esa-sceva/satcom-chunk-collection huggingface dataset:
# Testing of Indoor Obstacle-Detection Prototypes Designed for Visually Impaired Persons
## 2 Materials and Methods
### Testing Environment and Methods  
#### 2.3.1 Test Environment  
The main function of a test environment is to provide a controlled and isolated space where software or systems can be tested thoroughly before being deployed into a production environment. It is a simulated setting that mimics the conditions of the actual production environment as closely as possible. The test environment used for this prototype consists of the following:  
* An unfurnished room that is wide enough to enable the prototypes to be placed at different distances without other interference.
* On the floor, different positions are marked for the obstacles (both square and cylinder).
* From each obstacle position, markers (blue tape) are placed on the floor at the following distances: 50, 70, 100, 140, 180, 210 and 230/250 cm.
* The prototype and a laptop that will collect the log files is placed on a table with wheels so it can be easily moved to any marker. The following obstacles are used:
* The square obstacle is simulated with a cardboard box with sides measuring 40 cm.
* The cylinder obstacle is simulated using a 38 cm diameter cardboard box.
* For a specific test: a PVC pipe with a 2 cm diameter and a matchbox.
* Different types of materials used to simulate obstacles: cardboard, concrete, slicker, glass, mirror, MDF, paper, iron, plastic, wood, PAL, jeans, cotton, leather, linen. As a note, these materials will be tested under only one condition: in the full path of the sensor. Tor the confusion matrix results, the readings will take place for 10 min.  
The obstacles will be placed in three different positions based on the location of the prototype. This is explained for the square obstacle; the same conditions were applied in testing both the square and the round obstacles as well.  
* In the full path of the sensor's directivity, as shown in Figure 4--the sensor has a 15 degree measuring angle.
* The obstacle is placed so it covers only half of the measuring angle of the sensor, as shown in Figure 5.  
Figure 4: Positioning of the square obstacle fully in front of the prototypes—view from above.  
Figure 5: Positioning of the square obstacle so it covers only half of the measuring angle of the sensor—view from above.  
The obstacle is placed so it covers only a quarter of the measuring angle of the sensor, as shown in Figure 6.
# An Energy-Efficient Approach to Enhance Virtual Sensors Provisioning in Sensor Clouds Environments
## 5 Performance Evaluation
### Performance Metrics  
As explained earlier, ACxSIMv2 performs the provisioning task using two main algorithms. The first one, ACASIMv2, clusters the physical sensor nodes based on the similarity of their measurements and the later, ACOSIMv2, selects only a subset of nodes that minimize the energy consumption. Hence, the mean squared error (MSE) and energy consumption were chosen to evaluate ACxSIMv2 properly. As the created clusters may include sensor nodes not physically close to each other, MSE helps to investigate how reliable is the measurements sent by the selected sensors, i.e., how they can be representative of multiple regions. The energy consumption measures how well ACOSIMv2 selects the sensor nodes in order to save energy.
## 4 Use Case and Simulation of the Proposed System
### PMIPv6/AAND
#### 4.4.3 F-PMIPv6--Handover Analysis  
The transition is evaluated in conjunction with the signal flow of F-PMIPv6 based on [10; 18]. In F-PMIPv6, a new link is created to make mobility faster. Therefore, the signal is transmitted twice to make the versatility session continuous.  
In the predictive mode, the F-PMIPv6 HOD is determined as follows. There is a need for a delay in authentication because, in the predictive mode, the authentication of the registration takes place before the MN attachment. HOD of F-PMIPv6 value calculated on Equation (4)  
\[\begin{array}{ll}\text{HOD}_{\text{F-PMIPv6 (Proactive Mode)}}=&\text{Layer 2 connection}+\left( \text{t}_{\text{AAAAreq}}+\text{t}_{\text{AAAAres}}\right)+\left(\text{t}_{ \text{PBU}}+\text{t}_{\text{PBA}}\right)\\ &+2(\text{t}_{\text{RS}}+\text{t}_{\text{RA}})+\text{TTD}_{\text{Data}}\end{array} \tag{4}\]  
F-PMIPv6--Handover Simulation Result  
Here, the MN begins with its if_1, i.e., the WLAN. Then if_2 i.e., Wi-Max, comes into the simulation at the 11 th second, however the MN handover of the signal to if_2 occurs at 13.9 seconds. Then, if_3 3G joins the simulation at the 24.5 th second, but the MN handover of the signal to 3G occurs at the 27 th second. In Figure 15 are shown the Handover Simulation result of PMIPv6/F-PMIPv6.  
