esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of the Reed-Solomon Algorithm as a Remote Sensing Data Fusion Tool for Land Use Studies
## 2 Methods and Materials
### Empirical Verification Case Study of Neighborhood of Legionowo Area  
The testing scene is an area covering 1326.2 sq.km, located in the central part of Poland on the Mazovian Lowland near Warsaw. In its center, between the Vistula and Narew rivers, is the city of Legionowo (54 K inhabitants in 2019). There are coniferous and deciduous forests, agricultural areas, and meadows and buildings, and this is mainly a dispersed built-up area. Agricultural areas are characterized by a high degree of fragmentation. Small fields, often below one ha, are adjacent to the grasslands. On the Narew River there is an artificial reservoir, Zalew Zegrzynski, with an area of over 30 sq.km [2]. The satellite image of the polygon is shown in the Figure 1.  
Figure 1: Testing scene of RS algorithm: (**a**) part of Landsat ETM+ satellite image (ID = LE71880232000128 EDC00); Red-Green-Blue (RGB) composition of bands (4, 5, 3). (**b**) Landsat ETM+ whole satellite image and testing scene (red)—location in Poland.  
The testing scene was chosen because the area had been studied and classified in detail twice before. The first was a visual interpretation of the images taken by the Landsat TM satellite, according to the uniform European CLC legend based on a study carried at the Institute of Geodesy and Cartography (2000, shared by the Polish Main Inspectorate of Environmental Protection) [82]. Second, a similar (but smaller) area was also the topic of a research project aimed at object-oriented classification to obtain information on land cover and land use [2] at the same time. The scene and the same year were intentionally selected and the same satellite image was used to check the usefulness of the procedure for the preparation of input data fusion. The map tile also covers an area not covered by the image in the south-west (left corner of image).  
The source was part of Landsat ETM+ scene [83]. The basic parameters of the image are available as a metadata file separately. The only correction concerned spatial resolution of panchromatic band No 9 (generalization of spatial resolution from 15 to 30 m). There were nine spectral bands. They were numbered 1 to 9 (as the \(k\) parameter for the RS algorithm). The spectral resolution was left intact.
# Internet of Drones Intrusion Detection Using Deep Learning
## 5 Simulation Results
### Performance Analysis  
Though the first set of experiments examined the accuracy of the LSTM_RNN with different sample sizes, this was done using these datasets due to a large number of records and fields. The dataset was adopted to fit the proposed framework for drones. Figure 11 shows the detection accuracy versus sample size. As shown in the figure, LSTM_RNN accuracy increased with the increase of the sample size. It became more stable with the increase in the sample size. In addition, the average accuracy was 95%, and in some cases, it reached 97%. This is a good indicator of the performance of the proposed methodology.
## 2 Use of Agricultural Residues as Low-Cost Sorbents in Colour Removal
### Optimisation of System Using Agricultural Residues
#### 2.1.2 Optimisation of Adsorption Conditions and Bio-Reactors  
A simple approach for the adsorption of textile dyes from the solution was studied by [PERSON] et al. [10]. The artificial dye-effluent was prepared by mixing two types of Clariant dyes, Solar Jade Green FFB and Solar orange 7 GLL, and the absorption of colour was studied onto particles of three commonly available agricultural residues, i.e., wheat straw, sugarcane bagasse, and barley husk. These agricultural residues particles of 600-\(\upmu\)m size were used as sorbent-material in solution at 20% concentration (dry weight, _w/v_); each substrate was tested in a separate experiment. The dye removal could be clearly seen within all substrates when the effluent contained dye concentrations in the range of 50 to 200 mg/L. Dye-adsorption on substrates was performed and calculated using two main conditions at which actual effluent is generated in textile industries. The conditions studied in the adsorption system were the effect of temperature of the solution in range of 25 to 50 \({}^{\circ}\)C and pH 6.0 to 12.0 [9].  
The bioreactors were designed by [PERSON] et al. [11] for batch and continuous dye-adsorption onto solid residues. The researchers studied the adsorption of five reactive dyes present in a synthetic textile dye effluent on particles of barley husks in process-systems designed as a static batch-mode reactor and a packed-bed reactor (CFPBR) operating with a continuous flow of effluent, with the purpose of a large-volume of water treatment. Under static batch-mode conditions, the adsorption of dyes, initial concentrations (C\({}_{0}\)), and thermodynamics were studied, whereas, in CFPBR, the effect of \(C_{0}\) and retention time (\(\tau\)) were studied by changing the packed weight and the height of packing column, which could affect the kinetics of dye adsorption. The saturation capacities were predicted by Langmuir isotherm. In such experiments, barley husks removed 8 mg dyes per g dry weight of barley husk at C\({}_{0}\) 100 mg L\({}^{-1}\) in the reactor (CFPBR), with a column flow adjusted with a liquid-residence time of 11 min. In such a set-up of the reactor, the authors achieved 90% adsorption of colour on particles of barley husk [12]. The safety of disposal of the biosorbant material after the biosorption-removal of dyes was studied, as described in the following sections.
