esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Hundreds of millions of citizens participate in sports, among which running is widely known. Injury remains high with the popularity of running, with 19.4% to 79.3% sustaining a running-related injury annually [1]; patellofemoral pain syndrome (PFPS) is one of the most common running injuries, exhibiting the highest incidence of 17% in the specific pathologies of running-related injuries [2]. Increased patellofemoral joint stress (PFJS) was determined as an important pathogenic factor [3]. The knee joint features a complex structure; the soft tissue and muscles maintain the stability of the tibia [4]. Thus, any change in mechanics may influence the force distribution around the patellofemoral joint. Studies have shown that patients with patellofemoral joint pain exhibit different biomechanical characteristics during running [5]. Therefore, changes in knee joint torque, patellofemoral contact force and stress during this activity may affect the risk of PFPS. Correspondingly, the mechanism of overwork injury is due to the cumulative effect of repeated high-load work of the muscles and bones [6];therefore, reducing the knee load to a certain extent may considerably decrease the cumulative effect of long-term running on the knee joint.  
With the development of technology, new materials and designs have been applied to sports shoes, especially midsole structures, to reduce the load on the lower extremities. The unique mechanical structures of different sports shoes can change the force characteristics of the human running gait [7]. For healthy runners, wearing different sneakers can affect the biomechanical characteristics of the lower extremities, such as stride frequency, ground contact angle, vertical load rate and joint force [8]. In recent years, barefoot running has become increasingly popular, and many runners have been opting for minimalist shoes, which provide basic protection but limited cushioning. The standardised definition of minimalist shoes, the Minimalist Index (MI), was developed to allow running shoes to be distinguished based on their degree of minimalism, and may help to decrease injuries related to footwear transition [9]. Numerous studies have verified [7; 10; 11] that minimalist shoes can change the landing posture of the lower limbs, mainly focusing on the adaptation of the ankle joints, that is changing the angle between the foot and the ground, the tendency of forefoot pattern strike and the adaptability of the calf triceps and the Achilles tendon. Relatively few studies have focused on knee load and this condition limits the understanding of the relationship between sneakers and knee joints, especially the patellofemoral joint.  
This study aimed to determine the biomechanical characteristics of the patellofemoral joint during running. To achieve this the immediate effects of wearing running shoes with different minimalist indices on patellofemoral contact force (PFCF) and patellofemoral contact stress (PFJS) were explored. Based on the previous observations, it was hypothesized that PFCF and PFJS would decrease when wearing running shoes with high Minimalist Index (MI), e.g., minimalist shoes.
## 2 Laboratory Test Programs
### Test Apparatus, Specim Preparation, and Testing Procedure  
The KTL-DYN8 CH cyclic triaxial test apparatus is used in this research (Figure 3). The apparatus consists of an electro-hydraulic servo controller with a double amplitude displacement of 50 mm (\(\pm\)0.035 mm) and an operational frequency range of 0.01-5 Hz. The confining chamber is designed to handle pressure up to 2 MPa. Three pressure transducers are equipped to measure cell pressure, back pressure, pore-water pressure, with the maximum capacity of 2 MPa, and a load transducer to measure the submersible load less than 10 kN. The testing is controlled by a fully automatic dynamic controller unit, which conveys the instructions and records the testing data with the help of the GeoSmart Lab software.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Soil** & \(\mathbf{G_{\mathrm{s}}}\) & \(\mathbf{e_{\mathrm{min}}}\) & \(\mathbf{e_{\mathrm{max}}}\) & \(\mathbf{d_{50}}\) (mm) & \(\mathbf{d_{10}}\) (mm) & \(\mathbf{d_{60}}\)/\(\mathbf{d_{10}}\) \\ \hline Fujian sand & 2.64 & 0.69 & 1.01 & 0.175 & 0.115 & 1.65 \\ \hline \end{tabular}
\end{table}
Table 1: Physical properties of the Fujian sand.  
Figure 1: Grain size distributions of the Fujian sand.  
Figure 2: Scanning electron microscope images of the sand particles (0.1–0.25 mm).  
All solid cylinder specimens are 50 mm in diameter and 100 mm in height. According to China standard [36], the specimens are prepared using the dry pluviation method in this study. The oven-dried Fujian sand specimens are prepared in four layers into the membrane-lined split mold with a funnel. A vacuum pressure of 20 kPa is applied before removing the mold to maintain the stability of the sand specimen. Then the confining chamber is installed, and de-aired water is filled, followed by the saturation and consolidation stages. The CO\({}_{2}\) is flushed through each specimen for about 1 h using a confining pressure-assist of about 20 kPa, to accelerate the saturation process. Then, the de-aired water passes through each specimen from the bottom to the top until the air bubble disappears. This process generally takes 30 min, followed by increasing the back pressure and confining pressure step by step. The B value of 0.95 (i.e., complete saturation) is generally obtained when the back pressure is about 300-400 kPa in this study. The specimen is isotropically consolidated to the target effective confining pressure in a loading sequence. Time of confinement is at least 250 min at each pressure during the period of tests.  
