esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Aspects
### Noise Power on the Output \(y_{H}(t)\) and on Functions \(g(t)\) and \(h(t)\)
According to the swept sine identification procedure, the kernel \(h_{k}(t)\) is obtained through Equation (7), i.e., through a weighted combination of \(g_{i}(t)\) functions, where the \(i-th\) function \(g_{i}(t)\) is associated with the \(i-th\) harmonic of the input signal, and thus with a frequency range from \(i*f_{MIN}\) to \(i*f_{MAX}\). The combination matrix present in Equation (7) is an upper triangular matrix, and thus the functions \(g_{i}(t)\) that contribute to build the \(k-th\) kernel \(h_{k}(t)\) are those with indices ranging from \(k\) itself to the value \(N_{H}\) i.e., to the maximum order of the Hammerstein model. Thus, according to Equation (7), to obtain the functions \(h_{k}(t)\) (that we know are defined in the frequency band limited to \(k*f_{MAX}\)), we linearly combine the functions \(g_{i}(t)\) whose frequency band is wider than that of the \(h_{k}(t)\) we are seeking. The frequency range of the combination of the \(g_{i}(t)\) needed to obtain \(h_{k}(t)\) has an overall harmonic content ranging from \(k*f_{MIN}\) to \(N_{H}*f_{MAX}\), so it extends to frequencies much higher than those we know characterize the \(h_{k}(t)\) function we want to identify. This implies that if we lowpass filter the linear combination of the \(g_{i}(t)\) functions contributing to \(h_{k}(t)\), and limit the contributions to the useful band of each \(h_{k}(t)\), i.e., to \(k*f_{MAX}\), we can sensitively reduce the noise effects without altering the quality of the identification result. This filtering operation has to be carried out in a frequency band whose range depends on the order of the function \(h_{k}(t)\) to be identified, so for each kernel \(h_{k}(t)\) an ad hoc lowpass filter will have to be defined.
# Effect of Harmonics on Ferroresonance in Low Voltage Power Factor Correction System--A Case Study
## 3 System Description
### Transformer Station  
The low voltage side of the transformer station can be supplied by up to three identical transformers (T1, T2, T3) which are always connected in parallel. The nominal values of the transformers are given in Table 1 in Appendix A. Depending on the load, the low voltage side is always supplied by two or three transformers. An automatic detuned PFC system is connected to the low voltage side of each transformer. Linear and nonlinear loads are connected to the low voltage busbar. The majority of the linear and nonlinear loads are compressor drives. Fixed speed compressor drives are linear loads and variable speed compressor drives are nonlinear loads. All variable speed compressor drives have a frequency converter with a three-phase diode bridge rectifier as the front-end topology and input reactor. Therefore, all variable speed drives inject the allowable values of current harmonics into the power system. Fixed speed compressor drives connected to the low voltage busbar range from 200 kW to 650 kW, variable speed compressor drives range from 45 kW to 200 kW, and fixed speed compressor drives connected to the medium voltage busbar range from 600 kW to 700 kW. The total connected load to the low voltage busbar is 3.3 MW, of which 1.1 MW are nonlinear loads, 1.6 MW are linear loads and 0.6 MW are other small loads whose harmonic injection can be neglected. On the medium voltage side, only linear loads (medium voltage induction motors with individual compensation) are connected. The schematic diagram of the transformer station with measurement points marked in red is shown in Figure 6. Only measurement points are marked whose results are given in Section 4.  
Figure 5: Ferroresonance characteristics for chaotic oscillations.  
Each of the automatic detuned PFC systems consists of seven capacitor stages and a PFC controller that switches the stages on or off depending on settings and measurements. The stages can also be switched on and off manually. Each capacitor stage consists of associated fuses, contactor, capacitor and detuned reactor with a detuning factor of 5.67% (210 Hz series resonant frequency). The rated reactive power of each of the first five stages is 49.2 kvar and each of the remaining two stages is 24.6 kvar. Therefore, the rated reactive power of each automatic detuned PFC system is 295.2 kvar. Each PFC controller measures the current and voltage of the associated transformer and has a displacement factor set point of \(cos\varphi=0.97\). The single-line diagram of the of the automatic detuned PFC system is shown in Figure 7. The capacitor ratings are given in Table 2 and the reactor ratings are given in Tables 3 and 4.  
