case of 16-QAM, the non-informative SER is equal to \(15/16\). Comparing Figures 4 and 5, we observe that for a given SNR \(E_{m}/N_{0}\), the derived Theorem 1 yields an upper bound on the SER with the deterministic power usage, i.e., the curves \(\text{SER}\left(0,\sqrt{\overline{P}}\right)\) and \(\text{SER}\left(\frac{\pi}{4},\sqrt{\overline{P}}\right)\). To take a deeper look, we depict in Figure 6 the relation between the optimal power allocation \(x_{1}^{*}\) and \(x_{2}^{*}\). The corresponding phase selection \(\theta_{x_{1}^{*}}\) and \(\theta_{x_{2}^{*}}\) are obtained from evaluating \(\overline{\text{SER}}\left(\sqrt{x_{1}^{*}}\right)\) and \(\overline{\text{SER}}\left(\sqrt{x_{2}^{*}}\right)\) in Equation (19), respectively. Figure 7 plots the probability to transmit \(x_{2}^{*}\), i.e., \(p\) given in Equation (23), noting that the probability to transmit \(x_{1}^{*}\) is given by \((1-p)\).  
In correspondence to Equation (21), we observe from Figures 6 and 7 that as the power of the AN \(\overline{P}\) increases, either the probability \(p\) remains constant and the powers \(x_{1}^{*}\) or, \(x_{2}^{*}\) increase or the values of \(x_{1}^{*}\) and \(x_{2}^{*}\) remain constant (with \(x_{2}^{*}>x_{1}^{*}\)) and the probability \(p\) of transmission with the larger power \(x_{2}^{*}\) increases. To simplify the discussion of Figures 5-7, we partition the values of the AN-to-natural noise ratio (ANR) per symbol into multiple regions by introducing the transition points \(A\)-\(D\). From these figures, we obtain the following observations. _Region I_ (\(0\leq E_{z}/N_{0}<A\)): In this region we observe that when the AN is low, the optimal AN design is to transmit a noise symbol with power \(x_{2}^{*}\) and rotated QAM phase \(\theta=0\) at a constant probability \(p_{0}\), and to transmit no AN (\(x_{1}^{*}=0\)) with probability \((1-p_{0})\). Thus, as \(\overline{P}\) increases, the power \(x_{2}^{*}\) increases while the probability of transmitting AN remains constant.  
Figure 5: SER performance of 16-QAM modulation as a function of \(E_{z}/N_{0}\) without and with optimal \(|z|\), where \(E_{m}/N_{0}=20\) dB, \(h=1\), and \(g=1\).  
Figure 6: Optimal amplitude distribution and the corresponding phase selection for 16-QAM modulation, where \(E_{m}/N_{0}=20\) dB, \(h=1\), and \(g=1\).  
Figure 7: Probability to transmit \(x_{2}^{*}\) for 16-QAM modulation, \(E_{m}/N_{0}=20\) dB, \(h=1\), and \(g=1\).
# Usability and Affects Study of a Virtual Reality System Toward Scorpion Phobia Exposure Therapy
## 4 Discussion
### Strengths and Limitations  
To the best of our knowledge, the proposed system is one of the first works to present a framework for VRET toward scorpion phobia treatment using heart rate biofeedback and usability and affects evaluation. Compared to most studies on the use of VRET for arachnophobia, this study has a relatively large sample (54), where 30 is recommended [25]. When the sample increases, the opportunity to generalize the findings increases the reliability. So, for future studies, we plan to increase the sample.  
First, the proposed framework allows one to develop a virtual environment with biofeedback and usability and affects assessment. Then, the VRET could be extended and scaled to other specific phobias or other arachnids. Although the system has several levels according to the exposure distance, it is necessary to increase the number of levels, the exposure scenarios, and the inclusion of haptic devices for user guidance. In addition, this study includes heart rate measures that allow the monitoring of decisions during interventions. In the future, studies could include other physiological variables, such as electrodermal activity, using a fusion sensor approach.  
The system was tested in an unbalanced sample, both in terms of gender and living area (rural/urban). Although there are studies that report that women are generally more afraid of arachnids, such as spiders [43; 73], in this study we did not address this question, nor did we address the difference depending on the area of living. Future research needs to test with a larger balanced sample, also the sex group and living area group, to probe some hypothesis related to scorptions phobia.  
Another limitation is that the experiment was carried out in a non-clinical sample instead of people with diagnosed scorpion phobia, using initially to this end the widely used spider phobia questionnaire (SPQ) or the fear of spiders questionnaire (FSQ) [25]. However, the inclusion criteria study was not related to a sample diagnosed with arachnophobia.  
