esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Spatial Distribution Characteristics of Groundwater Chemistry  
#### 4.2.1 Hydrogeochemical Facies  
A Piper diagram can help to evaluate the geochemical relationships between different dissolved ions and dominant types of water chemistry in groundwater [38]. The TDS of shallow groundwater was 738.4-2532.6 mg/L [39]. The shallow groundwater was highly variable with HCO3-, SO4-, Cl-, NO3- as the main anions and Na+, Ca2+, Mg2+ as the main cations. The NO3- concentration in the water samples was high and exceeded the quality standard for domestic drinking water, probably due to the inappropriate use of fertilizers making NO3- enter the groundwater with rainfall and irrigation [40]. The HCO3-Cl-Mg-Ca type, SO4-HCO3-Na type and HCO3-Mg-Ca-Na type characterized the hydrogeochemical composition of shallow groundwater (Figure 3). The groundwater below the chromium slag pile (CSP) was SO4-HCO3-Na type. The reason was related to the production process of chromium salt, and the specific chemical reaction was 2 Na2 CrO4+H2 SO4=Na2 Cr2O7+Na2 SO4+H2O, so SO4- will enter the groundwater under the leaching effect of chromium slag. The downstream was basically uncontaminated, and the groundwater was HCO3-Mg-Ca-Na type. The groundwater of middle reaches and east of the chromium slag piles was HCO3-Cl-Mg-Ca type.  
The TDS of deep groundwater in the site was 504-1198.26 mg/L. The anions were mainly HCO3-, SO4-, Cl-, NO3-, and the cations were mainly Na+, Ca2+, Mg2+. The NO3- concentration in the deep groundwater was lower than that in the shallow groundwater, but still exceeded the quality standard of domestic drinking water. It may be that the shallow groundwater overflowed into the deep groundwater, resulting in abnormal NO3- concentration in the deep groundwater. The deep hydrogeochemical composition was simpler than the shallow ones, and the groundwater was mainly HCO3-Cl-Mg-Ca type, HCO3-Na-Mg type and HCO3-SO4-Mg-Na-Ca type.  
Figure 3: Piper diagram for shallow and deep groundwater. CSP represents chromium slag pile.
# A Comparative Study of the Pullout Strength of Geostraps and Geogrids in Reinforced Soil
## 2 Materials and Methods
### Design Tensile Strength of Geostraps  
The geostraps were made of high-tenacity polyester yarn encased in a polymer sheath. Different types or grades of geostraps are available globally based on different tensile strengths. These were simulated, and their properties are tabulated in Table 2. Geostraps of different grades will impart different tensile strengths to the soil subgrade, which further impacts their pullout resistance.  
According to Geoguide 6 [35], the design tensile strength, \(T_{D}\), of a geostrap is expressed as  
\[T_{D}=\frac{T_{ult}}{\gamma_{m}\gamma_{n}} \tag{2}\]  
where  
\(T_{ult}\) = the characteristic short-term tensile strength;  
\(\gamma_{m}\) = the partial material factor on the tensile strength of a geostrap;  
\(\gamma_{n}\) = the partial consequence factor to account for failure.  
The partial material factor (\(\gamma_{m}\)) was considered for the environmental effects on material durability, construction damage, creep, hydrolysis, and stress rupture for a design life of 120 years at 30 degrees Celsius. The value of \(\gamma_{m}\) varies from 2.36 to 2.61 for different grades of geostraps. The partial consequence factor (\(\gamma_{n}\)) accounts for the potential negative impact of various factors such as installation, damage, creep, weathering, and degradation due to chemical or biological reaction. \(\gamma_{n}\) varies from 1.0 to 1.1 for different consequence factors.  
Assumptions:  
The following assumptions were made in the present study:  
* The friction between the front end of the geostrap and the connecting edge of the pullout box is negligible.
* The parameters obtained from the lab are independent of the scale effect.
* The effective area method was used to determine the pulling force, and, accordingly, the displacement was measured at the front of the geostrap.  
