esa-sceva/satcom-chunk-collection huggingface dataset:
# Charting Past, Present, and Future Research in the Semantic Web and Interoperability
## 6 Discussion, Limitations, and Future Research
We identified that the research focus areas revolve around eight major thematic clusters: (1) the Internet of Things, (2) the conceptualization of the semantic web, (3) semantic web services, (4) ontology mapping, (5) building information modeling, (6) bioinformatics, (7) education and e-learning, and (8) semantic web languages. The co-citation analysis revealed that the most influential papers concentrate on the vision of the Internet of Things achieving seamless interaction between physical objects and information systems through interoperability. Additionally, studies focus on the semantic web's vision, ensuring that machines can discover, combine, and act on web-based information. It is not surprising that our analysis showed a strong focus on semantic web and interoperability research in the area of the Internet of Things. With a wide array of sensors, and strong communications infrastructures, it is possible to gather numeric, text, and video-based data in large volumes relatively cheaply. This offers businesses and other organizations the possibility of greater insights into areas such as business performance and customer experience. However, this vision is difficult to realize, often because of the semantic and interoperability challenges between different sensors and systems discussed in this paper, which is why researchers have strongly focused on this topic. Despite the contribution of this study, several limitations exist. In contrast to [25], which used several data sources, we used a single database. As a result, in comparison to other databases such as Web of Science (WoS) and Google Scholar, citations and relatedness between publications will be more conservative. As a result, future studies may include additional databases to ascertain the validity of our findings. Nonetheless, it is worth noting that the vast majority of publications indexed in databases such as WoS and Scopus are dual-indexed in these two databases. Second, we limited our search to publications written in English. As a result, the scope of coverage may be reduced; consequently, future studies could consider publications in other languages, particularly the languages of the most productive countries (Germany and Italy), in order to assess the generalizability of this study's findings across languages. Third, the study's search query may have missed potentially relevant studies. Researchers may wish to extend the search strings to include a broader list of keywords related to the semantic web and interoperability to address this shortcoming. Finally, the bibliographic coupling is somewhat static and retrospective, as it is centered on the cited publications. As a result, additional bibliometric tools such as document co-citation analysis are required to study the topic in a forward-looking manner.
## 3 Application and Comparison of Various Classification Methods
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Attack Cases** & **Scenarios** & **Duration (hr)** & **Period (YYYY:MM,DD)** & **Attack Descriptions** \\  & & & & **HH: mm)** & **Attack Descriptions** \\ \hline
1 & Scenario 2 & 50 & 2016.09.13 & Attacker changes L,T7 thresholds controlling PU10 and PU11 by altering SCADA transmission to PLCS. \\  & & & 2300-2016.09.16. 16:00 & This causes low levels in T7. \\
2 & Scenario 2 & 24 & 2016.09.26 & Attacker changes L,T7 thresholds controlling PU10 and PU11 by altering SCADA transmission to PLCS. \\  & & & 1100-2016.09.27. 10:00 & This causes low levels in T7. \\  & & & & Attacker alters L,T1 readings arriving to PLC2 with a constant low level. PLCL receives the manipulated readings from PLC2 and keeps Pumps PU1 and PU2 on, driving T1 to overflow. \\  & & & & Attacker alters L,T1 readings arriving to PLC2 with a constant low level. PLCL receives the manipulated a constant low level. PLCL receives the manipulated readings from PLC2 and keeps Pumps PU1 and PU2 on, driving T1 to overflow. \\  & & & 2016.11.26 & Working speed of PU7 reduced to 0.9 of nominal speed causes lower water levels in T4. \\