Figure 6: Single-line diagram of the transformer station with marked measurement points.  
Figure 7: Single-line diagram of the automatic detuned PFC system.
# Wireless Sensor Network Aided Assembly Line Monitoring According to Expectations of Industry 4.0
## 3 Results
### Laboratory Setup  
The industrial laboratory used for testing the proposed model is presented in Figure 6. It simulated the conditions of the real industrial surroundings. It consisted of an assembly line, which was set up for assembling a circulation pump. The assembly line consisted of professionally used elements, such as:  
* Assembling workstations
* Conveyor with pallets for the base assembly
* Technological systems
* Industrial BOSCH-scara robot.  
Figure 6: Industrial laboratory.  
By that, the assembly line created the surroundings that was similar to the real one.  
Within the industrial laboratory, the wireless sensor network presented in Figure 7 was set up. Having in mind the specific characteristics of the assembling line, the proposed RSSI map for the system had to take into consideration the layout of the system. Fixed anchors were set-up too, which are presented as red dots.
###### Abstract  
The participating nodes in Wireless Sensor Networks (WSNs) are usually resource-constrained in terms of energy consumption, storage capacity, computational capability, and communication range. Energy is one of the major constraints which requires an efficient mechanism that takes into account the energy consumption of nodes to prolong the network lifetime. Particularly in the large scale heterogeneous WSNs, this challenge becomes more critical due to high data collection rate and increased number of transmissions. To this end, clustering is one of the most popular mechanisms which is being used to minimize the energy consumption of nodes and prolong the lifetime of the network. In this paper, therefore, we propose a robust clustering mechanism for energy optimization in heterogeneous WSNs. In the proposed scheme, nodes declare themselves as cluster head (CH) based on available resources such as residual energy, available storage and computational capability. The proposed scheme employs the multi criteria decision making technique named as Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) which allows the child nodes to select the optimal CH among several potential CH candidates. Moreover, we also propose mechanisms such as CH-acquaintanceship and CH-friendship in order to prolong the network lifetime. Simulation results show that our proposed scheme minimizes the control overhead, reduces the power consumption and enhances overall lifetime of the network by comparing with the most recent and relevant proposed protocol for WSNs.  
clustering; energy consumption; MAC; TOPSIS; WSNs; internet of things; network lifetime; load-balancing +
Footnote †: journal:  
_Article_
# Encoder-Decoder Structure Fusing Depth Information for Outdoor Semantic Segmentation
## 3 Experimentation
### Brief Overview of Depth Estimation Methods  
The depth estimation methods used in this paper were derived from our previous research results [25; 26]. We mainly used two different schemes to train the depth estimationmodel. The first scheme uses a supervised learning approach, and since the training dataset contains RGB images and corresponding depth images, we proposed an encoder-decoder structure with the feature pyramid to predict the depth map from a single RGB image [25]. The second scheme uses an unsupervised learning method. Since the training dataset only contains rectified stereo pairs without corresponding depth images, we considered the depth prediction problem as a regression problem of the disparity map based on the basic principle of binocular stereo vision [26].
## 2 Materials and Methods
### Calculation of Faradatic Efficiency  
The efficiency values reported in this work are based on the statistical average of at least three biological replicates. When the biocathodes operated at \(-\)0.6 V (vs. Ag/AgCl, saturated NaCl) showed steady or repeatable current, the biocathodes were operated at fixed constant potential to measure the biomass and succinic acid production, and the faradaic efficiency. To measure the total biomass of the planktonic cells, optical density (OD) was corrected to biomass for _R. sphaeroides_ to determine the dry weight. Cells attached to the biocathodes were determined by a gravimetric method for each biocathode. The production of succinic acid was measured by a high-performance liquid chromatography (Agilent 1260, Agilent Technologies, Santa Clara, CA, USA) equipped with an HPX-87 H column. The faradaic efficiency (\(\upeta\)) was calculated according to the following Equation (1) [14]:  
\[\upeta=(mnF)/(\int_{t=0}^{t}Idt)\ \times\ 100 \tag{1}\]  
where \(m\) denotes the mole of products (mol), \(n\) denotes the number of electrons transferred for the production of 1 mole of products (succinic acid), \(F\) denotes the faradaic constant (96,485 C mol\({}^{-1}\)), \(I\) denotes the current (A), and \(t\) denotes the time (s).  