The first assumption was made as proper greasing between the front end of the geostrap and the connecting edge of the pullout box assembly was performed. The second assumption implies that the characterisation and properties of the geostrap and soil used for the lab and field testing were the same. The third assumption suggests that out of the three ways for evaluating the average pullout resistance, total area, effective area, and maximum slope area, the effective area method was used as it related more to the actual test conditions [15].  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Geostrap Grade** & **Mass per Unit Length (g/m)** & **Characteristic Short-Term Tensile Strength (kN/m)** & **Partial Material Factor \(\gamma_{m}\)** & **Partial Consequence Factor \(\gamma_{m}\)** \\ \hline M25 & 103 & 25 & 2.50 & 1.0 \\ M37.5 & 111 & 37.5 & 2.43 & 1.0 \\ M50 & 138 & 50 & 2.38 & 1.0 \\ M65 & 179 & 65 & 2.36 & 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Different grades of geostraps along with their properties.
# Measurement of Head-Related Transfer Functions: A Review
## 2 Principle of HRTF Measurements
### Golay Codes
#### 2.2.2 HRTF Measurement with Sweep Signals
The group delay of the \(k\)th harmonic distortion that occurred in the impulse response can be expressed as [73]:  
\[\tau_{k,lin}(f) =-f\ \frac{\mathrm{T}}{f_{2}-f_{1}}\ \left(1-\frac{1}{k} \right), \tag{22}\] \[\tau_{k,exp}(f) =-\frac{\mathrm{T}}{\ln(f_{2}/f_{1})}\ \ln k,\]  
where \(\tau_{k,lin}(f)\) and \(\tau_{k,exp}(f)\) are the group delay of the \(k\)th harmonic in the system impulse response measured with linear and exponential sweeps, respectively. It can be observed that \(\tau_{k,lin}(f)\) depends on frequencies, while \(\tau_{k,exp}(f)\) is frequency independent (constant group delay). Figure 6 shows an example of measured system impulse responses by using linear (upper panel) and exponential (bottom panel) sweep signals [73]. The linear impulse responses and harmonic distortions are represented in the time-frequency domain (spectrogram). It can be observed that the linear sweep transforms harmonic distortions into down-sweeps (the frequency decreases with time) mostly before the linear impulse response begins. If exponential sweeps are used for the measurement, all harmonic distortions are packed in specific time intervals at negative times. Thus, compared to linear sweeps, the use of exponential sweep signals can better separate the linear impulse response and harmonic distortions of the system under test. Several studies demonstrated that the use of sweep signals, especially the exponential sweep, is more robust against the time-variance effect than using pseudo random sequences [37; 40; 41]. One of the reasons is that the exponential sweep varies the frequency more slowly at low frequencies than at high frequencies, and the low-frequency signals are less sensitive to phase shifts [40]. [PERSON] [70] proposed some further improvements of the exponential sweep method to enhance the SNR, suppress the pre-ringing at low frequencies, etc. [PERSON] et al. [77] mentioned that the environmental noise is mainly in low frequency ranges, and therefore proposed to use low-frequency emphasized sweep signals for acoustic measurements [78; 79].
## 3 Theoretical Volume Considerations in Assessing P30 and P32 and Rock Strength and Deformability
### Representative Elemental Volume (REV)  
Once boundaries of a structural domain are broadly established, it becomes easier to define a representative elemental volume (REV). There is no strict protocol or clear definition in the literature on how to determine an exact volume of a REV. Nevertheless, provided the REV fits within the structural domain, it can be a cube or sphere or another shape. An REV should be the minimum volume of rock mass within a structural domain that is statistically homogeneous beyond which any sub-domains behave (in terms of strength and deformability) essentially like the whole rock mass within a structural domain ([3; 5]). This concept is shown in Figure 1 in terms of engineering behaviour.  
In this research P30 and P32 for an REV are established via a structural domain approach first, followed by stepping down to the minimum volume that fits in a field-determined structural domain provided P30 and P32 are relatively homogenous within both the domain and the REV of a given domain. An REV established in this manner needs to be differentiated from a computer modelling discrete feature network (DFN) approach. In both the field-determined REV and DFN approaches, estimates of P30 and P32 require a definition of the total volume under consideration. In discrete fracture network (DFN) modelling, a block volume may include a large block that can be divided into several sample-size blocks that are generated in the digital computer space. The volume of smaller sample-size blocks within the large modelling block may or may not be the same volume established in the field-assessed REV approach described above. The results in both approaches are reported in the same units as in Figure 5 and Table 1.  
