esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Variable Step Size Incremental Conductance Method with an Adaptive Scaling Factor
###### Abstract  
In this paper, a novel variable step size (VSS) incremental conductance (INC) method with an adaptive scaling factor is proposed. The proposed technique utilizes the model-based state estimation method to calculate the irradiance level and then determine an appropriate scaling factor accordingly to enhance the capability of maximum power point tracking (MPPT). The fast and accurate tracking can be achieved by the presented method without the need for extra irradiance and temperature sensors. Only the voltage-and-current sets of any two operating points on the characteristic curve are needed to estimate the irradiance level. By choosing a proper scaling factor, the performance of the conventional VSS INC method can be improved. To validate the studied algorithm, a 600 W prototyping circuit is constructed and the performances are demonstrated experimentally. Compared to conventional VSS INC methods under the tested conditions, the tracking time is shortened by 31.8%. The tracking accuracy is also improved by 2.1% and 3.5%, respectively. Besides, tracking energy loss is reduced by 43.9% and 29.9%, respectively.  
variable step size MPPT algorithm; maximum power point (MPP) tracking (MPPT); modified incremental conductance method +
Footnote †: journal: Applied Sciences
# Improving Prostate Image Segmentation Based on Equilibrium Optimizer and Cross-Entropy
## 5 Experiments
### Evaluating Image Quality  
Image quality after processing can be assessed through both objective and subjective methods. Objective evaluations include numerical metrics, either with or without a reference image, where the reference is often referred to as ground truth. While having a ground truth is ideal in many cases, creating annotated datasets is time-intensive and can sometimes introduce subjectivity. To address this limitation, many methods employ no-reference quality metrics such as _PSNR_, _SSIM_, and _FSIM_. In MRI, a high _PSNR_ indicates that critical anatomical details are preserved, which is crucial since noise can obscure important structures. A high _SSIM_ value ensure that important clinical features remain intact after processing. Given the importance of features such as tissue boundaries in MRI images, a high _FSIM_ value indicates that essential diagnostic details have been retained.
## Materials and Methods
### Hydrocarbon Degradation Analysis  
The ability of the ZCR6 strain to degrade crude oil was studied in 0.1 LB (Luria Bertani) broth. Replicate flasks containing 500 mL of medium supplemented with crude oil at a 1% concentration (\(v/v\)) were prepared. The flasks were inoculated with 5 mL of one-week-old liquid culture of the ZCR6 strain. Non-inoculated flasks were prepared as controls. The cultures were grown aerobically at 28 \({}^{\circ}\)C for 4 weeks with constant shaking (120 rpm). To determine the amount of hydrocarbon degraded, the initial concentration of hydrocarbons and the residual hydrocarbon concentration after 4 weeks of incubation were determined.  
The mineral oil content was estimated according to PN-EN ISO 9377-2. The quantitative determination of mineral oil (hydrocarbon) content after extraction was estimated by gas chromatography. The 0.1 LB medium with crude oil was extracted by mechanical shaking with an acetone/\(n\)-heptane mixture for 1 h at 120 rpm. The organic layer was separated and washed twice with water to remove acetone. The remaining \(n\)-heptane extract was dried using anhydrous Na\({}_{2}\)SO\({}_{4}\). Polar compounds were removed by adsorption on Florisil(r)/Na\({}_{2}\)SO\({}_{4}\) columns. An aliquot of the purified extract was analyzed by GC-FID. The total peak area in the range C\({}_{10}\)-C\({}_{40}\) was measured, and the amount of hydrocarbons in the sample was quantified against an external standard.  
The total petroleum hydrocarbon (TPH) content was analyzed using a gas chromatograph (Agilent 7820A) equipped with a flame ionization detector and an Rxi-5 ms capillary column (25 m \(\times\) 0.2 mm ID \(\times\) 0.33 \(\upmu\)m); the injection volume was 1 \(\upmu\)L, and hydrogen was used as the carrier gas (gas flow 2.1 mL minute\({}^{-1}\)). The operation program was started with injector and detector temperatures of 300 \({}^{\circ}\)C. The oven temperature was initially programmed at 60 \({}^{\circ}\)C, held for 10 min, increased to 320 \({}^{\circ}\)C at 30 \({}^{\circ}\)C minute\({}^{-1}\), and then held for 10 min.