Among participants with scorpion phobia, real findings are expected [23]. This study could have benefited from initially using an adaptation of the fear spider questionnaire, and by using the specialized psychological diagnosis, it could avoid using self-reported fear of scorpions [74].
# New Evolution of Robotic Radical Prostatectomy: A Single Center Experience with PERUSIA Technique
###### Abstract  
Radical prostatectomy (RP) is the standard surgical treatment of organ-confined prostate cancer in patients with a life expectancy of at least 10 years. In a recent prospective study, we described the PERUSIA (Posterior, Extraperitoneal, Robotic, Under Santorini, Intrafacial, Antergorade) technique, which is an extraperitoneal full nerve sparing robotic RP, showing its feasibility and safety. The aim of this retrospective study was to evaluate the peri-operative, oncologic, and functional outcomes of the PERUSIA technique. We retrospectively analyzed the data of 454 robotic-assisted radical prostatectomies (RARP) performed using the PERUSIA technique from January 2012 to October 2019. We evaluated perioperative outcomes (operative time, estimated blood loss, catheterization time, complication rate, length of stay), oncological (positive surgical margins and biochemical recurrence), and functional outcomes in terms of urinary incontinence and sexual potency. The overall complication rate was 16%, positive surgical margins were 8.1%, and biochemical recurrence occurred in 8.6% at median follow-up of 47 months. Urinary incontinence was achieved in 69% of cases the day after the removal of the catheter, in 92% at 3 months, and in 97% at 12 months after surgery. The average rate of sexual potency was 72% and 82% respectively 3 and 12 months after surgery. Our findings show that the PERUSIA technique is a safe extraperitoneal approach to perform a full nerve sparing technique providing exciting functional outcomes.  
radical prostatectomy; PERUSIA; nerve-sparing; functional outcomes; oncological outcomes; RARP; oncology
## 4 Results
### Main Architectural Design Parameters: Core Planning and Building Form  
Through the analysis of Figure 5, it becomes apparent that the central core predominates as the frequently employed core configuration, comprising 55% of occurrences. Subsequently, peripheral arrangement follows, representing 45% of occurrences.  
In high-rise residential wooden projects, a central core configuration offers numerous benefits [87]: 1. Structural stability against lateral forces, reducing reinforcement needs. 2. Maximized usable space by centralizing essential services. 3. Enhanced efficiency with shorter travel distances and streamlined access. 4. Improved natural light and views due to larger windows. 5. Flexibility for future adaptations with minimal structural changes. 6. Cost savings through optimized structural efficiency. 7. Facade design freedom with core bearing most of the load. 8. Enhanced fire safety with fire-resistant zones in the core.  
In accordance with the morphological categorization for high-rise residential timber structures, the prismatic configuration, constituting 78% of instances, emerges as the prevailing architectural inclination, whereas unbounded configurations comprise 22% of the aggregate, as shown in Figure 6.  
Figure 5: Case studies categorized by core planning.  
The prevalent application of prismatic forms in high-rise timber residential buildings can be attributed to several factors [33]: 1. Structural efficiency, distributing loads uniformly. 2. Material optimization, aligning with standardized wooden construction materials. 3. Construction simplicity, with regular geometry reducing time and labor costs. 4. Space utilization, maximizing interior space with rectangular or square floor plans. 5. Facade design flexibility, allowing for various architectural features and energy efficiency. 6. Modular construction potential, enhancing efficiency and quality control. 7. Fire safety considerations, with efficient compartmentalization and modern techniques mitigating risks.  
Table 1 shows the comparison between our findings on core planning and building form and findings on non-timber skyscrapers [25; 26; 27].
###### Abstract  
Signal processing and machine learning techniques are changing the clinical practice based on medical imaging from many perspectives. A major topic is related to (i) the development of computer aided diagnosis systems to provide clinicians with novel, non-invasive and low-cost support-tools, and (ii) to the development of new methodologies for the analysis of biomedical data for finding new disease biomarkers. Advancements have been recently achieved in the context of Alzheimer's disease (AD) diagnosis through the use of diffusion weighted imaging (DWI) data. When combined with tractography algorithms, this imaging modality enables the reconstruction of the physical connections of the brain that can be subsequently investigated through a complex network- based approach. A graph metric particularly suited to describe the disruption of the brain connectivity due to AD is _communicability_. In this work, we develop a machine learning framework for the classification and feature importance analysis of AD based on communicability at the whole brain level. We fairly compare the performance of three state-of-the-art classification models, namely support vector machines, random forests and artificial neural networks, on the connectivity networks of a balanced cohort of healthy control subjects and AD patients from the ADNI database. Moreover, we clinically validate the information content of the communicability metric by performing a feature importance analysis. Both performance comparison and feature importance analysis provide evidence of the robustness of the method. The results obtained confirm that the whole brain structural communicability alterations due to AD are a valuable _biomarker_ for the characterization and investigation of pathological conditions.  