A series of strain-controlled cyclic triaxial tests are then performed at 1.0 Hz loading frequency, corresponding to the strain level which varies from \(10^{-4}\) to \(10^{-2}\). Five cycles of cyclic loading are applied during cyclic triaxial tests and the second cycle of loading is used to determine the shear modulus in the present study. The sand specimens are tested at three different relative densities (\(D_{\rm r}\) = 30%, 50%, 70%) and six effective confining pressures (\(\sigma_{\rm c}\) = 50, 100, 300, 500, 700, 900 kPa).
# Development and Research of a Medium Vertical Dipole Electromagnetic Pulse Simulator
## 1 Introduction  
Electromagnetic energy can be coupled into the power and electronic systems through cables, antennas, and some holes or gaps, leading to damage or confusion in hardware or software, thus compromising the reliability and security of the system [1; 2; 3; 4]. High-altitude nuclear explosion is an important way to generate strong electromagnetic pulses (EMP), which have the characteristics of a high peak field strength, wide coverage range, and multiple coupling targets. In these environments, modern information equipment must be protected [5; 6]. Among them, the early (E1) environment of a high-altitude electromagnetic pulse (HEMP) is generated by the interaction of instantaneous gamma rays with the air medium, and the transient electromagnetic pulse waveform formed can be equivalent to an ideal double exponential waveform with a front edge of only a few ns, a pulsewidth of tens of ns, and a peak field strength of more than 50 kV/m, which can pose a threat to a lot of large- and medium-sized modern equipment [7]. Building an EMP simulator in the laboratory that can generate a strong electromagnetic environment with the above indicators is currently the most realistic, economic and efficient method to improve the anti-HEMP performance of modern equipment, carry out EMP effect and protection performance tests, and conduct threat level assessments [8; 9].  
In this study, we developed a medium-sized vertical dipole radiating EMP simulator with a 5 m antenna that has an inverted cone structure with a half-cone angle of 32 degrees. Two different driving sources were designed to feed high-voltage pulses separately into the radiation antenna, forming a circular EMP simulation test environment with a 25 m diameter around the conical vertex of the antenna. On this basis, the waveforms of the radiated electromagnetic pulses generated by the simulator under different feed-in modes were measured, and the reasons for waveform distortion before and after high-voltage pulse feeding into the antenna were analyzed. As there are some differences between the ideal state and the actual engineering implementation of the antenna, the distribution of the radiation field of the antenna was studied under different cable numbers, different end processing methods, and different resistive loading on the antenna cables, and the factors affecting the radiation characteristics of the antenna and the waveform of its radiated EMP field were analyzed based on the measurement results. Through the aforementioned work, a comprehensive understanding of the performance, radiation characteristics, and engineering factors affecting the electromagnetic environment generated by the simulator has been obtained, which is of great significance for mastering the development technology of such simulators, antenna radiation characteristics, and conducting the relevant assessment and effect experiments.
## 2 Materials and Methods
### Assessment for Rock Fragmentation of Bench Blasting  
In this study, full-scale bench blasting was performed for each stemming material (sand or STF), and an image-based blast fragmentation method was applied to compare and evaluate the performance of each stemming material.  
Sieving or screening is a direct and accurate method for evaluating the size distribution of particles or their fragmentation. This method is feasible for small-scale blasts or operations; however, it is costly and time-consuming. Rock fragments are screened through sieves of different mesh numbers for different fragment sizes, and the screened fragments are grouped based on their size. The nature of the blast was predicted by counting the number of fragments of each size [37].  
WipFrag is an image analysis system for sizing materials, such as blasted or crushed rocks [38]. It has also been used to measure other materials, such as ammonium nitrate prills, glass beads, and zinc concentrates. WipFrag accepts images from a variety of sources, such as roving camcorders, fixed cameras, images, or digital files. It uses automatic algorithms to identify individual blocks and to create an outline \"net\" using state-of-the-art edge detection. If desired or necessary, manual intervention (editing the image net) can be performed to improve the fidelity. WipFrag measures a 2D net and reconstructs a 3D distribution using the principles of geometric probability. WipFrag supports two methods: Rossin Rammler and Swebrec. Two parameters were used by [PERSON] as key performance indicators (KPIs); \(X_{c}\), known as the characteristic size of the distribution and more specifically D63.2, and \(n\), the value of which is the measure of uniformity [39].  