# Open-Source MQTT-Based End-to-End IoT System for Smart City Scenarios
## 1 Introduction
Among different messaging protocols for data exchange in IoT systems, we envisage the use of the Message Queue Telemetry Transport (MQTT) [7] protocol between the Android device and the intended users that require monitoring the road user's behavior. Similarly, the use of an Android device as an intermediate point between the wearable device and the rest of the network can be considered a viable option since we can rely on its presence with any user in an urban environment. Experiments on MQTT application protocol to support data acquisition and transfer in the cloud has been performed in the past [8] using a Linux operating system in a fixed workstation. Here, a mobile scenario is considered where the Android device acts as a publisher with the role of sending data acquired by the external sensor and internal sensors. The subscriber is here implemented through a dashboard deployed in the customer premises. In order to deploy the MQTT communication flow, a broker has also been implemented by resorting to a Raspberry PI node. Proper wireless communication links have been considered for allowing the interconnection between the publisher(s), the broker and the subscriber, even if any broadband and narrow-band solution can be used. The main novelties of the paper can be summarized in:  
* A perfectly functional PoC of an E2E system for message delivering in a Smart City scenario has been released;
* The PoC is composed of COTS technologies; thanks to this, their integration allows having a low-cost, deployable solution for different Smart City applications;* The PoC is based on open-source technologies, allowing its expansion with other components and services through simple open interfaces by any interested user;
* The integration of an Android-based app into the architecture allows the system to be potentially used by a large number of people, leading to a widely used echo-system;
* The modularity of the architectural design, along-with the open-source approach of the different components, allows a rapid evolution of the system toward other potential application scenarios. The paper is structured in such a way. In Section 2, we enforce the rationale behind our solution by analyzing the most important solutions proposed up to now in practical implementations and by conducting a literature survey. Then, in Section 3, the high-level description of the architecture is presented, focusing on the main functional aspects of each node. Following this, in Section 4, the description of the PoC is given by sticking around the technological characteristics of each element considered in the system. Finally, some feasibility results are provided, showing the effectiveness of the proposed solution, in Section 5, and a final discussion is conducted in Section 6.
# Bridgeless Buck-Boost PFC Rectifier with Positive Output Voltage
## 2 Overall System Configuration  
Figure 1 displays the overall system configuration for the proposed circuit. The main power stage contains four high-switching-frequency diodes, \(D_{1}\), \(D_{2}\), \(D_{3}\), and \(D_{4}\), three high-switching-frequency switches, \(S_{1}\), \(S_{2}\) and \(S_{3}\) with individual gate drivers, one output diode \(D_{o}\), one output capacitor \(C_{o}\), and one inductor \(L\). Regarding the control stage, it contains one voltage divider, one analogue-to-digital converter, named \(ADC\), and one field programmable gate array, named \(FPGA\). The output resistor is signified by \(R\). Note that the switch \(S_{3}\) is utilized to make the output voltage positive.  
The output voltage is sensed from the main power stage and passed to the voltage divider and then to the ADC to generate a digital value. Afterwards, such a digital value is passed to the controller embedded in the FPGA to yield three proper PWM signals for \(S_{1}\), \(S_{2}\) and \(S_{3}\). Note that the voltage-follower control is employed herein. This is because if the main power stage operates in the DCM, the corresponding power factor is inherently high [18].  
In addition, the proportional-integral (PI) controller embedded in the FPGA can make the proposed converter operated stably. The parameters of this controller are obtained by the industrial try-and-error tuning method as follows.  
Figure 1: Overall system configuration for the proposed circuit.  
* Under the input nominal voltage of 110 Vrms and rated output power, the integral gain \(k_{i}\) is first set to zero, and then the proportional gain \(k_{p}\) is gradually increased, so that the value of \(k_{p}\) stops being increased until the output voltage reaches 75% of the desired value.
* Under the same conditions, the value of \(k_{p}\) obtained from step 1 is fixed, and then the value of \(k_{i}\) is gradually increased, so that the output voltage is stabilized at the desired value without oscillation.
* Under the input nominal voltage of 110 Vrms but different output powers, the values of \(k_{p}\) and \(k_{i}\) are finely tuned, so that the output voltage is stabilized at the desired value for all the output power range.
* Change the input voltage levels, and repeat step 3, so that the output voltage is stabilized at the desired value for all the input voltage range and all the output power range.
# Design and Analysis on Decoupling Techniques for MIMO Wireless Systems in 5G Applications
## 3 Evaluation Parameters of Mutual Coupling
### Envelope Correlation Coefficient (ECC)
It is usually considered that MIMO systems with \(ECC\) below 0.5 can run normally. When calculating \(ECC\), the above equations can be flexibly selected.