6 & Scenario 1 & 94 & 07.00-2016.12.04 & Working speed of PU7 reduced to 0.7 of nominal speed causes lower water levels in T4. \\  & & & 2016.12.14 & Working speed of PU7 reduced to 0.7 of nominal speed causes lower water levels in T4. \\  & & & 2017.01.16 & Working speed of PU7 reduced to 0.7 of nominal speed causes lower water levels in T4. \\  & & & 2017.01.19. 06.00 & Working speed of PU7 reduced to 0.7 of nominal speed causes lower water levels in T4. \\
9 & Scenario 3 & 65 & 08.00–2017.02.02. 17:00 & Special cases lower water levels in T4. \\  & & & 2017.02.09 & Attacker alters L,T2 readings arriving to PLC3, which reads a constant low level and forces Valve V2 open, leading T2 to overflow. \\  & & & & \\
10 & Scenario 5 & 31 & 2017.02.09 & Malicious activation of Pump PU3. \\  & & & 03.00–2017.02.12 & 09.00 & Malicious activation of Pump PU3. \\
11 & Scenario 5 & 31 & 01.00–2017.02.12 & Malicious activation of Pump PU3. \\  & & & 01.00–2017.02.17.07 & Malicious activation of Pump PU3. \\  & & & 2017.02.24 & Attacker alters L,T2 readings arriving to PLC3, which reads a constant low level and forces Valve V2 open, leading T2 to overflow. \\  & & & & Attacker changes L,T7 thresholds controlling PU10 and PU11 by gaining control of PLC5, causing the pumps to switch continuously. \\
13 & Scenario 6 & 80 & 14.00–2017.03.13. 21:00 & and PU11 by gaining control of PLC5, causing the pumps to switch continuously. \\  & & & 2017.03.25 & \\
14 & Scenario 4 & 30 & 20.00–2017.03.27. 01:00 & Alteration of T4 signal arriving at PLC6. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Attack specifications in training and test input data.
# An Experimental Study on Alkali Silica Reaction of Concrete Specim Using Steel Slag as Aggregate
## 2 Experimental Procedure
### Evaluation of Expansion Properties of the Steel Slag
#### 2.3.2 Experimental Setup for the Alkali-Silica Reaction Expansion of Steel Slag  
Several studies have been performed to develop accelerated test procedures to determine the potential alkali-silica reactivity of aggregate [20]. The accelerated alkali-silica reaction test method using mortar bars was originally proposed by [PERSON] and [PERSON] in 1986 [21; 22]. It is the most widely used method to evaluate the potential alkali-silica reactivity of aggregate [17; 20; 21]. Mortar bars with a water-binder (i.e., W/B) ratio of 0.47 and a sand-binder (i.e., S/B) ratio of 2.25 were fabricated. The particle size distribution of the aggregate was in conformance with the ASTM C 1260 standard [23]. The grading curves of the aggregates are shown in Figure 1.  
The standard test was performed by immersing the mortar bars in 1N NaOH solution at 80 \({}^{\circ}\)C. The prism specimens were prepared according to the ASTM C 1260 standard [23], with dimensions of 25 mm \(\times\) 25 mm \(\times\) 285 mm. The expansion rate due to ASR was determined from the average expansion value of three specimens by using a calibrated digital indicator (resolution: 0.001 mm). The test variables are listed in Table 3.  
Figure 1: Grading curves of aggregate.
###### Abstract
Following the orbit optimization problems, the methodology to derive the engineering system requirements is presented: the paper also addresses the mission-enabling technology (i.e., propulsion) and corresponding instrument performances, the accelerometer selection for the selected set of orbits and the drag compensation solution and its versatility in order to address the entwined impact of different levels of drag compensation designs and related orbit altitudes on the mission performance.
# Highly Adaptive Reconfigurable Receiver Front-End for 5G and Satellite Applications
## 5 HARRF LNA Design
### X-Band LNA Design Procedure
#### 5.1.1 Stage-by-Stage Design Considerations  
* **Stage one:** The first stage consists of a transistor of size \(2\times 75\,\mathrm{\SIUnitSymbolMicro m}\), using inductive source feedback for stabilisation. This feedback helps bring the optimum noise match \(\Gamma_{\mathrm{opt}}\) close to the gain match, thereby reducing the overall noise figure. The input source impedance is \(50\,\mathrm{\SIUnitSymbolOhm}\), and the input matching network was implemented using an LC combination (series inductor and capacitor).
