esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Test Results
### Flexural Test Results  
Figure 3 shows the post-cracking resistance of the OPC for different waterproofing materials attached under concrete. The specimen (OPC-PE/) reinforced with polyethylene film indicates a sharp drop of applied load during the transitional period and represents the lowest residual load against the post-crack propagation. The specimen with OPC-Bituthene 3000 shows an almost similar tendency to the OPC-PE behavior. The specimens reinforced with Typar SF 65 and Preprufe delay crack propagation after the first crack initiation and show the highest residual load during crack development. The specimens reinforced with the addition of steel fiber (20 kg/m\({}^{3}\)) as well as waterproofing reinforcement indicate an apparent improvement of post-cracking behaviors as shown in Figure 4. The addition of hooked steel fiber in the concrete significantly delayed crack propagation at the load transition zone and safely transferred the applied load to the waterproofing materials at which point they continuously resisted crack development. Among the types of waterproofing reinforcement materials, the specimen with SF 20-Typar SF 65 showed the best performance during post-cracking behaviors.  
Figures 5 and 6 show the post-cracking behaviors of concrete reinforced with micro-fibers. The addition of micro-fiber with a dosage of 1.0 kg/m\({}^{3}\), as shown in Figure 5, indicates relatively lower residual loads than that of SF 20 and quite a similar tendency to that in Figure 3. The specimens reinforced with Typar SF 65 represented the highest residual loads during crack propagation. The load-displacement curves demonstrate some interesting results that with the help of both micro-fibers (2.8 kg/m\({}^{3}\)) and waterproofing reinforcement significantly improved the post-cracking behavior while minimizing load reduction in the transition period for all types of waterproofing materials. Preprufe indicates an almost close post-cracking behavior compared to the Typar SF 65. It can be summarized that different types of waterproofing materials with a combination of fiber types and fiber volume fractions help the apparent improvement of load dissipation after the first crack and the maintenance of relatively high residual loads against crack development.  
Figure 3: Load-displacement curve during crack propagation (OPC).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multirow{2}{*}{**OPC**} & \multirow{2}{*}{**Steel Fiber**} & \multirow{2}{*}{**Micro Fiber**} & \multirow{2}{*}{**Micro Fiber**} \\  & & & **20 kg/m\({}^{3}\)** & & **1.0 kg/m\({}^{3}\)** & **2.8 kg/m\({}^{3}\)** \\ \hline \multirow{2}{*}{Compressive strength (MPa)} & 1 & 46.16 & 47.29 & 41.24 & 41.74 \\  & 2 & 45.09 & 45.15 & 42.31 & 42.38 \\ \multirow{2}{*}{Ave.} & 3 & 43.70 & 43.83 & 39.98 & 42.31 \\  & & 44.98 & 45.42 & 41.18 & 42.14 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The results of compressive strength test.
# An Improved Multi-Scale Feature Fusion for Skin Lesion Segmentation
## 3 Proposed Methods  
The UNet network is a widely adopted and efficient network architecture. Its design is relatively simple, making it easy to train and adjust. By using an encoder-decoder structure and skip connections, the UNet network demonstrates strong feature extraction capabilities, multi-scale information-processing abilities, and trainability in image segmentation tasks. In this section, the proposed model based on UNet3+ is presented for dermoscopy image segmentation. Whether based on simple connections or dense connections, UNet and UNet++ cannot fully extract full-scale feature information. In contrast, UNet3+ has redesigned the dense connection method between the encoder and decoder, allowing each decoder to cross all scales to fuse small- and same-scale feature maps from the encoder, and also extract information via the large feature maps generated by the current decoder. This improvement not only makes the model more concise, but also more effectively captures complete semantic information. Therefore, UNet3+ is a promising model to push the development of medical image segmentation in the future.
# Path Planning of Mobile Robots with an Improved Grey Wolf Optimizer and Dynamic Window Approach
## 6 Experiments Simulation and Analysis
### Experimental Content of Path Planning  
To evaluate the effectiveness of the PAGWO-IDWA algorithm, the following three sets of experiments are designed in this paper:  
* Global path planning experiments: the GA, the SCA, the PSO, and the GWO algorithm are used as references in three raster maps of varying complexity to verify the performance of the PAGWO algorithm in global path planning.