## 3 Methods and Techniques
### Proposed Method for Lung Nodule Localization
#### 3.1.1 Pre-Processing  
The resolution of images acquired from CT scanners is 512 pixels on each side, recorded in the DICOM format. One of the objectives of this research was to detect malignant cells, which should be made simpler by using these samples that have already undergone pre-processing. The dataset given by the LIDC was used in this study. The images were stored in the LIDC using the \"digital imaging and communications in medicine\" (DICOM) standard. The procedure for acquiring data included CT scans of both benign and malignant neoplasms. Each image was first converted into a grayscale version before being rescaled. The photos were scaled up to a better resolution, after which they were utilized by the algorithm to train itself. The procedure may be divided into the following stages.
# Wastewater Treatment Using Alkali-Activated-Based Sorbents Produced from Blast Furnace Slag
## 3 Results
### Metal Capture in Mono-Ionic and Multi-Ionic Solutions  
Mono-ionic and multi-ion solutions of composition as in Table 3 were filtered on PMG1-type monoliths fixing the filtration time at 10 min. Results are reported in Figure 7a-c, where initial and captured metal ions concentrations are compared for mono-ionic and multi-ionic solutions, Figure 7a,b respectively; while capture efficiency values are plotted in Figure 7c.  
According to literature data on different solution composition [31], the monolith was able to capture practically all the metal present in the initial solution; moreover, very similar capture performances were found in both mono-ionic and multi-ionic solutions. Therefore, no effect of the solution composition is apparently present, and the solid exerts its capture capability on the ions, regardless of whether they are alone or mixed in solution. The capture capability is also insensitive to the initial pH; indeed very similar capture efficiencies, around 100%, were observed in the case of both multi-ionic and mono-ionic solutions, whose initial pH was somewhat different (Table 3). On the other hand, such a high efficiency reveals a poor selectivity; indeed, the solid is not able to capture the metal ions in a differentiated way. However, the material here proposed can be efficiently applied  
Figure 7: Results of capture tests performed on 54.7 cm\({}^{3}\) monolith using mono-ionic (**a**) and multi-ionic (**b**) solutions; a comparison of capture efficiencies for both feeds is reported in (**c**).  
to treat wastewaters in order to reach ruled EU emission limits for the heavy metal ions here considered (Table 3).
## Appendix A  
From Figure 10, the capacitor current feedback \(K_{cf}i_{cf}(z)\) and the output current \(i_{o}(z)\) to the voltage \(v_{e}(z)\) can be written as follows:  
\[\frac{K_{cf}i_{cf}(z)}{v_{e}(z)}=K_{pvm}K_{cf}G_{dc}(z)G_{T}(z) \tag{1}\]  
\[\frac{i_{o}(z)}{v_{e}(z)}=K_{pvm}K_{cf}G_{dc}(z)G_{P}(z) \tag{2}\]  
\[v_{e}(z)=[i_{o}^{*}(z)-i_{o}(z)]G_{c}(z)-K_{cf}i_{cf}(z). \tag{3}\]  
From Equations (1)-(3), the closed-loop and open-loop transfer function, respectively, can be obtained as follows:  
\[T(z)=\frac{i_{o}(z)}{i_{o}^{*}(z)}=\frac{G_{dc}(z)G_{c}(z)G_{P}(z)}{1+K_{cf}G _{dc}(z)G_{T}(z)+G_{dc}(z)G_{c}(z)G_{P}(z)} \tag{4}\]  
\[G(z)=\frac{i_{o}(z)}{i_{o}^{*}(z)-i_{o}(z)}=\frac{G_{dc}(z)G_{c}(z)G_{P}(z)}{ 1+K_{cf}G_{dc}(z)G_{T}(z)}. \tag{5}\]  
Applying z-transformation to \(G_{T}(s)\), \(G_{T}(z)\) can be obtained as follows:  
\[G_{T}(z)=Z\left[K_{pvm}H(s)G_{1}(s)\right]=K_{pvm}\sqrt{\frac{C_{f}}{L_{f}}} \frac{(z-1)\text{sin}\varphi}{z^{2}-2z\text{cos}\varphi+1}, \tag{6}\]  
where  
\[\varphi=\omega_{res}T_{sp}\text{ and }\omega_{res}=\frac{1}{\sqrt{L_{f}C_{f}}}.\]  
Commonly, because \(T_{sp}<<L_{o}/R_{o}\), \(e^{-\frac{R_{o}}{L_{o}}T_{sp}}\approx 1\). Therefore, applying z-transformation to \(G_{P}(s)\), \(G_{P}(z)\) can be obtained as follows:  
\[\begin{split}& G_{P}(z)=Z\left[K_{pvm}H(s)G_{1}(s)G_{2}(s)\right] \\ &\approx K_{pvm}\frac{-z(R_{o}F\text{cos}\varphi+L_{o}\omega_{res }F\text{sin}\varphi-R_{o}F)+R_{o}F-R_{o}F\text{cos}\varphi+L_{o}\omega_{res} F\text{sin}\varphi}{z^{2}-2z\text{cos}\varphi+1}\end{split}, \tag{7}\]  