* **Stage two:** The second stage also employs a transistor of size \(2\times 75\,\mathrm{\SIUnitSymbolMicro m}\). An inter-stage matching network is used between stages one and two to transform the output impedance of stage one to the input impedance of stage two. The goal is to provide the maximum stable gain and linearity while maintaining stability.
* **Stage three:** The final stage uses a transistor of size \(2\times 100\,\mathrm{\SIUnitSymbolMicro m}\). The output of stage two is matched to the input of stage three to maximise the overall gain. A parallel feedback network is introduced in this stage to achieve gain flatness and to boost output power handling. Figure 8 shows the full schematic of the three-stage X-band LNA. From Figure 8, the following key observations can be made:
* Capacitors C1 and C10 act as DC blocks, preventing DC voltage from flowing along the RF path.
* Capacitors C2, C4, C5, C7, C8, and C9 are bypass or decoupling capacitors connected to the bias lines to reduce the coupling of RF signals into the power supply.
* Resistors R1, R3, and R5 are high-value resistors used to isolate RF signals from the DC supply.
* The input matching network for the first stage is formed by C1 and L1, ensuring that the amplifier sees \(\Gamma_{\mathrm{opt}}\) at the transistor's gate when terminated in \(50\,\mathrm{\SIUnitSymbolOhm}\).  
Figure 7: Three-stage X-Band LNA structure ([15]).  
* The output matching network is composed of L6 and C10, allowing for proper load matching.
## 2 Materials and Methods
### Data Collection and Model Calibration
#### 2.3.3 Analytical Methods  
The water and organic matter content in the soil and sediment samples were determined with gravimetry [42] through drying the samples at 105 \({}^{\circ}\)C for 24 h and subsequent ignition at 550 \({}^{\circ}\)C for 2 h. Volatile solids were determined according to the Standard Methods [43]. Soil texture was evaluated by separating the relative proportions of sand, silt and clay using grading sieves. Nitrate, sulfate and phosphate concentrations were determined using spectrophotometric measurement kits (HACH-LANGE, Germany): LCK 339, range 1-60 mg/L for nitrate; LCK 153, range 40-150 mg/L for sulfate; LCK 349, range 0.15-4.50 mg/L for phosphate. DR2800 spectrophotometer (HACH-LANGE, Dusseldorf, Germany) was used in these analyses. The pH was measured with an inoLab(r) pH 7110 bench meter (WTW, Weilheim, Germany).  
Hexadecane was quantified in solid and liquid phases. Quantification in solid phase was performed by extracting the hydrocarbon with a mixture of hexane and acetone (1:1) in closed Schott flasks at room temperature for 4 h at 180 rpm [44]. Hexadecane in liquid samples was sequentially extracted three times with hexane, using separatory funnels [45]. All the extracts were cleaned using Sep-Pak Florisil(r) cartridges (Waters, Milford, MA, USA) and evaporated in TurboVap(r) LV (Biotage, Uppsala, Sweden). Hexadecane concentration was quantified by gas chromatography (GC) in a Varian 4000 GC/MS equipped with a BRUKER BR-1 ms column. The injector temperature was set at 250 \({}^{\circ}\)C. The temperature of the oven started at 60 \({}^{\circ}\)C for 1 min, and then increased up to 170 \({}^{\circ}\)C through a temperature ramp of 8 \({}^{\circ}\)C/min, holding then at 170 \({}^{\circ}\)C for 1 min. Airflow rate was 1 mL/min and the flame ionization detector (FID) temperature was 315 \({}^{\circ}\)C [46].