Keywords:computer aided diagnosis; Alzheimer's disease; machine learning; brain connectivity +
Footnote †: journal: Medical Imaging
# A CNN-LSTM-Attention Model for Near-Crash Event Identification on Mountainous Roads
## 5 Results  
### Results of Data Processing  
In this study, an adaptive mean-standard deviation filter is first used to denoise the naturalistic driving data. Figure 7 shows a comparison between the SG (Savitzky-Golay) filter [38] and the adaptive mean-standard deviation filter. From the figure, it can be seen that the two filters can effectively filter out abrupt anomalous points, such as the upward and downward anomalies near sample point 8000. However, from an overall trend perspective, the SG filter adheres too closely to the original curve, only removing clearly abnormal points and being less effective for smaller anomalies. In contrast, the adaptive mean-standard deviation filter used in this study can effectively remove most of the anomalous points while smooth the data and preserve the characteristic information of the original signal.  
For the subsequent model training, we extract normal events and near-crash events from naturalistic driving data based on the threshold method combined with manual verification. Eventually, a dataset containing 3111 normal events and 301 near-crash events was constructed, with each event containing 200 frames and having 8 feature variables.  
Next, the random forest algorithm is used for feature selection. The process begins by determining the optimal number of feature variables for splitting nodes in the random forest model based on the out-of-bag (OOB) error produced by the bootstrap samples that are not used in the model's training. As shown in Figure 8, the out-of-bag error reaches its minimum value at 0.0862 when the number of feature variables is 5. Therefore, the number of node feature variables is set to 5. Hence, the number of decision trees in the random forest model is determined. As shown in Figure 9, the out-of-bag error gradually decreases as more decision trees are added, eventually plateauing beyond 300 trees. The random forest model is configured with 300 decision trees to ensure efficiency and accuracy in feature selection, which can strike a balance between computational resource use and predictive performance.  
Figure 8: Determination of node feature variables based on OOB error.  
Figure 7: Comparison of the effects of mean filtering and adaptive mean-standard deviation filtering.  
Finally, feature selection is conducted based on the determined number of decision trees, and the importance of each feature variable is assessed, as shown in Table 2. This evaluation results in the selection of the top five motion features based on their importance, which are subsequently used as input variables for the near-crash event identification model. These critical features include x-axis acceleration, y-axis acceleration, y-axis angular velocity, heading angle, and vehicle speed, which offer a robust foundation for accurately detecting near-crash events in naturalistic driving scenarios.
# Energy-Efficient Connected-Coverage Scheme in Wireless Sensor Networks
## 6 Experimental Simulation
### MCCA Algorithm Simulation Results  
#### 6.1.1 Parameter Setting  
400 sensor nodes and 64 MTPs are randomly deployed in the area of 100 m \(\times\) 100 m. To form a connected communication network, the sensing radius \(R_{\rm s}\) and the communication radius \(R_{\rm c}\) are set as 8.8375 m and 17.6750 m, respectively. The energy consumption rate of each activated sensor node is 0.1 J/s. While the sleeping node does not consume energy. The population size is assumed as 100, while the maximum genetic algebra is 15. Set the crossover rate as 0.7. The value of three weighting coefficients \(\lambda_{1}\), \(\lambda_{2}\), and \(\lambda_{3}\) are 0.6, 0.2, 0.2, respectively.
###### Abstract  
An ultra-wideband common-mode (CM) filter for a gigahertz (GHz) data rate signal is proposed in this paper. The proposed filter was designed only on the printed circuit board (PCB) ground plane; no additional components wererequired. We took advantage of producing second-order transmission zero by an asymmetrical magnified coupled DCS to extend the suppression bandwidth. Full-wave simulations and equivalent circuit models of the DCS resonator were established to predict the suppression performance. The measured differential-mode insertion loss (S\({}_{\text{dd21}}\)) from direct current (DC) to 12.35 GHz was obtained within the \(-3\) dB definitionin the frequency domain. The CM noise was suppressed by more than10 dB in the frequency range from 2.9 GHz to 16.2 GHz. The fractional bandwidth (FBW) reached 139.3%. The proposed filter blocked 62.3% of the CM noise magnitude in the time domain measurement. In addition, the eye diagram measurement proved that good transmission quality was maintained. The proposed filter can be widely implemented to reduce electromagnetic interference (EMI) in radio frequency (RF) andWi-Fi (wireless fidelitystandard) 5 and 6E wireless communication applications.  
