esa-sceva/satcom-chunk-collection huggingface dataset:
# Modified Equation of Shear Strength with Respect to Saturation
## 3 Materials and Experimental System
### Shear Strength Test  
In order to validate Equation (5), a triaxial shear apparatus (Figure 6) was adopted to control the shear strength and determine the parameters of the proposed model. The silty clay was compacted in a triaxial saturator (diameter: 61.2 mm, height: 125 mm). After taking apart the triaxial saturator, the soil enclosed with a rubber membrane was put into the triaxial compression apparatus (Figure 7), then many triaxial compression tests with various degrees of saturation (30%, 40%, 50%, 60%, 70%, 80%, 90%) under different confining pressures including 50 kPa, 100 kPa, 150 kPa, and 200 kPa were conducted. The initial confining pressure for the triaxial shear test was often set at a low value such as 50 kPa to ensure that the sample was not damaged during the initial stages of the test. The reason why the effective confining pressure was set at a low value was that the soil sample had relatively low strength, and this sample can be adequately tested by lower confining pressures. Excessive pressure could cause damage to the sample, which could result in inaccurate results. In addition, the sample may experience excessive deformation, failure, or crushing at higher pressures, which could also affect the shear strength parameters. The mass of the soil sample and the mass of water need to be measured in advance to put them together so that the degree of saturation is determined. Degree of saturation = (mass of water added/(mass of solid + mass of water added)) \(\times\) 100%.  
Figure 5: Test system of the soil–water characteristic curve using the pressure plate apparatus.  
Figure 6: General scheme of the triaxial shear test system.  
Moreover, the shearing velocity was 0.2 mm/min. There are three types of triaxial shear test including a consolidated drained triaxial test (CD test), a consolidation undrained test (CU test), and an undrained and unconsolidated test (UU test). In this study, the consolidated drained triaxial test (CD test) was conducted on saturated soil, and the consolidation undrained test was conducted on unsaturated soil. In this research, the CU test was adopted as the experiment aimed for unsaturated soil.
## 5 Simulation and Analysis
### Anti-Attack Ability Brought by NVP
#### 5.1.1 Single-Step Attack  
The SRN model is formulated with the Stochastic Petri Net Package (SPNP) [68] whereby we obtained the probabilities of a node being compromised and working under single-step attacking. Table 7 compares the effectiveness of various redundant systems, where baseline values \(BL\)s describe the corresponding standard system without NVP. The relevant compromising probability of \(BL_{j}\) is calculated as the average \(\lambda(t_{ia})\) as  
\[\overline{p_{A}}_{j}=\frac{\sum\lambda(t_{ia})}{j}. \tag{33}\]  
For the same \(N\) value, when \(M\) approaches \(N\), a strict criterion is indicated. With \(M\) approaching \(N\), Table 7 exhibits both the rapid decrease in the probability of a single node being compromised and normally working. That is, while the probability of \"error\" is increasing, the attack success and system availability rates decrease. Although it is difficult for the attacker to tamper with the final result, the system function can be disabled by tampering with several outputs from online SCVs. Therefore, an extremely high output threshold is not recommended.  
Moreover, to compare the effectiveness of various \(N\), we observe the values where \(M=\lceil N/2\rceil\). We found that all compromising probabilities are lower than the corresponding \(BL\)s which demonstrates the effectiveness of NVP. However, compared with \(M\), the differences among different \(N\) were insignificant. The decrease in the probability of a node being compromised is related to the statistical properties of the original \(OE_{j}\) set. In other words, there exist a proportional relation between \(OE_{j}\) and this probability, in a way that the safer the \(OE_{j}\) is, the more significant safety gain will be brought by NVP. According to the values where \(M=N\) indicates the reachable minimum \(p_{E}\) for a certain \(N\) value, with the increase in \(N\), \(p_{E}\) value decreases and therefore makes the system's dependability higher.
## 4 Results and Discussion
### Dataset  
A total of 48 audio records were gathered from fourteen different indoor and outdoor environmental sounds for performing the statistical analysis and audio classification. These environmental records have been downloaded from Freesound website [32]. Table 1 shows the dataset characteristics. Each environmental sound was recorded using a 10 kHz sampling frequency and 8-bit resolution during 10 s. In order to gather different samples, the start point of each recording was randomly established.
