esa-sceva/satcom-chunk-collection huggingface dataset:
# Quantitative Retrieval of Soil Salinity Using Landsat 8 OLI Imagery
## 5 Conclusions  
Soil salinization is a serious land degradation problem. It adversely affects the ecological balance, causes large-scale desertification and the decline or loss of land productivity, and negatively impacts agriculture and the economy. Therefore, soil salinization monitoring is crucial. However, inversion models of soil salinization are affected by soil type, rainfall, temperature, evapotranspiration, vegetation cover, and other factors. This paper investigated the influence of spatial heterogeneity of soil moisture on the inversion accuracy of soil salinization. The results showed the following: (1) the spatial distribution of soil moisture in the floodplain could be accurately estimated using the TVDI derived from Landsat 8 OLI images. Due to the influence of temperature, precipitation, soil type, and vegetation cover, the soil moisture in the Songnen Plain showed a trend of wet-dry-wet from the southwest to the northeast. (2) The spatial difference in the soil moisture significantly impacted the inversion accuracy of the soil salinity model. The inversion model underestimated soil salinity in regions with soil relative humidity higher than 50% and overestimated it in very dry regions. (3) The spatial heterogeneity of soil moisture should be considered in retrieval models of soil salinization. The establishment of soil salinityinversion models in areas with different soil moisture levels can improve the inversion accuracy.  
Conceptualization, X.N. and R.D.; methodology, X.N.; software, X.N.; validation, X.N.; formal analysis, X.N.; investigation, X.N.; resources, X.N.; data curation, R.D.; writing--original draft preparation, R.D.; writing--review and editing, X.N.; visualization, X.N.; supervision, X.N.; project administration, X.N. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Natural Science Foundation of Heilongjiang Province, China grant number YQ2020D005.  
Not applicable.  
The data presented in this study are available on reasonable request from the corresponding author.  
We than anonymous reviewers who suggested several improvements to the article, and facilities who provided technical supports to the field experiment.  
The authors declare no conflict of interest.
# A New SEPIC-Based DC-DC Converter with Coupled Inductors Suitable for High Step-Up Applications
## 5 Simulations and Experimental Results  
### Simulation Results  
A simulation of the ideal SEPIC-based dc-dc converter with coupled inductors was performed using Caspoc software [67]. This simulation was realized as a first validation of the theoretical considerations. The values of the components are the same as those from the design example. The simulation is made in an operating point, considering the input voltage \(V_{g}=30\) V, the static conversion \(M=4\). For these values, the duty cycle is \(D=0.6375\).  
The dc output voltage, \(V_{o}\), predicted by the simulation was 120 V, as Figure 8 reveals, exactly as theoretically calculated with (7). In Figure 9, the magnetizing inductor current is presented, confirming its triangular shape, obtained in Caspoc with the help of the mathematical blocks. For inductor \(L_{1}\), the first part of the triangular shape of the magnetizing current can be seen in Figure 10. The second part of the triangular shape of the magnetizing current can be seen in Figure 11, as the inductor \(L_{2}\) current. The voltage and current for inductor \(L_{3}\) are presented in Figure 12. The correct CCM operation of the converter is proven by the voltages and currents for the coupled inductors \(L_{1}\) and \(L_{2}\), and inductor \(L_{3}\). The voltage and current for the inner capacitor are presented in Figure 13 and, for the output capacitor, are depicted Figure 14. In Figures 15-18, the voltage and current for the transistor and diodes are presented.  
Figure 8: Output dc voltage.  
Figure 10: Voltage (blue) and current (red) of the primary winding \(L_{1}\).  
Figure 9: Magnetizing inductor current \(i_{LM}\).  
Figure 11: Voltage (blue) and current (red) of the secondary winding \(L_{2}\).  
Figure 12: Voltage (blue) and current (red) for the inductor \(L_{3}\).  
Figure 13: Voltage and current for the internal capacitor C\({}_{1}\).  
Figure 16: Voltage and current corresponding to the diode \(D_{1}\).  
Figure 14: Voltage and current for the output capacitor \(C_{2}\).  
Figure 15: Voltage and current corresponding to the transistor \(S\).  