Biomass formation:  
\[\begin{array}{c}\text{CO}_{2}+0.095(\text{NH}_{4})_{2}\text{SO}_{4}+4.42\text{H} ^{+}+4.42\text{e}^{-}\rightarrow\\ \text{CH}_{1.99}\text{O}_{0.50}\text{N}_{0.19}+0.095\text{H}_{2}\text{SO}_{4}+ 1.5\text{H}_{2}\text{O}\end{array} \tag{2}\]  
Succinic acid formation:  
\[4\text{CO}_{2}+6\text{H}_{2}\text{O}+3.5\text{e}^{-}\rightarrow\text{C}_{4} \text{H}_{6}\text{O}_{4}+3.5\text{O}_{2}+3\text{H}_{2}\text{O} \tag{3}\]
## 5 Finite Element Analysis
### Force Transfer Pattern  
The load transmitted from the beam end is mainly transferred to the end-plate through the upper and lower flange of the beam. The end-plate is bent, and the end-plate continues to transfer the load to the bolt. For ordinary CFST column joints, the bolt directly transmits the load to the steel tube wall, resulting in obvious outward deformation of the steel tube wall. However, for anchored blind-bolted joints to the CFST column, due to the extended bolt shank into the concrete and the mechanical anchor at the end, the load transfer pattern is obviously different. Taking the FEA model of specimen SJ-3 as an example, the internal stress distributions in the concrete in the elastic stage, yield stage and ultimate stage are shown in Figure 28. For the anchored blind bolt in the tension zone, obvious stress concentrations occur around the expansion sleeve and around the anchor nut at the end, indicating that the tension is effectively transferred to the whole bolt shank until reaching the anchor nut. For the anchored blind bolt in the compression zone, due to the support of the CFST column, the stress concentration only appears around the expansion sleeve.  
In the tensile zone, due to the anchoring effect of the blind bolt, the force transmission zone is approximately a cone with the anchor nut as the vertex, which transmits the load to the steel tube wall in the form of a circle. The cone angle is approximately 60\({}^{\circ}\). The bolt force is mainly undertaken by the concrete in the cone zone and the upper steel tube wall. The circular stress area of the steel tube wall is much larger than that of the common joint without an anchored bolt, and the stress is more dispersed, which decreases the outward deformation of the steel tube wall. After defining the load transmission pattern and stress distribution in anchored blind-bolted joints to CFST columns, it is helpful to propose a calculation model for the stiffness and flexural resistance for such joints.  
Figure 27: Predicted weakest component deformation. (**a**) Outward deformation of the column flange in specimen SJ-2; (**b**) Flexural deformation of the thick end-plate in specimen SJ-3; (**c**) Fractured bolt in specimen SJ-4 (bending and shearing fracture); (**d**) Local buckling of the steel beam in specimen SJ-6.
# Investigation of Static and Kinematic Surveying Performance of Handheld GNSS Receiver +
## 2 Materials & Methods  
### Static Field Measurement  
Static GNSS measurements were made for 24 h at a control point (CP-1) established on the roof of the Civil Engineering Faculty in Istanbul Technical University (ITU) Campus, Istanbul, Turkiye, between 08:45 on 18 September 2023 (GPS Day: 261) and 08:45 on 19 September 2023 (GPS Day: 262). In the measurements, the Garmin GPSMAP 66 sr handheld receiver was used (Figure 1a,b). It was introduced in 2020 as the first dual-frequency multi-constellation handheld GNSS receiver. The receiver has a quad helix antenna, which plays a vital role in reducing the multipath effect by creating a circularly polarized hemispherical radiation pattern [16]. It has a BCM47758 chipset and can track the L1 and L5 GPS (G) signals; L1 GLONASS (R) signals; E1 and E5a Galileo (E) signals. It is also capable of tracking QZSS and IRNSS signals [18]. During static measurements, GNSS raw data was recorded in RINEX v3.04 format at a 1-s measurement interval from all available GPS, GLONASS, and Galileo satellites. There is an issue that is worth emphasizing here since only GPS and GLONASS observations were collected at the reference stations used in the relative solution, only GPS (L1 and L5) and GLONASS (L1) satellite observations were used during the process in the static scenario. The tracked GNSS number of satellites and Position Dilution of Precision (PDOP) values are given in Figure 1c,d. Site-dependent multipath is one of the sources of GNSS errors that affect both code and carrier-phase measurements. Studies have revealed that there is a strong correlation between multipath and Signal-to-Noise Ratio (SNR) [19]. Therefore, multipath also affects the SNR of the measurements. In this context, SNR on L1 and L5 signal values, which express the quality of GNSS signals, were calculated and plotted in Figure 1e as a skyplot. In addition, multipath values affecting the measurements were also calculated and presented in Figure 1f as a skyplot. The SNR and multipath plots were generated using RTKLIB and in-house software.