P30\({}_{\rm{REV}}\) and P32\({}_{\rm{REV}}\) is the nomenclature adopted in this paper (see Table 1) to describe the fracture size and intensity within a field-assessed REV. P30\({}_{\rm{REV}}\) is essentially the estimated number of fractures divided by the volume of the REV. P32\({}_{\rm{REV}}\) is the estimated fracture area divided by the volume of the field-assessed REV in question.  
P30\({}_{\rm{DFN}}\) and P32\({}_{\rm{DFN}}\) is the nomenclature adopted here (Table 1) to describe the fracture size and intensity for an assumed block volume generated in computer space in programs such as Fracman (WSP proprietary software Version 8.0, February 2021).
###### Abstract  
This research aims to optimize the solar-hydrogen energy system at Kangwon National University's Samcheok campus by leveraging the integration of artificial intelligence (AI), the Internet of Things (IoT), and machine learning. The primary objective is to enhance the efficiency and reliability of the renewable energy system through predictive modeling and advanced fault detection techniques. Key elements of the methodology include data collection from solar energy production and fault detection systems, energy potential analysis using Transformer models, and fault identification in solar panels using CNN and ResNet-50 architectures. The Transformer model was evaluated using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and an additional variation of MAE (MAE2). Known for its ability to detect intricate time series patterns, the Transformer model exhibited solid predictive performance, with the MAE and MAE2 results reflecting consistent average errors, while the MSE pointed to areas with larger deviations requiring improvement. In fault detection, the ResNet-50 model outperformed VGG-16, achieving 85% accuracy and a 42% loss, as opposed to VGG-16's 80% accuracy and 78% loss. This indicates that ResNet-50 is more adept at detecting and classifying complex faults in solar panels, although further refinement is needed to reduce error rates. This study demonstrates the potential for AI and IoT integration in renewable energy systems, particularly within academic institutions, to improve energy management and system reliability. Results suggest that the ResNet-50 model enhances fault detection accuracy, while the Transformer model provides valuable insights for strategic energy output forecasting. Future research could focus on incorporating real-time environmental data to improve prediction accuracy and developing automated AIoT-based monitoring systems to reduce the need for human intervention. This study provides critical insights into advancing the efficiency and sustainability of solar-hydrogen systems, supporting the growth of AI-driven renewable energy solutions in university settings.  
solar-hydrogen storage system; artificial intelligence (AI); Internet of Things (IoT); machine learning; fault detection +
Footnote †: journal: Applied sciences
## 3 An Approach to Ensuring the SDN Resilience
### Examples of CA Reference Models  
The SDN is an open network architecture that has been proposed in recent years to address some of the key weaknesses of traditional data networks. SDN proponents argue that network management logic and network functions are two separate concepts, and therefore should be separated into different layers. To this end, the concepts of control plane and data plane were introduced into SDN: a centralized control plane (otherwise called the controller) manages the logic of the network and controls traffic engineering functions from the data plane (called switches), which simply forward packets between networks.  
Thus, SDN can be viewed as a physically distributed switching structure with logically centralized control. It is designed to provide highly dynamic management and QoS/security policies.  
Consider the process of CAs implementation against SDN (Table 4).  
\begin{table}
\begin{tabular}{p{142.3 pt} p{284.5 pt}} \hline \hline
**Stages of Impact Implementation** & \multicolumn{1}{c}{**Basic Execution Methods**} \\ \hline Collection of information & The first stage of the attack implementation is the collection of information about the attacked system or node. It includes such actions as determining the network topology, the type and version of the operating system of the attacked node, as well as available network and other services, and so on. These actions are implemented in various ways. \\ \hline Exploring environment & At this stage, the attacker explores the network environment around the intended target of the attack. Such areas, for example, include the hosts of the “victim’s” Internet provider or the hosts of the remote office of the attacked company. At this stage, the attacker may be trying to determine the addresses of “trusted” systems (for example, the partner’s network) and nodes that are directly connected to the target of attack (for example, the ISP router), etc. Such actions are quite difficult to detect, since they are performed over a sufficiently long period of time and outside the area controlled by security measures (firewalls, intrusion detection systems, etc.) \\ \hline Network topology identification & There are two main methods for determining the network topology used by attackers: (1) TTL modulation; (2) recording the route. \\ \hline \hline \end{tabular}
\end{table}
Table 4: The process of CAs on SDN.  