## 2 Materials and Methods
### Sodium Dodecyl Sulfate-Polyacrylamide Gel Electrophoresis and Western Blot Analysis  
Proteins were extracted from whole cells by lysis in radioimmunoprecipitation assay (RIPA) lysis buffer (Sigma-Aldrich, St. Louis, MO, USA) containing complete protease inhibitors. The total protein concentration was determined using a bicinchoninic acid (BCA) assay kit (Thermo Fisher, Waltham, MA, USA). The total protein (20 \(\upmu\)g) was subjected to sodium dodecyl sulfate-polyacrylamideamide gel electrophoresis (SDS-PAGE) on a 4% stacking gel for 20 min at 80 V and an 8-12% separating gel for 90 min at 110 V. The proteins were transferred to polyvinylidene fluoride (PVDF) membranes (Millipore, St. Louis, MO, USA) using a Trans-Blot Turbo transfer system (Bio-Rad Laboratories Inc., Hercules, CA, USA) for 150 min at 200 mA. The membranes were blocked with blocking buffer that contained 5% skimmed milk in tris-buffered saline (TBS)-T buffer (10 mM HCl, pH 7.5, 100 mM NaCl, and 0.1% Tween-20) for 1.5 h at room temperature and then incubated with primary antibodies in 5% skimmed milk overnight at 4 \({}^{\circ}\)C. The experiment was repeated 5 times under the same conditions. The primary antibodies were as follows: anti-protein-disulfide isomerase (PDI; 1:500, Cell Signaling Technology, MA, USA), anti-C/EBP-homologous protein (CHOP; 1: 1000, Novus, CO, USA), anti-activating transcription factor 6 (ATF6; 1:500, Abcam, Cambridge, UK), anti-tumor growth factor-\(\beta\) (TGF-\(\beta\); 1:500, Abcam, Cambridge, UK), anti-protein kinase RNA (PKR)-like endoplasmic reticulum kinase (PERK; 1:1000, Cell Signaling Technology, MA, USA), anti-glucose-regulated protein 78 (GRP78; 1:1000, Abcam, Cambridge, UK), anti-Janus kinase 2 (JAK2; 1:1000, Abcam, Cambridge, UK), anti-caspase-3 (1:1000, Santa Cruz, TX, USA), and anti-\(\beta\)-actin (1:1000, Abcam, Cambridge, UK). Then, the membranes were incubated with horseradish peroxidase (HRP)-conjugated secondary antibodies at room temperature for 1 h. Proteins were detected using chemiluminescence reagents (Thermo Fisher, Waltham, MA, USA), and the band densities were quantified with a ChemiDoc XRS system (Bio-Rad Laboratories Inc., Hercules, CA, USA).
# Adaptive Impedance-Based Fault Location Algorithm for Active Distribution Networks
## 2 Proposed Generalized Fault Location Equations  
This work proposes an Adaptive Impedance-Based fault location algorithm for active distribution networks. The proposed method is an extension of the mathematical formulation presented in [11]. Ref. [11] considers typical features of distribution systems for its formulation, such as the unbalanced operation; capacitive effect; single, two-phase, and three-phase laterals; making it a robust fault location method. However, it does not consider the presence of DER.  
Consider the power distribution system illustrated in Figure 1. This system has a fault between nodes \(k\) and \(k+1\).  
The main objective of a fault location formulation is to estimate the distance \(d\) from the substation up to the fault point. Therefore, considering that the fault section is unknown, all sections of the system are assumed in fault and a distance \(x\) is estimated for each section. This estimation is made until a convergence criterion is achieved. For this reason, the fault location analysis is reduced to a particular line section.  
Consider now a distribution line under fault, which is represented by its exact line segment model, as shown in Figure 2. Different types of faults can be obtained from the combination of switches \(s1,\ s2,\ s3\), and \(s4\).