## 2 Methodology
### A Novel Local Similarity Measure  
In this paper, the local similarity measure comprises the spatial distance factor and the intensity distance factor. For one pixel, the spatial constraint reflects the damping extent of the neighbors by the distance from the central pixel and is represented as  
\[w_{\text{d}}=\frac{1}{d(x_{i},x_{j})} \tag{6}\]  
here \(x_{i}\) is the central pixel in the neighborhood \(N_{i}\), and \(x_{j}\) represents the neighbor of \(x_{i}\). \(d(x_{i},x_{j})\) represents the Euclidean distance between pixel \(x_{i}\) and pixel \(x_{j}\). Seen from Equation (6), we can find \(w_{\text{d}}\) is determined by the distance between the centered pixel and its neighbors, as a result, more local context information can be exploited.  
But it is not enough to show the damping extent of the neighborhood by the distance alone. Here, the intensity distance is introduced to consider local grey level statistics. The intensity distance \(w_{\text{u}}(k)\) between two pixels is defined as  
\[w_{\text{u}}(k)=u_{k}(x_{i})\times u_{k}(x_{j}) \tag{7}\]From equation (7), we can find that the class similarity between two pixels is proportional to the \(w_{u}(k)\), a high value means that there is a high probability that the pixels \(x_{i}\) and \(x_{j}\) belong to the same class, and vice versa.  
Therefore, the similarity measure \(w_{ij}(k)\) between pixels \(x_{i}\) and \(x_{j}\) can be defined as  
\[w_{ij}(k)=w_{d}\times w_{u} \tag{8}\]  
Obviously, no experimental parameters are needed in the similarity measure, and the pixel is affected by its neighboring pixels and itself simultaneously, and the local similarity measure is also adaptive to classes and anisotropic in the neighborhood, which is useful for remaining boundary pixels and image details when performing spatial regularization.
## 3 Results and Discussion
### Mechanical Properties
#### 3.1.2 Elastic Modulus  
In this study, the slope of the stress-strain curve in the 45-55% section of the peak strength is taken as the elastic modulus. The elastic moduli of specimens at different loading-foliation angles are listed in Table 2. The results show that the elastic modulus of the slate specimen gradually decreases with the loading-foliation angle, exhibiting a strong anisotropy. This variation in the trend of the elastic modulus, following the transversely isotropic model, has also been observed in other transversely isotropic rocks, e.g., Asan gneiss, Boryeong shale and Yeoncheon schist [38]. This phenomenon can be explained as follows: since the stiffness of the foliation is much lower than that of the rock matrix, the deformation of the rock mainly depends on the deformation of the foliation; as the loading-foliation angle is gradually increased from 0\({}^{\circ}\) to 90\({}^{\circ}\), the stress acting on the foliation will increase, resulting in an increase in the deformation of the foliation, and the overall elastic modulus of the specimen shows a gradually decreasing trend.
# Detection of Forest Windstorm Damages with Multitemporal SAR Data--A Case Study: Finland
## 2 Material
### Multi-Source National Forest Inventory Data  
The raster form data from the Finnish multi-source national forest inventory (MS-NFI) were used as additional information in estimating the models to predict the windstorm damages, their severity and uncertainty [36; 37]. The data have been projected to correspond to the 31 July 2017 situation and cover all forest ownership groups [38].  
The following variables were used in the analyses: mean diameter of the trees, mean height of the trees, mean age of the trees and basal area of trees as well as the volume of the growing stock by tree species groups. The groups were Scots pine (_Pinus sylvestris_ L.), Norway spruce (_Pica abies_ Kast. L.), birch (_Betula_ spp) and other broad leaved trees, mainly aspen (_Populus tremula_ L.) and alder (_Alnus_ spp.). The first three variables were calculated from the tree-level field measurements as weighted averages, the weight being the basal area of trees. A similar method was used when calculating stand-level averages from pixel-level estimates or measurements. A variation of volume of the growing stock is shown in Figure 5 and a zoom from a sub-area in Figure 6.  
Figure 5: Volume of the growing stock on 31 July 2017 on the study area based on MS-NFI (EPS3067).