* PAGWO-IDWA and DWA Comparison Experiment: in the DWA, two enhancements are based on PAGWO, so the improved version can essentially be regarded as PAGWO-IDWA. To evaluate the performance of the improved algorithm relative to the original DWA, we conducted two sub-experiments. The first validates that using PAGWO-generated waypoints significantly enhances DWA's global path planning capability, while the second demonstrates that employing PAGWO-generated waypoints as orientation angles improves its path efficiency. The dynamic obstacle avoidance performance of DWA will be validated in a subsequent experiment;
* Dynamic obstacle avoidance experiment: dynamic obstacles are added to the map to assess the PAGWO-IDWA algorithm's obstacle avoidance performance in dynamic environments and evaluate its safety and generalizability. Figure 5 shows the three raster maps used for testing in this paper. The environment complexity increases with the number of obstacles. Figure 5 shows a simple 20 m \(\times\) 20 m map, Figure 5 shows a moderately complex 30 m \(\times\) 30 m map, and Figure 5 shows a highly complex map of 30 m \(\times\) 30 m.  
The experimental hardware consisted of a laptop with an AMD R9-7940H processor at 4 GHz (AMD, Santa Clara, CA, USA), 16 GB of RAM, running Windows 11 64-bit with MATLAB R2021b software.  
Figure 5: Simulation map. (**a**) Simulation map 1; (**b**) simulation map 2; (**c**) simulation map 3.
## 2 Methods and Materials
### Characteristics of the Tested Organic Soils  
#### 2.3.1 Antoniny Site  
The Antoniny test site is located in north-western Poland in the Notec River valley, where the Department of Geotechnical Engineering of the Warsaw University of Life Sciences in cooperation with the Swedish Geotechnical Institute performed extensive field and laboratory investigations in the 1980s [36; 37]. Two test embankments (with and without vertical drains) were constructed in three stages between 1983 and 1987. The fill without vertical drains was then brought to failure by successively increasing the height of the fill. The field investigations included dilatometer tests and field vane tests (FVT).  
At this site, the virgin organic subsoil, 7.8 m thick, consists of a 3.1 m thick peat layer and a 4.7 m gyttja layer. Based on the index properties, the peat layer was sub-divided into two layers: the first one being pseudo-fibrous peat, occurring from the surface to the depth of 1.0 m, and the second one being amorphous peat, occurring below 1.0 m to the depth of 3.1 m. The gyttja layer was sub-divided into three layers, with the first being calcareous-organic gyttja occurring from the depth of 3.1 m to 4.5 m and the second and third layer being calcareous gyttja occurring from the depth of 4.5 m to 6.8 m and below the depth of 6.8 m, respectively. The organic subsoil is underlined by a sand layer. Organic soils are overconsolidated with an overconsolidation ratio, \(OCR\), decreasing from 5 to 2 with depth. The results of the index properties of organic soils are summarised in Table 2. Soil profiles of organic subsoil at the Antoniny site for overconsolidated subsoil (\(oc\)) and normally consolidated subsoil (\(nc\)) are shown in Figure 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Properties**} & \multicolumn{2}{c}{**Peat**} & \multicolumn{2}{c}{**Gyttja**} \\ \cline{2-6}  & **(1)** & **(2)** & **Calcareous-Organic (1)** & **Calcareous (2)** & **Calcareous (3)** \\ \hline Thickness (m) & 0–1.0 & 1.0–3.1 & 3.1–4.5 & 4.5–6.8 & 6.8–7.8 \\ Water content \(w_{n}\) (\%) & 420–450 & 310–340 & 130–140 & 105–110 & 110–115 \\ Bulk density \(\rho\) (t/m\({}^{3}\)) & 1.05–1.1 & 1.05–1.1 & 1.25–1.30 & 1.35–1.40 & 1.40–1.45 \\ Specific density \(\rho_{s}\) (t/m\({}^{3}\)) & 1.4 & 1.45 & 2.2 & 2.3 & 2.4 \\ Liquid limit \(w_{1}\) (\%) & 450 & 305–310 & 100–110 & 80–90 & 90–100 \\ Organic content \(l_{m}\) (\%) & 80–85 & 65–75 & 15–20 & 8–10 & 5–7 \\ CaCO\({}_{3}\) content (\%) & 5–10 & 10–15 & 65–75 & 80–85 & 85–90 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Index properties of organic soils at the Antoniny site.  
Figure 1: Profiles of the corrected readings \(p_{o}\) and \(p_{1}\) and index parameters \(I_{D}\), \(K_{D}\), and \(E_{D}\) from dilatometer tests obtained for organic subsoil at the Antoniny site; (\(oc\)): overconsolidated subsoil, (\(nc\)): normally consolidated subsoil.