where  
\[F=\frac{L_{f}C_{f}}{L_{o}^{2}+L_{f}C_{f}R_{o}^{2}}.\]
# Monitoring-System Development for a Bottom-Set Gillnet through Time-Domain Dynamic Simulations
###### Abstract
The monitoring system may also be applied to protect fishing gear in case of deep-water turbulences. Even though there have been experiments [17; 18] and numerical simulations [19; 20] investigating the dynamic behaviors of the gillnet, these have only focused on small sections of the gillnet. This means that there has been no publication in the open literature which has attempted to investigate not only the global behaviors of the bottom-set gillnet but also the simulations of transient responses with various scenarios of line failures, which could be applied to the development of a sensor-based monitoring system. A commercial program, OrcaFlex, was used as a base program and the entire gillnet was rigorously modeled through equivalent line elements and three degrees of freedom (DOF) buoys. Previous studies have included authors' well validated methodologies for the similar applications of fishing cages/gear under waves and currents [3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13]; thus, in this study, we adopted those procedures for dynamic analysis. Since the longitudinal length of a bottom-set gillnet can be up to several kilometers, creating an equivalent net model is essential in modeling, and the equivalent net model was completed by adjusting the projected area, wet weight, and axial stiffness. The relative-velocity-based Morison-force model, which has been suggested by previous research [3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13], was applied at the instantaneous net-position to estimate the hydrodynamic force on the entire system, which is also critical in simulating highly transient phenomena. The global performance of the bottom-set gillnet was investigated in operational and fishery-prohibition conditions. Particularly, in this study, various failure scenarios and the corresponding patterns of numerical sensors were further analyzed for the feasibility of the sensor-based monitoring system. Key monitoring parameters were identified to detect sudden failures of mooring lines and location-buoy-connection ropes. Furthermore, using real-time sensor signals, a simple monitoring algorithm was devised and demonstrated as an example of machine-based problem-detection and decision-making processes.
# Memory and Cache Contention Denial-of-Service Attack in Mobile Edge Devices
## 5 Evaluation  
### Evaluation Methodology  
For evaluations of the attack and our detection and mitigation methods, we model a mobile edge computing system in gem5 architectural simulation tool [28], as described in Section 3.1. The performance of the system is measured as IPC (instructions per cycle) of the normal program. Please note that performance of the malicious code is meaningless, as it is only used to cause DoS to the normal programs. For normal programs, we use a set of SPEC2006 workloads (selected 13 workloads) [29]. For the accuracy of the simulations, we show the results of running 1 billion instructions after we fastforward two billion instructions. The instruction count is measured based on SPEC2006 programs for fair comparisons. In other words, we only count the number of executed instructions of the normal programs, while the executed instruction count of the malicious program is ignored. For energy evaluations, we use McPAT [30] with 32 nm process technologies. In our experiments, we assume that the malicious code was already injected to the system,  
Figure 8: An example timing diagram for the instruction fetch width throttling.  
because our focus is to unveil adversarial performance and energy impacts from our DoS attack. The attacker can easily inject and execute the malicious codes using various methods, as we explained in Sections 3.1 and 3.2 [31]. The malicious codes can also be injected via downloading the malicious apps or phishing, as reported in [32].  