Figure 14: PMIPv6-Handover Simulation Result [9].  
Figure 15: PMIPv6/F-PMIPv6—Handover Simulation result [9].
## 3 Results and Discussion
### Adsorption Thermodynamics  
The results of the thermodynamic study are presented in Table 5. Regarding ammonium adsorption, the \(\Delta\)G value is negative for all substrates, indicating that the adsorption process is spontaneous. The positive \(\Delta\)H values for zeolite substrates indicate that the adsorption process is endothermic and favored by increasing temperature, while for gravel, it is exothermic and favored by decreasing temperature. The positive value of \(\Delta\)S indicates increased randomness at the solid-solution interface during adsorption. In other words, the ammonium ions on the adsorbent's surface are in a more chaotic distribution compared to those in the aqueous solution. Regarding orthophosphate adsorption, the \(\Delta\)H values are positive for all substrates, indicating that the adsorption process is endothermic. Comparing the results of the present work with those in the literature [33; 35; 48; 77; 78], it emerges that the effect of temperature on the adsorption of ammonium and orthophosphates and the nature of the adsorption process (endothermic or exothermic) depends both on the type and characteristics of the adsorbent and on the selected experimental conditions.
###### Abstract  
High-resolution water depth data are of great significance in island research and coastal ecosystem monitoring. However, the acquisition of high-resolution imagery has been a challenge due to the difficulties and high costs associated with obtaining such data. To address this issue, this study proposes a water depth inversion method based on Gaofen-1 (GF-1) satellite data, which integrates multi-source satellite data to obtain high-resolution bathymmetric data. Specifically, the research utilizes bathymmetric data derived from Sentinel-2 and Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) as prior information, combined with high-resolution imagery obtained from the GF-1 satellite constellation (GF-1B/C/D). Then, it employs a scale-invariant effect to map bathymetry with a spatial resolution of 2 m, applied to four study areas in the Pacific Islands. The results are further evaluated using ICESat-2 data, which demonstrate that the water depth inversion results from this study possess high accuracy, with \(R^{2}\) values exceeding 0.85, root mean square error (RMSE) ranging from 0.56 to 0.90 m, with an average of 0.7125 m, and mean absolute error (MAE) ranging from 0.43 to 0.76 m, with an average of 0.55 m. Additionally, this paper discusses the applicability of the scale-invariant assumption in this research and the improvements of the quadratic polynomial ratio model (QPRM) method compared to the classical linear ratio model (CLRM) method. The findings indicate that the integration of multi-source satellite remote sensing data based on the scale-invariant effect can effectively obtain high-precision, high-resolution bathymmetric data, providing significant reference value for the application of GF-1 satellites in high-resolution bathymetry mapping.  
b  
202517640  
2 January 2025
## 3 Results
### Application to Real Samples  
The surface water samples were taken from two rivers, Sperchios (RW 1) and Gorgopotamos (RW 2) at Fthioidia, Greece on 27 March 2021, and from three sea water samples, Port of Stylida (SW 1) and Karavomylos (SW 2) at Fthioidia, Greece on 29 March 2021 and from Aretsou (SW 3) of the municipality of Kalamaria at Thessaloniki, Greece on 25 May 2021. Neither ALP nor FLT was detected in the five samples without spiking appropriate amounts of stock solutions. Each concentration level (0, 25 and 50 ng/mL) was analyzed thrice to calculate the RSDs and the R\({}_{\text{rel}}\). A typical chromatograph of a lake water sample is shown at Figure 16, while the rest are shown in Figures S2-S5. Lastly, in Table 4 the results of the real surface water samples are demonstrated, where low RSDs were obtained along with high relative recoveries, highlighting that the proposed method is highly accurate and precise for its application to real surface water samples for
## Appendix B The Procedures and Code for Extracting Phenological Transition Dates (PTDs)  
The procedures to extract PTDs from time series of vegetation indexes (VIs) or gross primary productivity (GPP) are as follows (also referring to Figure A7:  
* Try to find interminD. The linking point of two peaks (interminD) is the minimum between the two peaks between the Julian day of the year (Doy) 150-250.