The simulation results completely validated the theoretical considerations, both qualitatively and quantitatively.
# Social Media, Quo Vadis? Prospective Development and Implications
## 2 Theoretical Background and Development of Projections
### Interactive Technologies  
Perpetual innovation of digital technologies is transforming social media [14]. This is especially the case for the user experience when interacting with the platform [15]. From being mainly a text-based medium (e.g., early Twitter and Facebook) to incorporating image and videos (e.g., later Facebook, Instagram, and Pinterest), social media keeps on adapting new technologies [5].  
One of these technologies that is already integrated into several digital services is Augmented Reality (AR), which adds virtual objects to the real physical world [16; 17; 5; 18], and Virtual Reality, which creates an artificial environment for the user to immerse in [18; 19; 20; 21; 22; 23; 24]. In social media, AR is most commonly used in the shape of face filters, for instance on Snapchat. Furthermore, Facebook created a platform called Spark AR that allows users to create their own AR filters and effects for Instagram [25]. In addition to that, Facebook is working on a social VR experience that can be described as an interactive virtual world, where users can play, create, and explore together [26]. Therefore, we state the following projections: P1. Apart from text-, image-, and video-based social media platforms, also platforms mainly based on virtual reality will emerge. P2. Some social media platforms will use augmented reality (e.g., to display information or commercials while looking at things).  
Besides sound and sight, other sensory stimuli such as touch, taste, and smell are mostly unused so far but could emerge in the future. Whereas touch- and movement-based human-technology interaction is on its way, gustatory and olfactory senses are dependent on chemical transduction, which cannot be easily digitized yet [27]. In that sense, we formulate the following projections. P3. Apart from the visual and auditory sense, some social media platforms will also address the somatosensory sense. P4. Apart from the visual and auditory sense, some social media platforms will also address the olfactory sense. P5. Apart from the visual and auditory sense, some social media platforms will also address the gustatory sense. P6. Apart from entering text or uploading media, some platforms will also allow for a touch- and movement-based interaction. P7. Apart from entering text or uploading media, some platforms will also allow for a thought-based interaction.  
Social bots are another notable technology. These are computer algorithms able to interact in human-like behavior and create content [28; 29; 30; 31; 32]. Not only can bots influence and manipulate the opinion of users [33], they are also difficult to detect. Bots have become increasingly sophisticated and advanced in their behavior. Hence, recognizing the difference between a human and a bot is increasingly challenging [34]. Considering this, we posit as follows. P8. Users will not recognize a difference when interacting with a real person or an AI-based bot.
###### Abstract
[13] obtained the full-field deformation distribution of sand sample surface by a plane strain experimental device and incremental reliability-guided digital image correlation (RG-DIC) technology, analyzed the evolution law of shear zone in sand, and determined the inclination angle and thickness of the shear zone, which showed that RG-DIC technology had good application effect in large deformation geotechnical experiment. [PERSON] et al. [14] measured the Poisson's ratio of three commonly used sealing elastomers by digital image correlation (DIC) and tested the mechanical properties of samples under three different constant stresses to understand the application prospect of DIC in mechanics. The accuracy of the non-contact video measurement method is verified to be satisfactory [15; 16]. Therefore, the non-contact measurement technology is introduced into the underground granary, which can, in real time, record the trajectory of the granary, the position of the displacement mutation, and the whole process of unstable failure. Currently, in geotechnical engineering, PIV technology has been used to study the observation of soil movement and the interaction between soil and structure [17; 18; 19; 20; 21; 22; 23; 24]. [PERSON] et al. [19] applied PIV technology in geotechnical engineering experiment, which laid a foundation for the development of PIV technology in the geotechnical field. [PERSON] et al. [20] developed small-scale physical modeling to investigate the bearing capacity of piles and the soil displacement around the pile using a transparent soil and particle image velocimetry (PIV) technique. [PERSON] et al. [21] compared the stress-strain curves of transparent sand made of fused silica sand and natural sand, and found that the two are particularly similar, and the failure model of transparent sand was basically the same as that of standard sand. [PERSON] et al. [22; 23; 24] studied the pile-soil interaction using PIV technology and transparent soil technology. Therefore, it is necessary to introduce the PIV technique to study the soil deformation around underground granaries and discuss the meso-motion law of soil particles around underground granaries. In this paper, based on digital image correlation (DIC) technology and particle image velocity (PIV) technology, a non-contact experiment system is introduced to investigate the interaction between the underground granary and its surrounding soil. Firstly, a non-contact experiment system is prepared, and its accuracy is verified. Then, this system is employed to investigate the displacement of an underground granary and the deformation of the soil around the granary subjected to water buoyancy. Finally, the soil pressure theory  
underground reinforced concrete granary in the project [26]. The model granary was made from photosensitive resin by 3D printing, and the parameters of the photosensitive resin are shown in Table 3[27].