# Business Intelligence through Machine Learning from Satellite Remote Sensing Data
## 2 Satellite Systems in Remote Sensing
### Overview of Major Satellite Missions and Their Derived RS Products
**Airbus Defense and Space** have launched more than fifty satellites, such as TerraSAR-X NG, featuring the X-band radar sensor, an instrument that allows the acquisition of images with different resolutions, swath widths, and polarizations, offering geometric accuracy unmatched by other space-borne sensors. Another constellation of satellites is the **Pleiades**, which has found successful remote sensing applications in the fields of cartography, geological prospecting, agriculture, and civil protection. Pleiades features the High-Resolution Imager, which delivers very high optical resolutions of 0.5 m, making it an ideal data source for civil and military projects. Data collected from satellite missions can be distributed raw or processed at various levels. Depending on the speed at which they become available to users, they can be split into different categories. Different data providers use different names for these categories. In the context of this paper, we use the terminology proposed by NASA. Since the terminology regarding **data latency** varies between the various Earth science data providers (e.g., NASA, NOAA, etc. ), in the context of this paper, we use the one provided by NASA ([[https://earthdata.nasa.gov/learn/backgrounders/data-latency](https://earthdata.nasa.gov/learn/backgrounders/data-latency)]([https://earthdata.nasa.gov/learn/backgrounders/data-latency](https://earthdata.nasa.gov/learn/backgrounders/data-latency)), accessed on 1 September 2023). One of the key differences between standard data products and Near-Real-Time (NRT) is that the latter makes use of predictive orbit information for geolocation. Furthermore, the NRT processing algorithm can use ancillary data from other sources whose accuracy may vary. The standard products, on the other hand, are processed utilizing precise geolocation and instrument calibration, and as a result, they offer a reliable, internally consistent record of Earth's geophysical characteristics that can aid a scientific investigation. Even though NRT products may include information that makes their analysis harder, they can be very important in various applications.
# A Global-Local Blur Disentangling Network for Dynamic Scene Deblurring
## 4 Experimental Results and Discussion
### Ablation Experiments  
To verify the effectiveness of our proposed dual-branch module and branch attention module, we conducted 3 ablation experiments. First, the local blur feature extraction branch network is used as the baseline, which is referred to Local-Branch-Net (LB-Net). Then, a global blur feature branch network is added to the baseline, which is refer to Dual-Branches-Net (DB-Net). Finally, the branch attention network is added to the DB-Net, and the method we propose is called Dual-Branch Attention Fusion Net (DBAF-Net). We use the direct addition method to fuse the features extracted by the two parallel branches of DB-Net. Table 1 shows the average Peak Signal to Noise Ratio (PSNR) results of the 3 models on the GoPro dataset.  
**Effectiveness of dual-branch modules.** Comparing the objective experimental results of the LB-Net and the DB-Net, we can see that the PSNR of the LB-Net is increased from 31.07 dB to 31.77 dB, a gain of 0.7 dB is achieved. It shows that the global and local blur feature extracted by the dual-branch network complemented each other to enhance restoration results. However, the simply addition of the two features in the baseline does not take full advantage of the dual-branch features.  
**Effectiveness of the branch attention module.** Comparing the objective experimental results of the DB-Net and the DBAF-Net, we can see that the PSNR of the DB-Net has increased from 31.77 dB to 32.27 dB, namely 0.5 dB's gain. It shows that the branch attention module can effectively integrate dual-branch features.
# A Study on \(H_{\infty}\)-Fuzzy Controller for a Non-Linear Wind Turbine with Uncertainty
## 3 Control Algorithms
### Desired Set Points  
The proposed multiple-input \(H_{\infty}\) controller had previously been applied to determine the desired set points of each feedback signal. We used set points of a generator torque schedule compared to the generator speed of the wind turbine in order to track the maximum power point, and employed set points of the generator speed using a reference bias control technique to reduce transience in the rated wind speed region [23]. In addition, a peak shaving schedule was used in comparison to the estimated wind speed to obtain set points of the blade pitch by employing a wind speed estimator, and '0' was used as the desired value related to the load.  