In this paper, we compare performance and energy across three broad cases: _spec_, _spec_+_mal_tX_, and _spec_+_mal_tX_,_fl_, where \(X\) represents the number of the malcode type (type-\(X\) in Figure 4). _spec_ represents the case, where only the normal program is executed in the system. _spec_+_mal_tX_ corresponds to the case where the normal program and malicious code are running in the system simultaneously without our detection and mitigation techniques. _spec_+_mal_tX_fl_ represents the cases where our malicious code detection and mitigation techniques are applied when running both normal and malicious programs. We have a total of nine different cases for comparisons, as we have four types of the malicious codes.  
Our detection and mitigation mechanisms are geared towards our memory and cache contention DoS attack and corresponding malicious codes. Thus, a comparison between the detection and mitigation mechanisms for different types of the attacks may result in unfair comparisons. Thus, we perform the comparison among the three cases explained above (_spec_, _spec_+_mal_tX_, and _spec_+_mal_tX_fl_).
## 3 Plasma Application to Eliminate Organic Pollutants in Water
### Pharmaceuticals
#### 3.1.1 Antibiotics  
Antibiotics are widely studied because bacteria develop resistance to them. Reports on the removal of antibiotics such as atenol [82], verapamil [83], and enalapril [84] by plasma have been published. Plasma was used both above and below the water surface to break down oxytetracycline hydrochloride (OTC) and doxycycline hydrate (DXC) for removing antibiotics from water. This allows researchers to determine which plasma discharge method eliminates antibiotics from water the fastest. Due to their wide range of antibacterial activity, accessibility, and affordability, those two pharmaceutical drugs are mostly commonly used in veterinary care. Due to their poor biodegradability, they can be found in high concentrations in groundwater, surface water, and soil [85]. According to the study results of [PERSON] et al. [86], the type of plasma produced below the water's surface in a discharge vessel with air bubbles is more effective in breaking down antibiotics than plasma produced in air above the water. Additionally, antibiotic residues in water are also decreased with longer plasma treatment times. OTC was reduced by 30% of its initial value in plasma generated above the surface of water after 90 min of exposure, whereas DXC was reduced by just 3%. The period needed for plasma below the water surface was much less; after exposure to the plasma for less than 20 min, both antibiotics practically vanished. As a result of the degradation of the antibiotics, DXC displays one degradation product along with plasma discharge, whereas OTC displays two. Recently, a setup that incorporates a cavitation tube and plasma discharge in the air has been used to treat two common antibiotics (sulfathiazole and norfloxacin) and an azo dye (methylene blue). The setup improves the barrier to gas-liquid mass transfer by increasing the mass transfer of active species generated from cold plasma discharge from gas to liquid [87]. Results show that when operating at a flow rate of 5 L/min and a volume of 500 mL for 30 min, all three pollutants with an initial concentration of 8 mg/L or lower can be degraded to >80%.
# Fast and Reliable Burst Data Transmission for Backscatter Communications
## 3 Challenge and Motivation  
In this section, we investigate the fundamental factors underlying the poor performance of backscatter communication and describe the main challenges that need to be addressed to improve the backscatter system efficiency.
## 2 Materials and Methods
### Gas Permeability Measurements  
Gas permeability measurements were performed with a mixed gas-continuous flow permeation cell designed in our laboratory (Figure 2). Pure gases (CO\({}_{2}\) or N\({}_{2}\)) (120 Ncc/min) were fed to the system with mass flow controllers (Bronkhorst Company, Ruurlo, The Netherlands). The stream was humidified (RH = 75% and 95%) using a controlled evaporating and mixing system (Bronkhorst Control Evaporator Mixer W-202A-221-K). The total pressure of the humid feed and retentate streams were controlled by a back pressure controller (Bronkhorst Company, Ruurlo, The Netherlands) and the permeate stream was collected using a helium sweep gas with a controlled flow rate (10 Ncc/min, Bronkhorst Company, Ruurlo, The Netherlands). The permeation cell was placed in a heating cabinet (Carbolite PF030-230 SN) in order to maintain a constant temperature (40 \({}^{\circ}\)C). All the tubing was heated with heater ropes in order to avoid any condensation in the rig.  
The relative humidity and the gas concentration of the streams were measured using respectively a humidity sensor (developed in-house based on the Honeywell HH4000 chip) and a mass spectrometer (Proline, AMETEK, Berwyn, PA, USA). The temperature of each stream was measured using a thermocouple (\(K\) insulated 1.5 mm thermocouple).  