# UWB-Based Real-Time Indoor Positioning Systems: A Comprehensive Review
## 3 UWB Positioning System
### Optimization Filters and Fusion-Based Algorithms for IPSs
#### 3.3.2 Kalman Filters (KFs)  
The Kalman filter is a recursive estimation method that is typically used to calculate or predict the states of a system based on a model or noisy measurements of it considering additive white Gaussian noise. Its method of operation is based on predicting the next state based on information from the previous state. It calculates the values of the states of a dynamic system in an optimal way that minimizes the expected value of the square of the difference between the prediction and the correct state, and then uses them as inputs for the next iteration [24]. Many developments have been made to the regular Kalman filter to improve its operation, resulting in new types of this filter, the most prominent of which are the extended Kalman filter (EKF) and the unscented Kalman filter (UKF).  
The limitations of the Kalman filter in dealing with primarily linear systems led to the development of the extended Kalman filter, which represents the nonlinear version of the Kalman filter. The unscented Kalman filter (UKF) was proposed by [PERSON] and [PERSON] in 1997. The central operation of the Kalman filter is to propagate a Gaussian variable (GV) through the dynamics of the system. In the EKF, the state distribution is approximated by the GV, which is then propagated through a first-order linear equation of the nonlinear system. This process can introduce large errors in the posterior mean and variance of the transformed GV, which can result in poor performance and deviations from the true value. The UKF is used to address this problem, using deterministic sampling. The state distribution is again approximated by the GV, which is now represented by a set of carefully selected sample points rather than the single average point in the EKF. The captured sample points contain the true mean and variance of the GV. When propagated through the true nonlinear system, the posterior mean and variance are accurately scaled to the third degree (Taylor series expansion) of the nonlinear state. This is not possible with the EKF, as it does not exceed first-class accuracy [25; 26].
# DRLLA: Deep Reinforcement Learning for Link Adaptation
## 7 Conclusions and Future Work  
In this paper we examined a deep reinforcement learning method for link adaptation and tested several algorithms on different channels. In order to properly leverage the promising advantages of DRLLA, we propose the following directions for future improvements. First, the current reward function optimizes for throughput. However, as we saw in our experiments, this can lead to other transmission metrics taking a hit. When applying LA in real-world scenarios, some use cases can benefit from a balance of different transmission metrics, including latency, PER, and data rate. For a more balanced implementation, we can derive a more complex reward function that considers additional characteristics of the transmission system. The achieved data rate may drop slightly, but the overall system can potentially benefit from other application-specific objectives.  
Additionally, a more sophisticated sampling method can be used to interpret the produced probability distribution (e.g., only sample from the actions with the top k probabilities). The field of generative language modeling proposes many different approaches, such as Top-K [21] or Top-P [22] sampling methods. This would prevent the extremely low likelihood, suboptimal actions from being used due to chance.  
Also, the observation space can be increased to further enhance the agent's decision-making process. Observing additional channel-related metrics might improve DRLLA's performance due to the agent's increased knowledge about the state of the system.  
Finally, to close the gap with the state-of-the-art mobile telecommunication networks and to gain from underlying mechanics that improve a real-world outdoor radio transmission besides LA, an upgrade of the radio simulation flowgraph from IEEE 802.11p to 4G LTE or 5G NR can be beneficial. This enhancement potentially increases the DRLLA performance on the real-world channel without touching the LA part.  
In conclusion, this paper presents the potential of a DRL-based approach for LA called DRLLA and identifies potential advantages when compared to the state of the art. Results hint that the reward function plays a major role in this work and could be used to adjust for one or several application-related objectives. Thus, this can be a potential detail to continue future research.  