Figure 4: (**a**) Shear thickening fluid-based stemming material image and (**b**) package products.  
Analyzing every fragment in the rock muck pile is fortunately not necessary because it is widely accepted that the mass percentage of fragments smaller than any given size varies linearly with the fragment size when plotted in the Rosin-Rammler domain. By measuring only a sufficient number of particles, the slope and intercept of the Rosin-Rammler line can be confidently defined [40]. A Rosin-Rammler line can be expressed as in Equation (3).  
\[R(X)=1-\exp\left[-\left(\frac{X}{X_{c}}\right)^{n}\right] \tag{3}\]  
Here,  
\(R(X)=\) Cumulative fraction by weight undersize in relation to size \(x\).  
\(X_{c}=\) Size modulus, which defines the characteristic size of the distribution.  
\(n=\) Distribution modulus, which defines the spread of the distribution.  
For \(R(X)=\) 0.5 (i.e., 50% of the fragments passing through the sieve), the value of \(X_{c}\) can be measured as follows:  
\[X_{c}=\frac{X_{50}}{0.693^{1/n}} \tag{4}\]
## 3 Results and Discussion
### Effectiveness of LLME-SPME on Other Volatile Organic Contaminants in Water  
The toluene and TCB were selected to test the effectiveness of LLME-SPME method because both are among the top 15 most frequently detected VOCs in aquifers [3]. Water samples contain 20 ng mL\({}^{-1}\) of toluene and TCB each were used. The extraction conditions for toluene and TCB are the same as the optimum conditions for TCE. The peak areas of extracted molecular ions (e.g., _m/z_ 92 for toluene, _m/z_ 182 for TCB) were used to compare the extraction efficiencies between regular SPME method and LLME-SPME methods with hexane, pentane, and chloroform. The results are graphed in Figure 7. For both toluene and TCB, the LLME-SPME with hexane method gives the better extraction efficiency than the other methods (_p_-values of 10\({}^{-4}\) and 0.06 for toluene and TCB by paired t-test evaluation between the largest peaks and second largest peaks with 95% confidence intervals). The extraction efficiencies for toluene and TCB by LLME-SPME method would be further improved after optimization. Therefore the LLME-SPME method could be used for the analysis of various VOCs in aqueous matrices.  
Figure 6: Gas chromatography (GC) chromatogram for spiked river sample containing TCE at 20 ng mL\({}^{-1}\) (**A**) and mass spectrum of TCE/TCE-_d_ peak at 0.416 min (**B**).
## 2 Materials and Methods
### Development of the Self-Adaptive Transtibial Prosthesis Concept
#### 2.1.1 Conceptualization of the Limb Length Discrepancy Detection System  
The detection system concept used in this research targets the detection and compensation of lower-limb length discrepancy (LLD) using a Master-Slave type system. This system's functional block diagram is illustrated in Figure 1. The system architecture comprises two distinct components: the Master and the Slave. Both components are equipped with dedicated microcontrollers (1) and (2) to manage signals and commands.  
The proposed communication protocol between the Master and Slave components is proposed to occur via a unidirectional wireless connection facilitated by Bluetooth modules (3) and (4) integrated into the development boards, employing a low-latency data transmission protocol. This setup ensures communication between the sensor and control system, allowing the prosthetic movements to be nearly simultaneous with the user's intentions. Limb discrepancy data are collected from the user's lower limbs using an accelerometer (5) strategically placed on the lower limbs. These data are processed and displayed in real  
Figure 1: Functional block diagram of the self-adaptive transtibial prosthesis.  
time on a display (6) integrated into the Master module, providing visual feedback to the user and ensuring constant monitoring of limb position and length discrepancy.  
From an electrical perspective, each module is equipped with a stabilized power circuit that is optimized to support both the microcontroller and the actuator (7) integrated within the extensible prosthetic module (MOPE). In the Slave module, the actuator is managed by the secondary microcontroller (2) via a driver (8) powered by a 12 V source (9). This actuator receives real-time commands from the Master module, which controls the system's operation. The activation and deactivation of the command are facilitated by a push-type switch (10). The generation of commands is based on the analysis and processing of data collected by the accelerometer mounted on the affected lower limb, thereby ensuring precise adjustment of length to compensate for the detected discrepancy. Information about the operations executed by the module is displayed in real time on both a display (11) and an LED module (12), facilitating efficient monitoring of system functionality.