Figure 4: The flowchart of SDN resiliency assessment in the conditions of CAs.  
To ensure the basic requirements for a model with a given level of accuracy and to obtain the most reliable probabilistic temporal characteristics, it is required to develop a complex model of the CA against the SDN, consisting of a verbal CA model of the SDN, a mathematical model and a simulation model.  
To develop a mathematical model, it is proposed to use the reference CA models and the TTSN method.
# Text Detection and Recognition for X-ray Weld Seam Images
## 2 X-ray Weld Seam Image Text Recognition Model Framework
### Evaluation  
#### 3.4.1 The Text Detection Experiment  
In the text detection experiments, we compared the improved text detection method proposed in this paper with the DBNet method, and the experimental results are shown in Figure 9,b. We observed that, especially when detecting longer text lines, the DBNet method has some issues, including incomplete detection of the text lines and the phenomenon of a long text line being recognized due to multiple overlapping bounding boxes. These problems significantly impact the subsequent text recognition, leading to a decrease in the recognition accuracy. However, by introducing a post-processing method, we successfully overcame these issues. The post-processing method ensures the complete and standardized detection of long text lines, providing high-quality data for downstream text recognition tasks.  
Additionally, we compared our method with other state-of-the-art methods, and the experimental results are shown in Table 1. Although the precision of our method is slightly lower than the PSENet algorithm, both the recall and F1 value are superior to the other algorithms compared in this paper. Moreover, compared to DBNet, our method shows improvements of 1.9%, 0.3%, and 1.1% in precision, recall, and the F1 value, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Precision/\%** & **Recall/\%** & **F1/\%** & **Parameters (M)** \\ \hline DBNet [5] & 89.1 & 92.1 & 90.6 & 13.8 \\ DBNet++ [6] & 88.5 & 91.5 & 89.9 & 29.0 \\ FCENet [4] & 51.8 & 61.1 & 56.0 & 26.3 \\ PSENet [3] & **93.5** & 84.6 & 88.8 & 29.2 \\ Mask R-CNN [2] & 65.0 & 80.0 & 71.7 & 44.0 \\ Our & 91.0 & **92.4** & **91.7** & 13.8 \\ \hline \hline \end{tabular} Bold represents the best result, underline represents the second best result. This explanation applies to all tables below.  
\end{table}
Table 1: Comparison with the results of other text detection algorithms.  
Figure 9: Comparison chart of text detection results: (**a**) DBNet; (**b**) our.
## 4 Effect of Continuous Low Thrust on Orbital Elements
### Effect of Continuous Normal Thrust on Changes in Orbital Elements  
When \(a_{\rm N}\
eq 0\), the normal acceleration affects the orbit inclination and RAAN (Right Ascension of Ascending Node) as shown in Equation (13).  