Investigation, F.G.; Methodology, F.G. and D.W. ([PERSON]); Software, F.G.; Supervision, D.W. ([PERSON]) and A.V.; Validation, A.V.; Writing--original draft, F.G.; Writing--review and editing, D.W. ([PERSON]), M.H., A.W., D.W. ([PERSON]), A.D. and A.V. All authors have read and agreed to the published version of the manuscript.  
This research was partly funded by German Ministry of Education and Research (BMBF) under grant 16 KIS1184 (FunKI).  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Mid-Flexion Instability in Total Knee Arthroplasty: Insights from Robotic-Assisted Surgery
## 3 Definition, Mechanism, and Patient Dissatisfaction of MFI  
[PERSON] and [PERSON] introduced the MFI concept in 1990 [11] when the authors described an increased laxity during mid-flexion in ten cadaveric knees. MFI represents a distinct clinical phenomenon, characterized by stability at full extension and 90\({}^{\circ}\) flexion but instability during mid-range flexion, occurring between 0\({}^{\circ}\) and less than 90\({}^{\circ}\)[9; 10; 11]. In detail, MFI was identified by laxity in the coronal plane (varus/valgus) between 30\({}^{\circ}\) and 60\({}^{\circ}\) of knee flexion; it should be underlined that losses in the sagittal (anterior/posterior) and axial (rotational) planes may also occur within this mid-flexion range [9; 11]. As recently reported by [PERSON] et al., the term mid-flexion, as used in TKA literature, includes a variety of definitions, highlighting the lack of consensus on its precise flexion range [1].  
[PERSON] et al. reported that a conventional gap balancing with equal flexion and extension gaps would result in approximately 2-4 mm residual laxity in mid-flexion despite a balanced knee in flexion and extension [16]. This scenario may be related to soft tissues, particularly medial collateral ligament (MCL) anatomy; they are tightest at full extension. However, as the knee flexes, its insertion distance gradually approximates, reducing MCL load and increasing laxity [16] (Figure 1).  
MFI is a cause of patient dissatisfaction, as reported in the literature [4; 17]. Patient satisfaction is an essential outcome following TKA, and it is well known that there is a significant difference between patient satisfaction and expectations and surgeon clinical outcomes evaluation [4; 17]. For this reason, the new 2011 Knee Society Knee Scoring System (KSS 2011) includes patients' perspectives [4; 17; 18]. Mid-flexion medial laxity has been associated with worse expectations because, during daily living activities, the knee is utilized in mid-flexion and not in full extension [4; 17]. Nonetheless, as reported by [PERSON] et al., MFI assessed intraoperatively is not always symptomatic postoperatively [9].
# Thallium Use, Toxicity, and Detoxification Therapy: An Overview
## 2 Thallium Chemistry and Properties  
Thallium (Tl; atomic number 81) is located between mercury and lead in the periodic table of elements and belongs to the metals of main Group 13 (IIIA, or boron group). Tl is a bluish-white heavy metal, and is ductile, malleable, shiny, with a density of 11.85 g/cm\({}^{3}\) (Table 1). Thallium was discovered in 1861 by the English chemist [PERSON] who observed a bright-green line never seen before in the spectrum of a sample of selenium, used in the production of sulfuric acid. The next year (1862), independently of each other, the English [PERSON] and the French [PERSON] isolated thallium. Its atomic weight (204.38) derives from a mixture of the two more stable isotopes: \({}^{203}\)Tl (29.5%) and \({}^{205}\)Tl (70.5%). Thallium has 41 isotopes with atomic masses ranging from 176 to 216. \({}^{204}\)Tl is the most stable radioisotope with a half-life of 3.78 years; it emits beta particles, forming \({}^{204}\)Pb. \({}^{201}\)Tl has a half-life of 72.9 h and decays by electron capture (EC) in \({}^{201}\)Hg. Thallium concentrations in the Earth's crust range between 0.1 mg/kg and 1.7 mg/kg [29], and it is found in crookesite selenium-containing mineral [(Cu-Tl-Ag)\({}_{2}\)Se], in lorandite sulfur-containing mineral (TIAS\({}_{2}\)) and in Hutchinson sulfur-containing