* Once we found the interminD, the time series is split into two parts and for each part the main PTDs are computed. The date of POS is calculated by determining the date at which the maximum value of the time series (using f(t) to refer to the time series hereafter) is reached. Baseline and maxline are the minimum and maximum value in each part of f(t), respectively.
* The maximum and minimum of the first derivative of the f(t) (f'(t)) represent the maximum slopes of the upward and downward period (dashed lines). The intersections between these lines and the baseline are defined as upward day (UD) and recession day (RD). UD stands for the value when the f(t) begins to increase during the Green-up period. RD stands for the value when the f(t) stops decreasing during the Dry-down period. The intersections between these lines and maxline are the saturating day (SD) and downward day (DD). The SD indicates when the plants begin to reach full greenness or maximum photosynthesis, while DD stands for the date when plants begin to senesce [61].  
Figure A6: Time series of green chromatic coordinate (GCC) computed for Grass, Tree and Ecosystem region of interest (ROI) in four experimental sites (FLUXNET IDs are ES-LM1, ES-LM2, ES-LMa, and ES-Abr, respectively) during year of 2014 to 2017. The increase of GCC for Tree ROI during spring is concomitant with the leaf flushing.  
\(\text{SOS}_{\text{trs}}\) and EOS\({}_{\text{trs}}\) are retrieved by computing the date when the value reaches 50% of the maximum in the upward and downward period, respectively [62].  
The Code for extracting PTDs is shared in the attached file (PTD_extraction.R). You can also use the example in the attached example folder for a test to extract PTDs.  
Figure A7: Procedures to extract phenological transition dates metrics (PTDs) from different vegetation indexes (VIs) or daily maximum gross primary productivity (GPP). The procedures for extracting PTDs from seasonal VIs or GPP trajectory f(t) are described in detail in the text of Appendix B (The number in the left of each panel corresponds to the order of procedures in the Appendix B). The black circles represent the original vegetation indexes value (CamGCC, CamNDVI, CamNIRv, CamRVI, or GPP). Results of the smoothing procedure are shown by red circles. The PTDs extracted in each step are highlighted with orange color, while the threshold values are indicated by the blue dotted line. The PTDs colored with dark green are the PTDs we study in this article. Their names are corresponding to UD, SOS\({}_{\text{trs}}\), POS1, POS2, EOS\({}_{\text{trs}}\), and RD in the Table 2, respectively.
# Resistance of \(L\). _monocytogenes_ and \(S\). Typhimurium
## Materials and Methods  
### Experimental Design  
In the first part of this research, _L. monocytogenes_ and \(S\). Typhimurium biofilms with five different ages were developed, i.e., 1, 2, 3, 7, and 10 day(s) old (starting from inoculation of the surfaces). For each of the biofilm ages, the cell density on general and selective medium was determined before and after a 10 min during treatment with Cold Atmospheric Plasma (CAP). CAP treatment conditions were selected based on the research that was done by [PERSON] et al. [17]. In that research, the most effective inactivation of the one-day old (reference) model biofilms was obtained by using a Dielectric Barrier Discharge (DBD) electrode, a helium gas flow, and an input voltage of 21.88 V (which resulted in a dissipated plasma power of approximately 7 W). In addition, for each of the biofilm ages in the presented research, the total biofilm mass of the untreated biofilm was determined using the crystal violet assay [25]. For both quantification methods, i.e., cell enumeration and crystal violet assay, three biological replicates were used.  