The wind speed estimator estimates wind speed through two procedures [26]. First, the aerodynamic torque is estimated using [PERSON]'s laws of motion for the driving heat model, and it can be assessed by measuring the rotor speed and the generator torque. The estimated aerodynamic torque \(\hat{T}_{a}\) can be expressed as:  
\[\hat{T}_{a}=J_{total}\hat{\Omega}_{r}+NT_{g}+T_{Loss} \tag{3}\]  
where \(J_{total}\) is the total moment of inertia of the rotor, generator, hub, and shaft, \(\Omega_{r}\) is the low-pass-filtered rotor speed, \(N\) is the gear ratio, and \(T_{g}\) and \(T_{Loss}\) are the generator torque and mechanical loss torque, respectively. Second, a function minimization method was used to inversely obtain wind speed from power coefficient data. The function minimization algorithm was calculated using the MATLAB (R2023a, The MathWorks, Inc., Natick, MA, USA)'s fminsearch function, and a non-linear wind speed function was implemented in the form of a 3D look-up table in advance. The 3D look-up table determines the current wind speed from the estimated aerodynamic torque, measured rotor speed, and measured blade pitch angle. The aerodynamic torque \(T_{a}\) for determining the estimated wind speed can be expressed as:  
\[T_{a}=\frac{1}{2}\rho\pi R^{3}\Bigg{\{}\frac{C_{P}\big{(}\hat{\lambda},\beta \big{)}}{\hat{\lambda}}\Bigg{\}}\hat{\sigma}^{2} \tag{4}\]  
where \(\rho\) represents air density, \(R\) indicates rotor radius, \(C_{P}\) represents the power coefficient, \(\beta\) represents the blade pitch angle, \(\hat{\lambda}(=R\Omega_{r}/\hat{\sigma})\) represents the tip speed ratio by the estimated wind speed, and \(\hat{\sigma}\) represents the estimated wind speed.  
Figure 1: Frequency response function of torque and pitch loop gain at different wind speeds. The left-hand plot (**a**) shows the results in generator speed output for blade pitch angle control input. The right-hand plot (**b**) shows the results in generator speed output for generator torque control input.
# Energy-Efficient User Pairing for Downlink NOMA in Massive MIMO Networks
## 4 Proposed User Pairing Solutions  
Users are divided into two main groups. The first group represents cell-center users of high channel gains, and the second group represents cell-edge users of low channel gains.  
The pairing algorithm selects one cell-center user with one cell-edge user to form a NOMA group that guarantees maximum EE in NOMA based scheme.
## 2 Problem Statement and Equations of Motion
,\ \delta=\frac{3}{2}J_{2}\mu_{g}R_{E}^{2},\ J_{2}=1082.23\cdot 10^{-6} \tag{8}\]  
\(R_{E}\) is the Earth's average radius, and \(\mathbf{f}_{i}^{\text{zero}}\) is the acceleration vector due to the aerodynamic force, described by the following formula [28]:  
\[\mathbf{f}_{i}^{\text{zero}}=-\frac{1}{m_{i}}\rho(\mathbf{R}_{i})V_{i}^{2}S_{ i}\Big{[}(1-\varepsilon)(\mathbf{e}_{V},\mathbf{n}_{i})\mathbf{e}_{V}+2 \varepsilon(\mathbf{e}_{V},\mathbf{n}_{i})^{2}\mathbf{n}_{i}+(1-\varepsilon) \eta(\mathbf{e}_{V},\mathbf{n}_{i})\mathbf{n}_{i}\Big{]}, \tag{9}\]  
where \(m_{i}\) is satellite mass, \(\rho(\mathbf{R}_{i})\) is the atmospheric density at the orbital position \(\mathbf{R}_{i}\), \(V_{i}\) is the magnitude of satellite's velocity relative to the incoming airflow, \(S_{i}\) is the area of the reflector, \(\mathbf{e}_{V}\) is the unit vector along the velocity, \(\mathbf{n}_{i}\) is the unit vector of the normal to the reflector surface, and \(\varepsilon\) and \(\eta\) are parameters of interaction of molecules with the surface, which are estimated by [PERSON] in [28]; in LEO, their values are \(\varepsilon\approx 0.1\), \(\eta\approx 0.1\). In aerodynamic force model (9), the first term is the aerodynamic drag, and the second and the third terms describe the lift force. Due to the lift component of the force, which is 10 times lower than the value of drag force, it is possible to control the relative motion in the out-of-orbital plane to achieve the required projected circular relative trajectories.