The temperature, the relative humidity and the gas composition of the permeate stream were recorded until the system equilibrium was reached (typically 2-3 h). The flow rates of the retenate and permeate were measured using mass flow meters (Bronkhorst Company, Ruurlo, The Netherlands), after passing through a water trap.  
The permeability of each gas species passing through the membrane was calculated by  
\[Perm(G)=\frac{e\cdot Flow^{p}\cdot X^{p}}{A\cdot(p^{F}-p^{p})}, \tag{4}\]  
where \(e\) is the thickness of the membrane (cm), \(Flow^{p}\) the permeate flowrate (cm\({}^{3}\) (STP)/s), \(X^{p}\) the mole fraction of the gas in the permeate stream, \(A\) is the effective membrane area (cm\({}^{2}\)), \(p^{F}\) and \(p^{p}\) are the partial pressure of the gas in the feed and permeate stream respectively (cmHg).  
The ideal selectivity between two gas species \(i\) and \(j\) was calculated as the ratio of the two permeabilities.  
\[\alpha_{ij}=\frac{Perm(i)}{Perm(j)}, \tag{5}\]  
Figure 2: Mixed gas–continuous flow permeation cell.  
A fresh membrane was used for each experimental run that consisted in firstly the measurement of the dry mixture, and secondly the wet mixture at the chosen water vapor activity. Between two runs, the rig was evacuated until the humidity sensor detects no water, approximately 10 h.
## 1 Introduction  
For more than 25% of patients under the age of 70 years, knee osteoarthritis (OA) is a widespread orthopedic problem [1]. [PERSON] et al. [2] reported a predicted increase in prevalence to 40% between 2005 and 2030 for Western Europe and North America.  
High tibial osteotomy (HTO) is a well-established and often recommended surgical option for medial compartment knee OA in patients with varus malalignment [3; 4]. This surgical intervention is indicated for varus deformities of 5\({}^{\circ}\) or greater in patients with cartilage defects and who are able to defer total knee arthroplasty (TKA) [5; 6]. HTO has been shown to reduce pain, improve function and slow the progression of OA [7]. Previous research has investigated the effectiveness of using an opening-wedge HTO to evaluate cartilage quality [8; 9; 10; 11] and compared HTO and knee joint distraction as an alternative surgical treatment for OA [6; 12]. [PERSON] et al. [5] compared the survival rates (defined as the absence for need of reintervention) of patients with less than 5\({}^{\circ}\) varus deformity when treated with autologous chondrocyte implantation and additional HTO compared to those treated with ACI alone. [PERSON] et al. [13] investigated the influence of HTO on return to sport at one year postoperative using the Tegner score (primary outcome) and quality of life questionnaire (SF-36).  
Unfortunately, the effects of HTO on postural stability and regulation have received little attention. [PERSON] et al. [14] found that standing balance in patients with OA is not significantly different following HTO, but this was only assessed 12 months postoperative. [PERSON] et al. [15] evaluated the circadian rhythm of balance control in patients with OA using posturography and found reduced postural performance and pain in the morning compared to the early afternoon.  
The current study is based on the longitudinal study design of [PERSON] et al. [16] with a postoperative follow-up of six months. Four hypotheses were proved:  
1. [label=(0)]
2. Medial tibofemoral OA and HTO will induce unbalanced mediolateral weight distribution;
3. Postural stability will be reduced before and after HTO;
4. After medial tibofemoral OA/HTO and compared to healthy matched individuals the cerebellar and somatosensory subsystem will display the largest changes;
5. Pain perception and quality of life will significantly improve following HTO and rehabilitation.
## 1 Introduction
* To show how well the proposed method works by comparing simulation results on an example localization system to well-known approaches and derived CRLB. The remaining sections of the paper are structured as follows. In Section 2, relevant work on localization and the application of evolutionary algorithms to localization problems is presented. In Section 3, the general localization problem of a passive target using TODA measurements is formulated. In Section 4, the derivation of the appropriate ML estimator for the considered localization problem is presented. In Section 5, the formulation of the CWLS is outlined. Section 6 describes differential evolution algorithm with the employed improvements. The procedure of the BOA algorithm along with the improvements is given in Section 7. The derivation of ICDEBOA is proposed in Section 8. Section 9 describes the CRLB in the context of the considered localization. In Section 10, numerical experiment results are presented. In Section 11, conclusions and future work are stated. The detailed derivation of CRLB for passive TDOA-based localization is presented in Appendix A, while the detield derivation of CWLS is provided in Appendix B.