In the second part of this research, complete inactivation curves were developed for two biofilm ages (for each species), i.e., for (i) the one-day old reference biofilm and (ii) the biofilm age, which showed the highest resistance towards CAP treatment in the first part of this research. To develop the inactivation curves, biofilms were treated with the same optimal CAP conditions, however, for different treatment times (i.e., 0, 1, 2, 5, 7.5, 10, 15, 20, 25, and 30 min). For each of the treatment times, two biological replicates were assessed and the cell densities were determined using general and selective media.  
For both parts of this study, medium refreshment was not implemented in the experimental design as the authors intended to simulate the effect of equipment downtime (following an inefficient cleaning procedure) on the biofilm resistance towards CAP treatment.
## 1 Introduction  
Language is a natural way of exchanging information between people. It is a fast and convenient way of communicating, which explains the popularity of instant messengers used by millions of users every day. It is also a way to store and exchange business, government, medical and research data in form of text documents. Because of its universal usage, humankind is generating a vast amount of text data every day due to the usage of the Internet.  
The complex nature of human language makes it hard for computers to process in its raw form. To effectively use an increasing number of documents, it is necessary to transform text to machine-processable format, that is, into the form of numbers. Currently, a prominent way of creating numerical representation of documents is to map text into a geometrical space in such a way that different documents are far away from each other and similar ones are close. All algorithms described in this survey built upon this assumption.  
The motivation for the study was to find out to what extent the use of deep neural networks improves the quality of representations of texts and how the improvement of quality influences computation time. Some papers aim to study the influence of different text representations [1, 2, 3]. An early study on using neural networks for text representation was presented in [4]. A more recent and extensive survey was presented in [5], including a wide theoretical review of existing algorithms, but lacking a comparison of the effectiveness of the algorithms for selected datasets. In other studies, evaluation has been performed for texts oriented in a particular domain [6; 7].  
The contribution of this paper is the proposed HWAN method, which extends the HAN model by including statistical features of the text in the latent representation, thus improving the text classification performance in four out of five datasets. The experiments conducted show that it is possible to improve the quality of document representation by incorporating statistical features learned on a large external corpus.  
This paper is structured as follows: In Section 2.1, natural language processing methods are described. In Sections 2 and 3 consecutively, statistical models and deep neural networks are presented. State-of-the-art models are presented in Section 4. Section 5.1 describes the datasets used for the evaluation of the presented algorithms. The experiment setup is presented in Section 5 and comparison results in Section 6. Section 7 presents our approach for the improvement of a Hierarchical Attention Network that is state-of-the-art for the neural representation of a text. Section 8 discusses the achieved results. We finalise our paper with the conclusions section.
## 4 Simulation Setup and Experimental Results
### ECC Key Distribution and Management Experimental Results  
We have tested the encryption approach proposed using different parameters, including power consumption, hop counts, transferred number of messages, and packet header size. In this part, we present the experiment results. The following table depicts how the rekeying operation performs efficiently on a conventional embedded processor that is running a full operating system instead of a customized and optimized image. In Table 4, the header specifies the number of computing nodes participating in the cluster. As we have used eight physical devices, those scenarios (marked with an asterisk) in which we needed a number higher than eight computational threads inside each device were used. The threads were evenly distributed among the physical devices. For example, in the case where we used 16 the number of threads obviously exceeded the number of physical devices. In this case, we created two threads per device. Each thread emulated a node. In the last case in which we tested 1024 threads, a total of 128 computational threads were run on each processor.  