bandstop filter (BSF); common-mode noise; defected ground structure (DGS); electromagnetic interference (EMI); signal integrity (SI) +
Footnote †: journal: Applied  
Article
# Surface Application of Different Insecticides Against Two Coleopteran Pests of Stored Products
###### Abstract  
The present study highlights the critical role of surface type, insect species, and exposure duration in determining the efficacy of surface-applied insecticides in stored-product pest management. Four insecticides were sprayed and evaluated on different surfaces (concrete, metallic, plastic, and ceramic) against two beetles: the red flour beetle and the tobacco beetle. Alpha-cypermethrin and spinosad exhibited rapid and high efficacy, particularly on non-porous surfaces such as metal and ceramic, whereas pirimiphos-methyl was less effective initially and required extended exposure to achieve complete mortality, especially against _Tribolium castaneum_. In contrast, _Lasioderma serricorne_ showed greater susceptibility across all insecticides and surfaces. Spinosad maintained high efficacy across all surface types, suggesting broader applicability under variable conditions. The reduced performance of insecticides on concrete surfaces underscores the influence of substrate porosity on insecticide bioavailability. Additionally, the observed delayed mortality effect in all treatments indicates that even brief exposure can result in lethal outcomes, emphasizing the long-term potential of these applications. These findings underscore the need for surface-specific application strategies and support the integration of surface treatments into comprehensive pest management programs. Further research is warranted under simulated field conditions to assess residual efficacy over time and in the presence of food, thereby enhancing the relevance of laboratory findings to real-world storage environments.  
surface types; contact insecticides; efficacy; stored product insects Article
# A Carbon Benefits-Based Signal Control Method in a Connected Environment
## 1 Upper-level model
### Solution  
In this section, a GA-PSO algorithm is designed to solve the proposed bi-level optimization model, optimizing the signal timing and the optimal trajectories of CBCVs. The methodology proceeds as follows:  
Step 1: Initialization. Initialize parameters for the GA-PSO, encompassing population size \(N_{p}\), crossover rate \(CR\), mutation rate \(MR\), inertia weight \(W_{i}\), cognitive constant \(c_{1}\), social constant \(c_{2}\), and the maximum number of iterations \(R\).  
Step 2: Generation. Generate \(N_{p}\) initial populations of signal plans, ensuring compliance with signal control constraints Equations (11)-(16).  
Step 3: Solution. For each population, solve the standard MILP model P2 to optimize the trajectories \(\mathbf{x}^{\omega_{k}}\) of CBCVs using common solvers and subsequently predict the trajectories of non-CBCVs \(\mathbf{x}^{\omega_{k}}\).  
Step 4: Fitness Calculation. Evaluate the fitness of each upper-level model configuration using the predicted trajectories and corresponding signal plans.  
Step 5: Iteration Check. Assess whether the optimization iteration limit R has been reached. If so, output the optimal result. If not, advance to step 6.  
Step 6: Selection and Update. Select individuals with high fitness scores and update the GA-PSO population. This includes selection, crossover, and mutation processes (GA components) as well as updates to velocity and position (PSO components). Subsequently, cycle back to Step 3.  
The algorithm flow chart is depicted in Figure 6.  
Figure 5: Carbon benefit calculation module.
# Acquisition of Weak BDS Signal in the Lunar Environment
###### Abstract  
Autonomous navigation using the GNSS has been increasingly used in lunar exploration missions, as it is considered an efficient method for spacecraft to operate without relying on ground facilities. However, so far, only GPS and Galileo have been studied and implemented in lunar missions, whereas the potential of BDS-3 for such missions remains largely unexplored. This paper presents an analysis and evaluation of the navigation service capabilities for spacecraft at lunar altitudes by utilizing existing GNSS satellite resources in orbit. In detail, we investigate the number of GNSS signals received by low lunar orbit (LLO) receivers, as well as the carrier-to-noise ratio (\(C/N_{0}\)), Doppler shift, and geometric dilution of precision (GDOP) of the received signal. Additionally, a digital intermediate frequency (IF) signal simulation program to emulate the BDS B1I signal is utilized, which allows to freely set the carrier-to-noise ratio, Doppler shift, and code phase. Based on this framework, we discussed a high-sensitivity BDS B1I signal receiver and validated its performance with simulated signals. We verified the capabilities of coherent integration, non-coherent integration, differential integration, and semi-bit methods to detect weak BDS B1I signals in a lunar environment. The results indicate that the semi-bit coherent differential integration method is still capable of acquiring signals at a \(C/N_{0}\) of 15 dB-Hz and can effectively suppress the navigation data bit sign transition.  
lunar navigation; BDS receiver; high sensitivity; weak signal acquisition 2023152445[[https://doi.org/10.3390/rs15092445](https://doi.org/10.3390/rs15092445)]([https://doi.org/10.3390/rs15092445](https://doi.org/10.3390/rs15092445))