# Enhancement of Sentinel-2A Images for Ship Detection via Real-ESRGAN Model
## 4 Conclusions  
This study successfully enhanced Sentinel-2A images for ship detection. The proposed system architecture consists of four stages: pre-processing, training the super-resolution model, training the object detection model, and calculating performance metrics. The Real-ESRGAN model was utilized for super-resolution with PlanetScope images. MSE, PSNR, and SSIM values achieved 80.63, 29.67 dB, and 0.84 on the Sentinel-2A images. This enhanced model was then applied to improve the Sentinel-2A images. Ship detection was conducted through two distinct processes to demonstrate the effectiveness of the super-resolution practices using Real-ESRGAN for ship detection. Separate detection procedures were performed using YOLOv10 on both the Sentinel-2A images and enhanced Sentinel-2A images. The results indicated that mAP50 and mAP50-95 were 87.5% and 68.5%, respectively. Both metrics showed an increase of 2.6% compared to the initial situation.  
Overall, the findings reveal that the super-resolution technique significantly enhances ship detection from Sentinel-2A satellite images with medium spatial resolution. These results are precious for studies focusing on ship detection, including ship carbon emissions and ship tracking.  
Conceptualization, H.D. and C.F.A.; methodology, H.D., C.F.A. and K.A.; software, K.A.; validation, H.D. and K.A.; data curation, K.A. and C.F.A.; writing--original draft preparation, C.F.A. and H.D.; writing--review and editing, H.D., K.A. and C.F.A.; visualization, K.A. and C.F.A.; supervision, H.D.; project administration, H.D. All authors have read and agreed to the published version of the manuscript.  
Centre Fazilet Aldogan acknowledges the Council of Higher Education (CoHE) 100/2000 scholarship.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The research presented in this article is part of the first author's (Centre Fazilet Aldogan) PhD thesis study at Geographical Information Technologies (GIT), Applied Informatics Department, Informatics Institute, Istanbul Technical University (ITU).  
The authors declare no conflicts of interest.
## 4 Comparison of Systems Discussed  
Several scientific and industrial research groups have proposed indoor positioning systems using different technologies, covering many types of sensors and models and logical algorithms with quite different characteristics. This advance in knowledge and mastery of physical and logical resources is essential for the proposed location and navigation services to be compatible with the users' needs.  
One of the main objectives of using these resources is to allow human activities to be mapped, offering products and services that increase the quality of life for users [24; 61]. The visually impaired make up one of the groups of people who wait for indoor positioning solutions, especially those that offer safe navigation guides. Indeed, such systems would be a great impact on the quality of their lives due to the better relationship with the environments and the independence from other people's help [82].  
In this section, we present the demands identified by a group of authors and their proposals of navigation systems. A comparison of the studies is made to facilitate the understanding of the benefits and limitations of each approach.  
When carrying out an evaluation, it is essential to observe the information provided by the authors themselves with some reservations, as their results generally do not follow independent evaluations [26]. One of the several reasons for the divergence between the values reported by the authors and those obtained in an attempt to replicate experiments is that the authors established conditions aimed at their investigation (ideal conditions), allowing to achieve the greatest possible precision, which is not realistic.
## 2 Materials and Methods  
### Plant Material and Treatment  
_R. chrysanthum_ belongs to the rhododendron family and grows in the highlands of Changbai Mountain (\(41^{\circ}59^{\prime}36^{\prime\prime}\) N \(128^{\circ}04^{\prime}39^{\prime\prime}\) E). After transferring _R. chrysanthum_ from its origin (Changbai Mountain) to the laboratory, explants were obtained by culturing stem segments of _R. chrysanthum_ in 1/4 MS medium. These explants cultured in 1/4 MS medium were placed in the artificial climate chamber (18 \({}^{\circ}\)C (14 h light)/16 \({}^{\circ}\)C (10 h dark))under the 50 umol (photon) m\({}^{-2}\) s\({}^{-1}\) white light, with a relative humidity of 60%, and in the intelligent artificial incubator (25 \({}^{\circ}\)C (14 h light)/18 \({}^{\circ}\)C (10 h dark)), with a relative humidity of 60% under the white light. Seedlings grown in the intelligent artificial incubator were termed incubator seedlings (Figure 1A), and seedlings grown in the artificial climate chamber were termed domesticated plants (Figure 1B), with domestication conditions applied in reference to the previous experiments [18; 27].  