mineral [(TIP)\({}_{2}\)As\({}_{5}\)S\({}_{9}\)]. It may also be found in iron, lead, cadmium, and zinc ones, from which it is obtained during refining and smelting processes [1]. Areas with high thallium concentrations are Rotokawa (New Zealand), Lengenbach (Switzerland), Guizhou Province (China) and Kavadarci (Macedonia). Thallium naturally accumulates in the environment where human activity is present, such as in the copper mines of El Loa (Chile). In this region, the concentration of thallium in potatoes and beans is very high (about 6800 \(\upmu\)g/kg) [30]. In oxidation state I (Tl\({}^{+}\), thallous cation), thallium is more stable with respect to Tl\({}^{3+}\) (thallic cation, oxidation state III). All thallium compounds are highly toxic. Thallium forms univalent thallous (Tl\({}^{+}\); chlorine, bromine, fluorine, and iodine) salts and trivalent thallic (Tl\({}^{3+}\)) salts, which are water-soluble and highly toxic. When exposed to air, this metal oxidizes, forming a thin Tl(I) oxide (Tl\({}_{2}\)O) coating at its surface, whereas at higher temperature it forms Tl(III) oxide (Tl\({}_{2}\)O\({}_{3}\)).
## 6 Simulation Results and Analysis
Figure 10 shows that when the maximum Doppler shift was 8 kHz, the BER of the proposed method reached \(10^{-3}\), for the best performance. The BER of the RBOP method was \(5.5\times 10^{-3}\) due to the fact that RBOP method has higher channel requirements and cannot effectively suppress multipath interference, hence resulting in impacted system performance. The REHS method had a BER of \(10^{-2}\) and required heavy computation and processing, which reduced the performance of the system. The BER of the ZF-HBF method was \(5.5\times 10^{-2}\). The Hy-BD method had the highest BER, of \(10^{-1}\), and performed the worst. Figure 8: Comparison of the spectral efficiency of the proposed method with other methods. Figure 9: Comparison of the proposed method with other methods for BER.
# Special Issue: Quality of Service in Wireless Sensor/Actuator Networks and Systems  
Mario Alves  
Politecnico do Porto, School of Engineering (ISEP/IPP), [PERSON], 431, 4249-015 Porto, Portugal, [EMAIL_ADDRESS]; Tel.: 22-834-0500  
23 February 2018 February 201  
Reliability and robustness: improving communication errors detection/correction, hardware robustness, and systems reliability in general.
* Timeliness and real-time: improving the timing behavior and reducing/bounding (end-to-end) communication delays; innovative time synchronization techniques.
* Security and privacy: new mechanisms to grant adequate levels of security/privacy without jeopardizing energy and time.
* Mobility: mechanisms to support mobile devices in a seamless and transparent way, while still respecting overall QoS requirements.
* Energy sustainability, efficiency, and harvesting: improving devices/system lifetime, e.g., through optimized communications scheduling/duty-cycling and energy/delay trade-offs.
* Radio interference identification and mitigation: improving the detection, classification, and mitigation of communication errors deriving from radio propagation and interference.
* Communication and network protocols: QoS add-ons; performance/worst-case analysis (analytical, simulation, and experimental).
* QoS in the Internet-of-Things, Cyber-Physical Systems, and Industry 4.0 contexts.
* Experimental facilities and test-beds, pilot demonstrations/deployments; innovative simulation and emulation models, platforms, and methodologies.
* Real-world applications, such as in smart health, environmental/structural monitoring, factory automation, process control, smart buildings, body sensor networks, vehicular networks, or security/surveillance.
* Communication standards and technologies for WSAN and LPWAN, e.g., IEEE 802.11, WiFi, IEEE 802.15.4, ZigBee, 6 loWPAN, WirelessHART, ISA SP100, LoRa, SigFox, and Narrowband-IoT and their integration/interoperability with wired networks and with the Internet/Cloud.
* Novel communication technologies to overcome an increasingly overcrowded radio spectrum (e.g., visible light, mm-wave, thermal, vibration, acoustic) communication.