# Joint Balanced Routing and Energy Harvesting Strategy for Maximizing Network Lifetime in WSNs
## 6 Conclusions  
To joint consider the energy balanced routing and energy harvesting benefits, a Shortest Path Tree with Energy Balance Routing strategy (SPT-EBR) is proposed in the article. In SPT-EBR, two methods are presented, including the power consumption and the energy harvesting schemes to select the forwarding node according to the awareness factors of link weight. First, the packet forwarding rate factor was considered in the power consumption scheme to update the link weight for the sensors with higher power consumption and mitigate the traffic load of hotspot nodes to achieve the energy balance network. Then, the energy harvesting scheme combines both the packet forwarding rate and the power charging rate factors together to update the link weight with a new battery charging rate factor for the hotspots. Finally, simulation results validate that both power consumption and energy harvesting schemes in SPT-EBR achieve better energy balance performance and save more charging power than the conventional shortest path protocol and thus improve the overall network lifecycle. Additionally, it can also be inferred that the immortal WSNs has a high probability to be realized when the energy harvesting rate is greater than the power consumption rate especially for the hotspot nodes. As a result, the design aim of immortal WSNs can be achieved, which profits from the effective harvested energy and load balanced routing strategy.  
[PERSON] designed the joint balanced routing and energy harvesting strategy with power consumption and energy harvesting schemes including the corresponding algorithms. M.T. helps to investigate  
Figure 12: The routing computation cost of SPT-EBR and Dijkstra algorithms.  
the related work and validates the simulation performances. [PERSON] and [PERSON] performed the simulation and analyzed the results.  
This research was funded by the College of Artificial Intelligence, Yango University, China.  
The authors declare no conflict of interest.
# Impact of the Elevation Angle on CYGNSS GNSS-R
## 2 GNSS-R Approaches for Studies over Land Surfaces  
The use of different GNSS-R methods (iGNSS-R, cGNSS-R, and GPS-IR) for land-surface applications requires further research activities because the dielectric properties of this scattering medium make it more complex than the ocean surface. The first study (to the authors' knowledge) on the potential use of GNSS-R for SMC estimation was published in 2000 [26]. Follow-on activities were proposed simultaneously to investigate the capabilities of GNSS-R for SMC determination using cGNSS-R [27, 28, 29] and GPS-IR [10, 11, 30, 31]. The use of the polarimetric ratio (ratio of the reflected signals' power at two different polarizations) on cGNSS-R data collected from two airborne experimentswas further studied for biomass monitoring up to an above ground biomass (AGB) of \(\approx\)300 ton/ha, and for SMC determination because it can cancel out surface roughness effects [15; 16]. A GNSS dual-polarization payload [4] was successfully tested over boreal forests during two experiments from a stratospheric balloon [32]. A comprehensive study over different land surface types was performed to further assess the use of dual-polarization information obtained during two airborne campaigns [33]. More recently, new important conclusions were also derived, such as: (a) the use of the GPS-IR for accurate SMC estimation was validated over multiple GPS test sites including vegetated surfaces [11; 34]; (b) a sensitivity to SMC of \(\approx\)38 dB/(m\({}^{3}\)/m\({}^{3}\)) was measured over nearly bare-soil target areas using data from U.K. TDS-1 [35; 36]; (c) a Pearson correlation coefficient of r \(\approx-0.6\) between one-year averaged polarimetric ratio from a GNSS-R experiment on-board the Soil Moisture Active Passive (SMAP) mission and SMC was measured over the complete Earth's surface [16; 37]; (d) \(\theta_{\rm e}\) should be considered for the application of the so-called Tau-Omega model in the GNSS-R case [35]; (e) the vegetation introduces short-term (volume scattering) and long-term (interference from large-size scatterers and canopy inhomogeneity) fluctuations on GNSS signals [38]; and (f) the unbiased root-mean-square difference between daily averaged CyGNSS-derived SMC and SMAP SMC is down to 0.045 cm\({}^{3}\)/cm\({}^{3}\), after correcting for surface roughness effects [17].
## 3 Experimental Results and Analysis
### Analysis of Compressive Peak Strain in Prismatic Specimens
The fitting expressions between peak stress and peak strain obtained from the experiment are also listed in Table 5.  