For PAR+UV-B treatment, 320 or 295 nm long-pass filters ([PERSON], Filter Long 2 IN SQ, Barrington, NJ, USA) were placed on the culture flask, respectively. For PAR treatment, the 400 nm long pass filter was placed on the culture flask. Corresponding to the transmittance function of the long pass filter, the actual UV-B and PAR irradiance of the sample were 2.3 W m\({}^{-2}\) UV-B and PAR of 50 umol (photon) m\({}^{-2}\) s\({}^{-1}\). The artificial UV-B and PAR used in this study were derived from UV-B fluorescent tubes (Philips, Ultraviolet-B TL 20 W/01 RS, Amsterdam, The Netherlands) and white-fluorescent lamps (Philips, T5 \(\times\) 14 W, Amsterdam, The Netherlands). Different long pass filters were used to obtain two modes of target radiation.  
The two forms of seedlings were subjected to PAR treatment or PAR+UV-B treatment for 8 h a day. After two days of stress treatment, two forms of tested materials with similar growth were selected, and the leaves with intact edges, no damage, and no macula were placed in liquid nitrogen. For RNA-seq analysis of leaf samples preserved in liquid nitrogen, a mixed sampling strategy was used to eliminate inter-individual variation and the experiment was repeated three times. For GC-TOFMS analysis of leaf samples, the experiment was repeated six times. All plants were completely randomized for RNA-seq and GC-TOFMS analysis.  
Control group M (A): PAR treatment for incubator seedlings cultured in intelligent artificial incubator. Experimental group N (B): additional UV-B radiation treatment for incubator seedlings cultured in intelligent artificial incubator. Experimental group Q (C): same additional UV-B radiation treatment for domesticated plants cultured in the artificial climate chamber. Both PAR and PAR+UV-B treatments lasted for two days, with 8 h of treatment per day (Figure 2).  
Figure 1: (**A**) The _R. chrysanthum_ in the intelligent artificial incubator. (**B**) The _R. chrysanthum_ in the artificial climate chamber.
###### Abstract  
Accurately quantifying gross primary production (GPP) globally is critical for assessing plant productivity, carbon balance, and carbon-climate feedbacks, while current GPP estimates exhibit substantial uncertainty. Solar-induced chlorophyll fluorescence (SIF) observed by the Orbiting Carbon Observatory-2 (OCO-2) has offered unprecedented opportunities for monitoring land photosynthesis, while its sparse coverage remains a bottleneck for mapping finer-resolution GPP globally. Here, we used the global, OCO-2-based SIF product (GOSIF) and linear relationships between SIF and GPP to map GPP globally at a 0.05\({}^{\circ}\) spatial resolution and 8-day time step for the period from 2000 to 2017. To account for the uncertainty of GPP estimates resulting from the SIF-GPP relationship, we used a total of eight SIF-GPP relationships with different forms (universal and biome-specific, with and without intercept) at both site and grid cell levels to estimate GPP. Our results showed that all of the eight SIF-GPP relationships performed well in estimating GPP globally. The ensemble mean 8-day GPP was generally highly correlated with flux tower GPP for 91 eddy covariance flux sites across the globe (R\({}^{2}\) = 0.74, Root Mean Square Error = 1.92 g C m\({}^{-2}\) d\({}^{-1}\)). Our fine-resolution GPP estimates showed reasonable spatial and seasonal variations across the globe and fully captured both seasonal cycles and spatial patterns present in our coarse-resolution (1\({}^{\circ}\)) GPP estimates based on coarse-resolution SIF data directly aggregated from discrete OCO-2 soundings. SIF-GPP relationships with different forms could lead to significant differences in annual GPP particularly in the tropics. Our ensemble global annual GPP estimate (135.5 \(\pm\) 8.8 Pg C yr\({}^{-1}\)) is between the median estimate of non-process based methods and the median estimate of process-based models. Our GPP estimates showed interannual variability in many regions and exhibited increasing trends in many parts of the globe particularly in the Northern Hemisphere. With the availability of high-quality, gridded SIF observations from space (e.g., TROPOMI, FLEX), our novel approach does not rely on any other input data (e.g., climate data, soil properties) and therefore can map GPP solely based on satellite SIF observations and potentially lead to more accurate GPP estimates at regional to global scales. The use of a universal SIF-GPP relationship versus biome-specific relationships can also avoid the uncertainty associated with land cover maps. Our novel, independent GPP product (GOSIF GPP), freely available at our data repository, will be valuable for studying photosynthesis, carbon cycle, agricultural production, and ecosystem responses to climate change and disturbances, informing ecosystem management, and benchmarking terrestrial biosphere and Earth system models.  