# Optimizing Sustainable Energy Transitions in Small Isolated Grids Using Multi-Criteria Approaches
## 4 Results and Discussion
### Performance of the Optimized Generation System
We end by pointing out that the excesses are extremely high in the last two scenarios (Figure 13). These surpluses reach values close to 50% of the energy produced by the system in the favorable case, and around 30% in the average year. This indicates how these systems face the issue of large surpluses. Given that the generation relies heavily on highly variable and unreliable resources, there is a significant difference in system performance depending on when and how these resources are available. For this reason, there is always a substantial oversizing of the generation and/or storage system to provide the energy demanded under any circumstances. This situation can be mitigated or even eliminated if there is a strong capacity for dispatchable generation, which can cover this variability when necessary. In El Hierro's system, the dispatchable energy  
Figure 13: Optimized system performance with the renewable resources of the year: (**a**) 2017; (**b**) 2018.  
source is diesel engines, which could not be completely eliminated from the proposed mix due to system constraints. While a fossil fuel source is still maintained, another alternative for a fully renewable system would be to use a dispatchable renewable generation source, such as biomass or geothermal energy. However, geothermal energy is not available on the island, leaving biomass as the only option. Unfortunately, no significant biomass fuel is available on the island, nor on any other islands in the archipelago. Therefore, most of the biomass would have to be imported, but it is important to remember that the system would act as a backup, not as a continuous generation source. It should also be noted that when the other renewable generation sources cannot provide the required energy, the backup would need to operate significantly during certain periods. In fact, as shown for a year with abnormally low renewable resources, there is substantial diesel generation, which is greatly reduced in a year with high renewable resources.
# An Autonomous Wireless Device for Real-Time Monitoring of Water Needs
## 3 Materials and Methods
### Hardware Description
#### 3.1.1 Monitored Parameters and Sensors  
According to [4], the majority of papers monitored between three and four parameters with soil moisture being the most used in soil sensors, and air temperature and humidity as atmospheric parameters. Soil temperature, electrical conductivity (EC), and luminosity are other parameters that are measured regularly [5].  
In relation to the measurement of soil moisture, most studies use the conductivity between two electrodes that are inserted into the soil by measuring the constant dielectric of the soil. Most published works use the low-cost sensors YL69 (Sparkfun Electronics, Niwot, CO, USA) and VH400 (Vegetronix, Inc., Riverton, UT, USA) that operate with Arduino. However, we both tested and discarded them because they did not behave stably under real conditions.  
Thus, to measure the soil parameters, we used well-known commercial sensors [35] (see Table 2) as the capacitive sensor of the frequency domain reflectometry (FDR) type ECH2O 10 HS (Meter Group, Inc, Pullman, WA, USA) to measure soil moisture, and ECH2O 5 TE to measure soil moisture and temperature, as well as EC. These sensors were not specifically created to be integrated in an Arduino measuring system, but we developed our own code written in the open source Arduino Integrated Development Environment (IDE) software and uploaded it to the board to be able to work with it.  
The low-cost DHT11/DHT22 sensors (Adafruit Industries, New York, NY, USA), which provide temperature and relative humidity readings, as well as light-dependent resistance (LDR), are the most commonly used sensors for smart irrigation monitoring [4]. Just as we did to test low-cost soil sensors, we tested these sensors for a year in real conditions and they worked well, and since IoT irrigation systems generally do not need the highest level of accuracy, we decided to use them for our work.
# Quality Assessment of Photogrammetric Methods--A Workflow for Reproducible UAS Orthomosaics
## 4 Discussion
### Optimized Georeferencing Accuracy  
The determination of optimal tie point filters leads to positional precisions of less than 6 cm in forested areas. Regarding the GSD of 2.58 cm/px the resulting orthomosaics have a positional error of up to three pixels. This error is stable over multiple computations and different sets of images from the six flights over the forest. This suggests that the iterative filtering approach leads to robust RE thresholds and only needs to be computed one time.  
The difference of 0.04 m in the checkpoint error between the six flights could come from different GNSS satellite constellations or cloud conditions [41] over the three hours the flights took place, but, most likely, these small differences come from slight inaccuracies during the manual alignment of the GCP. This suggests that the operational workflow consistently leads to viable orthomosaics with resolutions of less than 10 cm, which is more than sufficient for detailed spatio-temporal structural analysis of forests [9; 10]. In [PERSON] et al. [8], a checkpoint error of 1.4 m and a GSD of 15 cm led to validated object-based analysis even in moderately dense canopies. The accuracy in the experimental forest areas even keep up with the checkpoint error in the grassland time series (between 0.04 m and 0.08 m). This also compares very well with other studies in structurally sparse landscapes where checkpoint errors tend to be very low [33; 42].  
The grassland time series further demonstrates that the proposed methods of optimization and validation work outside of the experimental setup. Differences in flight planning, low quality GNSS measurements at the GCP and the usage of different cameras still led to consistent and accurate orthomosaics. Hence, the provided workflow can be used as a fully operational method in grassland and agricultural contexts.  
Figure 9: Example of the positional change of a tree between the forest flights 01 and 05.