For \(n\) number of nodes in the sensor network, computational complexity of the cyclic ECC key generation and distribution in the algorithm one table can be computed as follows using generator point \(G\), the elliptic curve, and \(r_{i}\) as the private key for node \(i\). To generate a shared key for a node, scalar multiplication has to be performed as indicated in Equation (10).  
\[P_{i}=(r_{1}.r_{2}.r_{3}.r_{4}.\ldots\ldots\ldots r_{n}).G \tag{10}\]  
where \"\(.\)\" operator is an EC scalar multiplication. Hence for \(n\) nodes the total complexity can be calculated using Equation (11).  
\[\mathcal{O}(n(r^{n}.G)) \tag{11}\]
## 4 Research and Project Aimed at Developing Modules of a Classification and Expert System  
The expert system for FSGS (FNDSS) is modular, guiding clinicians through key steps of patient management. It incorporates structured protocols for diagnosis, treatment initiation, evaluation, and follow-up, ensuring adherence to clinical standards while allowing flexibility for individual cases. The core modules are described below:  
* Diagnosis: This module encompasses the initial steps for confirming FSGS and assessing its severity, including:
* Patient history: systematic collection of clinical data to identify risk factors, secondary causes, and symptoms suggestive of FSGS.
* Diagnostic tests: recommendations for laboratory investigations and imaging studies, including assessments of kidney function.
* Kidney biopsy: a decision-making pathway for interpretation of histopathological findings.
* Risk assessment: evaluation of disease progression risk based on clinical indicators.
* Management and treatment: This module provides structured guidance for patient care based on disease severity and clinical characteristics:
* Basic management and treatment: emphasizes nephprotective strategies, including lifestyle modifications, dietary adjustments, and pharmacological interventions.
* Induction therapy with periodic evaluation: proposes immunosuppressive treatments tailored to disease severity, with regular monitoring to assess effectiveness and side effects.
* Final classification: at designated intervals, this module evaluates the patient's response to treatment. Based on clinical markers and outcomes, the system categorizes the disease into remission, partial response, or resistance.
* Treatment continuation: this module offers guidance for adjusting or continuing therapy. It includes strategies for maintaining remission, addressing partial responses, and managing relapses or resistance to first-line treatments.
* FSGS analysis: This module facilitates an in-depth review of FSGS cases, including analysis of disease patterns, treatment outcomes, and progression trends. It serves as a decision-support tool for complex or atypical cases, ensuring an evidence-based approach.  
The expert system's modular design (see Figure 1) ensures a clear and logical progression through each stage of patient management. A user-friendly graphical interface enhances its utility, presenting clinicians with interactive decision trees, data visualization tools, and step-by-step guidance for diagnosis, treatment, and follow-up. By standardizing processes and incorporating current clinical evidence, the system aims to optimize outcomes for FSGS patients.  
In the following sections, each module is discussed in detail, highlighting its methodology, functionality, and integration within the broader framework of FSGS management.
###### Abstract  
Pile-supported structures incorporating batter piles are commonly used, and can be installed both on the horizontal and inclined ground. Recent studies have considered the positive role of batter piles during earthquakes, highlighting their satisfactory contribution to structural seismic performance. However, in these structures, even though the dynamic system responses can vary greatly depending on the ground slope, few previous studies have evaluated the seismic performance of batter piles relative to the ground slope. Therefore, this study evaluates the seismic performance of pile-supported structures with batter piles, relative to the ground slope using dynamic centrifuge model tests. The acceleration, displacement, moment, and axial force of the system were experimentally derived and reviewed, and the pile moment and axial force (\(M\)-\(N\)) interaction diagrams of the pile cross-sections were analyzed. The installation of the batter piles resulted in a greater reduction in the system response in the inclined-ground model (acceleration: \(-48\%\), displacement: \(-50\%\), and moment: \(-84\%\)) compared to that in the horizontal-ground model (acceleration: \(-27\%\), displacement: \(+650\%\), and moment: \(-77\%\)). Overall, batter piles showed better seismic performance in the inclined-ground model than in the horizontal-ground model.  
pile-supported structure; dynamic centrifuge model test; batter pile +
Footnote †: journal: Applied  
Article  
Dynamic Behavior of Pile-Supported Structures with Batter Piles according to the Ground Slope through Centrifuge Model Tests