Applying the aforementioned formulae to calculate the actual values and comparing them with the measured values, the results are depicted in Figure 4. Analysis of Figure 4 reveals that the calculation results obtained from the first three formulae generally underestimate the experimental values and fail to align with the observed variation trend. However, the calculated values derived from the expression proposed in Equation (5) exhibit a strong agreement with the measured values, yielding a correlation coefficient of R\({}^{2}\) = 0.95. This indicates that the fitting formula proposed in this study accurately reflects the relationship between the peak strain and compressive strength of steel slag micro-powder UHPC mixed with steel fibre.
## 1 Introduction
The main objective of this study is to detect, using images from the Sentinel-3B OLCI satellite, the behavior patterns of chlorophyll-a (CHL_NN) in units of m\({}^{-3}\), water turbidity (TSM_NN) in units of g m\({}^{-3}\), and suspended pollution potential (ADG_443_NN) in units of 443 m\({}^{-1}\), in addition to quantifying the dangerous chemical elements present in NPs and ultrafine particles deposited in the sediments of Cartagena Bay (Colombia), in the period comprising the summer and winter seasons of 2020 and 2021. This study is expected to contribute to information that enables and/or complements the creation of future public policies aimed at impacts on aquatic environments in Colombia and other countries. Additionally, by collaborating with the use of new technologies made available by the ESA for assessment, aimed at assessing environmental quality through the detection of satellite information, they contribute to the creation of actions to preserve aquatic ecosystems with a greater degree of sustainability at a global level.
## 4 Conclusions and Outlook  
### Conclusions  
This study proposed an enhanced LKN-UKAN model and applied FTTL to it to address the deficiencies of existing segmentation networks in remote sensing landslide image segmentation tasks. The key findings and contributions are summarized as follows. (1) Tok-KAN module: We introduced the tokenized KAN block (Tok-KAN) to enhance the network's ability to model complex nonlinear features, addressing the limitations of U-Net++ in capturing intricate landslide details. (2) DLFFSKA module: We designed the DLFFSKA module to improve the network's global perception and multi-scale feature fusion capabilities, significantly enhancing edge delineation and segmentation accuracy in complex areas. (3) FTTL strategy: By applying FTTL, we developed an efficient and precise target model tailored for landslide image segmentation. The experimental results demonstrated that the LKN-UKAN achieved notable improvements over U-Net++, with the IoU, precision, and F1 score increasing by 12.22%, 5.4%, and 8.36%, respectively.
## 3 Results and Discussion
### Degradation of C-V Response  
Slight degradations in the C-V response for the cold white LED strip was observed at mAs values of 125, 150, and 250. There was also warm white LED strip signal decrease during the 85, 100, and 125 mAs exposures. The red LED strip signal also decreased when the mAs values were 20, 25, 50, 85, 100, 125, 150, 200, and 250. However, minimal signal depletions were observed during the 200 and 250 mAs exposure settings for both the green and blue LED strips as observed in Figure 8. The cold white LED strip's signal stability was observed at three mAs values: 50, 85, and 100. Similarly, the green LED strip signal was steady during the 85 and 125 mAs exposure settings. The warm white LED strip signal stability was only during the 250 mAs setting. Contrarily, the red and blue LED strip signals were unstable for all the tested mAs settings. Therefore, overall, the cold white LED strip had the highest number of steady and smallest number of degraded signals at different mAs exposure settings. Nevertheless, the red LED strip had the least stable and most degraded signals. Therefore, the cold white LED strip is preferred since output signal stability is an essential factor for a prospective radiation detector [5].  
Figure 7: Stability of three C–V signal measurements of (**a**) cold white, (**b**) warm white, (**c**) red, (**d**) green, and (**e**) blue LED strips during the mAs parameter variation.  
Bombardments during Compton scattering and photoelectric effects could displace charge (electrons) from their original lattice structural locations [26; 54]. This results in the absence of electrons in their normal lattice positions (vacancies) and electrons occupying lattice positions where they are  
Figure 8: C–V signal against mAs graphs of (**a**) cold white, (**b**) warm white, (**c**) red, (**d**) green, and (**e**) blue LED strips showing any possible C–V response deteriorations as a result of radiation exposure.  
not meant to be (interstitials) [54]. When an x-ray photon deposits its energy to a vacancy or an interstitial, low/no charge excitation will occur thus low/no C-V signals. Therefore, LED strip C-V signal decrease was perhaps a result of these radiation-induced displacement effects. Post-irradiation signal degradation was also observed in other studies [10; 30]. In these studies, LEDs and photodiodes were used. A possible solution to output degradation would be annealing the LEDs to \"flush out\" the vacancies and interstitials. During the annealing process, both the vacancies and interstitials recombine as suggested by [PERSON] [34].