## 2 Materials and Methods
### Methods
#### 2.2.7 Volatile Compounds  
Qualitative analyses of the volatile compounds were achieved using \"in-tube extraction\" coupled with gas-chromatography mass spectrometry (ITEX/GC-MS, Shimadzu GC-MS model QP-2010) [45]. Three grams of each sample were weighed into a 20 mL sealed-cap headspace vial and incubated for 30 min at 70 \({}^{\circ}\)C with continuous agitation. The volatile compounds from the gaseous headspace of the sample were repeatedly adsorbed onto a microtrap (ITEX-2 TrapTXTA, TA 80/100 mesh, Tenax, Zwingen, Switzerland) usinga CombiPAL AOC-5000 autosampler (CTC Analytics, Zwingen, Switzerland). The volatile compounds were further thermally desorbed into the injection port of the GC-MS QP2010 system and separated on a ZB--5 ms column of 30 m \(\times\) 0.25 mm i.d. and 0.25 um film thickness (Phenomenex, Torrance, CA, USA). The column oven temperature program was set initially at 40 \({}^{\circ}\)C (kept at this temperature for 6 min) and then increased to 50 \({}^{\circ}\)C at 2 \({}^{\circ}\)C/min, followed by an increase to 240 \({}^{\circ}\)C at 7 \({}^{\circ}\)C/min (kept at this temperature for 5 min). The temperatures of the ion source, injector, and interface were set at 250 \({}^{\circ}\)C. The carrier gas was helium at a flow rate of 1 mL/min. The split ratio was 1:2. The MS detector was operated in the EI mode, using a scan acquisition mode in the range of 40-400 _m/z_. Mass spectra was identified by comparison with a series of standard alkanes--alkane standard solution.  
The identification of volatile compounds was performed by comparing their mass spectra with those in the NIST27 and NIST147 libraries and by retention indices drawn from [46; 47] (for columns with a similar stationary phase to ZB-5 ms). Afterwards, the mass spectra values were compared and verified with the retention indices drawn from Pherobase [46] and Flavornet [47] databases. The results were expressed as relative percentage of the total peaks area.
## 2 Materials and Methods  
### Materials  
The suspension used for this study was prepared using powders produced by Treibacher Industrie AG (Althofen, Austria). The two powders used for making the mixed suspension were: (a) Al\({}_{2}\)O\({}_{3}\) (refer Figure 2a) with d10 = 0.51 \(\mu\)m, d50 = 2.20 \(\mu\)m, and d90 = 4.93 \(\mu\)m and (b) YSZ (8 wt.% Y\({}_{2}\)O\({}_{3}\)-ZrO\({}_{2}\), see Figure 2b) with d10 = 230 nm, d50 = 440 nm, d90 = 950 nm. Here, d10, d50, and d90 respectively indicate values of particle diameters below which 10, 50, and 90 vol.% of particles lie in the powders constituting the mixed suspension. The mixed suspension was manufactured by combining individual water-based suspensions of Al\({}_{2}\)O\({}_{3}\) and YSZ in the required amounts to yield a solid mass ratio of 80% Al\({}_{2}\)O\({}_{3}\) to 20% YSZ. The final solid content was then adjusted to 40 wt.%.  