sun-induced chlorophyll fluorescence; gross primary productivity; Orbiting Carbon Observatory-2; FLUXNET; climate change; carbon fluxes; carbon cycle; model benchmarking; ecosystem models; Earth system models 20191115263; doi:10.3390/rs11212563
###### Abstract  
The rapid momentum of deep neural networks (DNNs) in recent years has yielded state-of-the-art performance in various machine-learning tasks using speaker identification systems. Speaker identification is based on the speech signals and the features that can be extracted from them. In this article, we proposed a speaker identification system using the developed DNNs models. The system is based on the acoustic and prosodic features of the speech signal, such as pitch frequency (vocal cords vibration rate), energy (loudness of speech), their derivations, and any additional acoustic and prosodic features. Additionally, the article investigates the existing recurrent neural networks (RNNs) models and adapts them to design a speaker identification system using the public YOHO LDC dataset. The average accuracy of the system was 91.93% in the best experiment for speaker identification. Furthermore, this paper helps uncover reasons for analyzing speakers and tokens yielding major errors to increase the system's robustness regarding feature selection and system tune-up.  
classification; DNNs; English; speaker identification; speech; YOHO speech dataset +
Footnote †: journal: Medical  
feature, in addition to acoustic features such as energy; (2) we evaluate the proposed system using the YOHO speech dataset developed by the Linguistic Data Consortium (LDC) [5]; (3) we enhance the audio signal by pre-processing the speech files, segmenting the utterance into frames, and computing the fundamental frequency F0; (4) we make calculations to determine delta pitch (\(\Delta\)F0) and delta energy (\(\Delta\)E) that could help clarify and identify speaker identity by studying the change in vocal cord vibration of each speaker; (5) we state our outcomes, observations, and results about the system and the used model for comparison with previous studies.  
The rest of the manuscript is structured as follows: in Section 2, we provide a literature review of the speaker recognition process, including identification and verification tasks, along with relevant citations to prior works; in Section 3, the chosen dataset is presented; in Section 4, we show the methodology of the proposed system in detail; in Section 5, we evaluate the proposed system using different models; in Section 6, we present and discuss the results; finally, in Section 7, we provide guidelines for future research.
# Evaluation of Multi-Sensor Fusion Methods for Ultrasonic Indoor Positioning
## 3 Proposed Fusion Methods
### Loosely Coupled Approach
#### 3.1.2 Adaptive Kalman Filter Approach  
The adaptive Kalman filter (AKF) approach is based on the linear Kalman filter with a dynamic noise matrix \(\mathbf{Q_{k}}\) to improve the predictions at the instant \(k\)[32]. All the initial values are kept as in the LKF case, except the noise matrix that becomes \(\mathbf{Q_{0}}\) initially, \(N\) is a positive constant, in this case equal to 10, and \(\Delta\mathbf{Q}\) is the noise error covariance. The final \(\mathbf{Q_{k}}\) is computed using Equation (8).  
\[\mathbf{Q_{k}}=|diag(\alpha\mathbf{\cdot}\mathbf{Q_{k-1}}+\Delta\mathbf{Q_{k} })| \tag{8}\]  
where \(\alpha=\frac{N-1}{N}\), and \(\mathbf{w_{k}}=\alpha\mathbf{\cdot}\mathbf{w_{k-1}}+\left(\frac{1}{N}\right)\mathbf{ \cdot}\mathbf{w_{k}}\)  
\[\mathbf{Q}_{0}=\left(\begin{array}{ccc}\frac{\sigma_{x,i}^{2}}{10}&0.05\; \sigma_{y,i}^{2}&0.05\;\sigma_{z,i}^{2}\\ 0.05\;\sigma_{x,i}^{2}&\frac{\sigma_{y,i}^{2}}{10}&0.05\;\sigma_{z,i}^{2}\\ 0.05\;\sigma_{x,i}^{2}&0.05\;\sigma_{y,i}^{2}&\frac{\sigma_{z,i}^{2}}{10} \end{array}\right) \tag{9}\]