## 4 Use Case: Smart Buildings
### Implementation
#### 4.2.1 Rina Network Implementation  
The RINA network comprises five types of nodes (IoT devices, RINA-based IoT Gateway, EdgeNode, Core, and SDN controller), as Figure 11 shows. The DAF management was implemented using the aforementioned nodes configured with the latest version of IRATI open-source software v2.3.0 ([[https://github.com/IRATI/stack](https://github.com/IRATI/stack)]([https://github.com/IRATI/stack](https://github.com/IRATI/stack))) (accessed on 10 May 2023) [31]. IRATI enables a fast prototyping of Linux-based RINA nodes.  
The IoT devices are RINA sensors that are in charge of collecting temperature and humidity data. These sensors have been implemented using the ESP32 IoT platform and the DHT22 sensor. Furthermore, the RINAsense open-source software was employed to develop the temperature and humidity application. The RINAsense ([[https://github.com/Fundacio-i2](https://github.com/Fundacio-i2) CAT/rinasense]([https://github.com/Fundacio-i2](https://github.com/Fundacio-i2) CAT/rinasense)) (accessed on 15 June 2023) is based on the FreeRTOS real-time embedded Operative System (OS) that allows a rapid prototyping of RINA sensors for RINA networks [36].  
The RINA-based IoT gateway was implemented using the IRATI open-source software [31], and using as hardware a Raspberry Pi 4 with Debian Buster OS, Quad-core Cortex-A72 (ARM v8) 64-bit SoC @1.8 GHz, and 4 GB SDRAM. IRATI has been developed with a GNU/Linux based OS, and this software can be deployed on a Raspberry Pi.  
Figure 11: RINA architecture for the use case.  
The EdgeNode and the Core were implemented using two Intel(r) NUC9 VXQNX with Intel(r) Xeon(r) E-2286M 2.40 GHz with 32 GB of RAM, running Ubuntu 22.04. Furthermore, the Docker platform was employed in the EdgeNode to ease the deployment of applications in Docker containers. The applications deployed in Docker were the InfluxDB, Mosquitto MQTT Broker, Graphana dashboard, Python-based ML application, and Apache Kafka stream analytics. In addition, a RINA application was deployed on the edge node responsible for collecting data from RINA sensors (temperature and humidity) and ingesting them into the InfluxDB. It is relevant to highlight that only this RINA application used the RINA APIs to write into RINA flows. The other applications did not use RINA APIs because they are based on TCP/IP stack. However, these applications would communicate with their peers in the core using the L2 VPN to enable the progressive adoption of RINA in current TCP/IP networks.  
The SDN controller was implemented using as hardware an Intel(r) NUC 11 Performance Core i5-1135G7 4.2 GHzwith Ubuntu 20.04 LTS OS. Furthermore, it required the IRATI open-source software for deploying the RINA manager and the UoWM SDN Controller software. More details about the SDN controller will be detailed in Section 4.2.2.
## 7 Methodology and Experimentation
### Discussion  
We could extract critical results and insights from the experiments for future work. First, the proposed reliability score, which gives a scheduler awareness of node mobility, improves the job completeness metric with regard to human-designed heuristics, not considering such aspect in all run experiments. Moreover, in all experiments, it can be observed that human-crafted policies performed better with random waypoint than with random walk. This result agrees with what the authors in Ref. [53] state: random walk is the simplest model, and humans rarely move randomly.  
We also showed that RL is viable when the RL agent is adapted to connection and disconnection events. With sufficient training time, RL can devise a solution and even surpass human-crafted policies in terms of job completion rate. Regarding the overfitting problem when the agent needs to be applied in different environments, we suggest training the model with a set of varying environment events data to reduce the probability of overfitting and using the base model as a starting point to specialize one agent version to a specific environment.