Domex 355 steel in the form of buttons (25 mm diameter and 6 mm thickness) was used as a substrate to facilitate coating characterization and erosion studies. Rectangular plates (60 \(\times\) 20 \(\times\) 6 mm) of the same substrate material were used for generating specimens for scratch and wear studies.  
Figure 2: Morphologies of (**a**) Al\({}_{2}\)O\({}_{3}\) (reproduced with permission from [32], Elsevier, 2017), and (**b**) YSZ powder constituting the mixed suspension.
## 4 Result and Discussion
### Measurement Errors and Methods to Reduce them
#### 4.4.2 The Effect of Atmospheric Refraction  
For the observation of the sun, we have to know the solar position without the effect of atmospheric refraction. However, the observed radiation of the sun is refracted during its propagation through the atmosphere. Under normal conditions, the vertical gradient of the refraction causes the beam to bend downward [18]. During the observation, the refraction has to be taken into account, especially at the low elevation. The true solar elevation \(el_{t}\), apparent elevation \(el_{a}\) and refraction \(\delta_{r}\) are related by  
\[el_{t}=el_{a}-\delta_{r}, \tag{14}\]  
The refraction is a function of the elevation and depends on the relative humidity. [PERSON] and [PERSON] studied the atmospheric refraction model and this atmospheric refractive model is accurate enough for the requirement of antenna calibration [29]. Assuming a horizontally stratified atmosphere, we can calibrate the refraction with their calculation model and the result is shown in Figure 11. The effect of atmospheric refraction on the radiative transfer process is shown as well. The refraction curves have been calculated with different elevation for the U.S. Standard Atmosphere, 1976, that is, for an ambient temperature of 25 \({}^{\circ}\)C, a relative humidity of 85% and a surface pressure of 1013.25 hPa. The results show that radiation is influenced by atmospheric refraction, especially for antenna pointing with a lower elevation, and the influence becomes weaker with the increasing of antenna elevation.
## 4 Experiments and Results  
### TRIDEC Drilling Support Components  
The TRIDEC drilling support system (TDS) monitors the drilling operations performed at drilling rigs. It is scheduled to detect and show trends for critical situations in real time [1]. The system is designed to be used both onsite and offsite at a rig at a real-time operating center (RTOC); see Figure 7. It also guides the drillers during routine operations and presents counter-actions and recommendations for abnormal situations. The RTOC is dedicated to be used by a special stakeholder, the so-called RTOC engineer. That RTOC engineer monitors multiple rigs at the same time and is therefore typically located at a remote operating center.  
Such an operation center is connected to multiple rigs. The RTOC engineer is provided with an overview of all monitored rigs, enabling the user to get a clear view of the overall situation. It is possible to switch to a more detailed view in order to monitor a single rig on demand. With this system, the RTOC engineer analyzes long-running trends, giving learning feedback to the system and responding to any provided counter-actions and recommendations.  
Figure 7: TRIDEC Drilling Support Components (DSC). The different color line in the left block is the plan of the drill in the layers of the soil.  
The following subsections briefly introduce three of the drilling support components relevant to the scope of this paper.
###### Abstract  
Potholes pose a significant problem for road safety and infrastructure. They can cause damage to vehicles and present a risk to pedestrians and cyclists. The ability to detect potholes in real time and with a high level of accuracy, especially under different lighting conditions, is crucial for the safety of road transport participants and the timely repair of these hazards. With the increasing availability of cameras on vehicles and smartphones, there is a growing interest in using computer vision techniques for this task. Convolutional neural networks (CNNs) have shown great potential for object detection tasks, including pothole detection. This study provides an overview of computer vision algorithms used for pothole detection. Experimental results are then used to evaluate the performance of the latest CNN-based models for pothole detection in different real-world road conditions, including rain, sunset, evening, and night, as well as clean conditions. The models evaluated in this study include both conventional and the newest architectures from the region-based CNN (R-CNN) and You Only Look Once (YOLO) families. The YOLO models demonstrated a faster detection response and higher accuracy in detecting potholes under clear, rain, sunset, and evening conditions. R-CNN models, on the other hand, performed better in the worse-visibility conditions at night. This study provides valuable insights into the performance of different CNN models for pothole detection in real road conditions and may assist in the selection of the most appropriate model for a specific application.  
pothole detection; adverse conditions; R-CNN; YOLO; object detection