\[\left\{\begin{array}{l}\frac{{\rm d}i}{{\rm d}t}=\frac{r\cos u}{na^{2}\sqrt{1- e^{2}}}a_{\rm N}\\ \frac{{\rm d}\Omega}{{\rm d}t}=\frac{r\sin u}{na^{2}\sqrt{1-e^{2}}\sin i}a_{\rm N }\end{array}\right. \tag{12}\]  
For near-circular orbits, Equation (13) simplifies to  
\[\left\{\begin{array}{l}\frac{{\rm d}i}{{\rm d}t}=\frac{\cos u}{na}a_{\rm N} \\ \frac{{\rm d}\Omega}{{\rm d}t}=\frac{\sin u}{na\sin i}a_{\rm N}\end{array}\right. \tag{13}\]  
If the direction of the normal acceleration is constant, the orbital inclination and the RAAN will vary periodically, integrating to zero over an orbital period. In order to increase the efficiency of the thrust action, if the normal thrust is applied for the purpose of changing the inclination of the orbit, the direction of the thrust should be reversed at Argument of Latitude of \(-\frac{\pi}{2}\) and \(\frac{\pi}{2}\) so that the inclination of the orbit continues to change. Integrating the first equation of Equation (14) at \([-\frac{\pi}{2},\frac{\pi}{2}]\) gives  
\[\int\!\!\frac{\pi}{-\frac{\pi}{2}}\frac{{\rm d}i}{{\rm d}t}{\rm d}u=\int\!\! \frac{\pi}{-\frac{\pi}{2}}\frac{\cos u}{na}a_{\rm N}{\rm d}u \tag{14}\]  
\[\frac{{\rm d}i}{{\rm d}t}=\frac{2}{\pi na}a_{\rm N} \tag{15}\]  
If a normal thrust is applied for the purpose of changing the RAAN, the direction of the thrust should be reversed at Argument of Latitude of 0 and \(\pi\) so that RAAN continues to change. Integrating the first equation of Equation (14) at \([0,\pi]\) gives  
\[\int_{0}^{\pi}\frac{{\rm d}\Omega}{{\rm d}t}{\rm d}u=\int_{0}^{\pi}\frac{\cos u }{na\sin i}a_{\rm N}{\rm d}u \tag{16}\]  
\[\frac{{\rm d}\Omega}{{\rm d}t}=\frac{2}{\pi na\sin i}a_{\rm N} \tag{17}\]  
In our simulation analysis of the change in orbital inclination and RAAN under normal acceleration, the initial values were set to \(53^{\circ}\) and \(353^{\circ}\). The thrust acceleration was \(2.26\times 10^{-4}\) m/s\({}^{2}\) and other simulation conditions were as described in Table 1. When the normal thrust is applied for the purpose of changing the orbit inclination, the orbit inclination changes as shown in Figure 4. When the normal thrust is applied with the purpose of changing the ascending RAAN, the change in RAAN is shown in Figure 5.  
According to Equation (10), if the direction of radial thrust is not changed, both semi-major axis and eccentricity vary periodically. Although both tangential and radial thrusts affect the semi-major axis, the semi-major axis varies continuously under tangential thrusts and periodically under fixed-direction radial thrusts, and the radial thrusts have few application scenarios and do not act efficiently. Therefore, if the change of the semi-major axis is observed in a long-time continuous small thrust action scenario, we prioritize the results of tangential thrust action. In the following, we will not consider the case of radial thrust action.
# Design and Demonstration of a Flying-Squirrel-Inspired Jumping Robot with Two Modes
## 1 Introduction
Different from the locust, which is frequently considered as the bionic object of the jumping-gliding robots, flying squirrels do not rely on extra wings to glide through the air, but rather use their expansive patagium composed of the skin that spans from the squirrel's neck to its forelimbs and back to its hind limbs [28; 29; 30]. With the adjustment of limbs and tail, they can generally glide 50 m, the longest record of 150 m [31; 32; 33; 34; 35; 36]. Because of the flexibility and scalability of the patagium, flying squirrels also can jump on the ground like ordinary squirrels without restraints on limbs [37; 38; 39]. These features of flying squirrels provide a lot of bionics inspiration for the design of jump-gliding robots. This paper presents the biomimetic design and development of a prototype of flying squirrel inspired jumping robot. The robot has two modes of jumping for gliding and jumping for progress, which lays a foundation for the design and development of subsequent jumping-gliding robots. The major contributions of this research can be summarized into two aspects. First, we observed different jumping modes and analyzed the movement characteristics of flying squirrels, especially the change of hind limbs joint angles, which have not been mentioned in the literature of flying squirrels before. Second, we designed a new spring charging and releasing mechanism using a single drive to achieve controllable energy storage and release. Furthermore, we developed a jumping robot with two jumping modes by adopting the mechanism. To the best of our knowledge, it is the first miniature bionic jumping robot that has the capabilities of changing the speeds and stances autonomously at takeoff. The rest of this paper is organized as follows. First, the design goal of the robot is defined in Section 2. Then, we collect and analysis jumping information of the flying squirrel in Section 3. Based on the information, a jumping robot is designed in Section 4. The requirement for aerial posture of the robot after taking off is analyzed, and the dynamic model of adjusting the pitching by the swing of the forelimbs is established and simulated in Section 5. Finally, we present the prototype and experimental results for pitching adjustment and demonstrate the two different jumping modes abilities of the robot in Section 6.