# AUV Adaptive Sampling Methods: A Review
## 3 Target of Interest
### Physical Features
#### 3.1.3 Internal Waves  
The continuous change in isotherm depths, especially near a thermocline, can be a proxy to estimate the amplitude of internal waves [21]. [PERSON] et al. [22] introduced a mingled-spectrum principle to identify internal waves in this manner. Unlike Lagrangian and Eulerian approaches, this method enables an AUV to distinguish ocean phenomena by exploiting temporal and spatial information. A key proxy to detect these ocean processes is the vertical flow velocity. A significant occurrence of ocean convection, but no internal wave signatures, were found during these experiments in the Labrador Sea in 1998.  
[PERSON] [23] presented a temperature bounds method to detect internal waves in South Monterey Bay. The AUV remained in the temperature bounds, as opposed to the depth bound, so that the vehicle could keep track of an isotherm. The vehicle was programmed to follow the thermocline by transecting between two waypoints in the horizontal plane while performing a back-and-forth sawtooth pattern in the vertical plane. The AUV heading was perpendicular to the isobaths, which was assumed to be the likely propagating direction of any internal waves.  
[PERSON] and [PERSON] [21] furthered [PERSON]'s work [23]; they introduced a track-and-trail method for AUVs to determine the presence of internal wave signatures. The vehicles were adaptively redirected in accordance with their local environment in the region of the Tyrrhenian Sea, Italy, during the GLINT10\({}^{\prime}\) experiment [7]. Three behaviour modes were programmed: an adaptive yo-yo behaviour, an adaptive constant depth behaviour and a track-and-trail mode. Since the depths of the thermocline continued to change along the horizontal plane, the vehicle performed a yo-yo (sawtooth) behaviour to match the desired depth in real-time by autonomously adapting and re-setting new upper and lower bounds accordingly. Utilising a single vehicle or multiple vehicles, thermoclines were used as a proxy and made use of CTD sensors, to define the amplitudes of internal waves.
# LESS: Link Estimation with Sparse Sampling in Intertidal WSNs
## 2 Related Work
### Marine WSNs  
Marine WSNs are utilized in various applications including water quality monitoring [28], marine creatures monitoring, and ocean current monitoring. The networks structures, hardware design and communication technologies are different across applications. In general, a marine WSN contains a base station and multiple sensor nodes. Nodes can send data to the base station directly or in a multi-hop way. To sustain the requirements of diverse scenarios, sensor nodes may be equipped with temperature sensor, pressure sensor, etc. Many communication technologies are utilized in marine WSNs, e.g., acoustic communication, GSM, and ZigBee. Acoustic communication in [PERSON] et al. [29] is typically used for underwater scenario and cannot be utilized in intertidal areas. SEMAT [30] transmits packets via GSM and sensors are connected with a surface gateway (a buoy). According to the standards of GSM and ZigBee, the battery life of sensors with ZigBee is much longer than that in GSM [31]. Thus, ZigBee is more suitable for wireless sensor networks in intertidal areas. The latest communication technologies, such as LoRa [32] and NB-IoT [33], are also promising to be applied in future marine WSNs.
## 2 Materials and Methods  
### Laser Processing Workstation  
A linearly polarised, 450 fs, \(\lambda\) = 1030 nm, laser source delivering a maximum average power P = 310 W with maximum repetition rate RR = 13 MHz (Tangor, by Amplitude Laser, Pessac, France) was utilised to generate nanometric laser-induced periodic surface structures (LIPSS) on the surface of a 200 \(\upmu\)m thick, 304 stainless-steel coil (see Figure 1). The coil width was 40 cm. Estimations of the surface roughness value S\({}_{\text{a}}\) were obtained by means of the software ConfoMap ST 8.2 after surface profile acquisitions carried out with a \(\times\)50 optical profilometer (Smart Proof 5, by Zeiss, Gottingen, Grmany). The laser beam was magnified by a factor of 2, then deflected by a hybrid polygonal galvanometric scanning head (UHSS-II-15, by Raylase, Wessling, Germany) and focused through an F-theta lens with focal length f = 650 mm. This system enables scanning speeds as high as 360 m/s with a scanning field \(\geq\)350 mm. The coil is automatically unwrapped through a motorised rotator, then pulled for nearly 1.5 m through a second rotator. Before reeling, the coil passes beneath the laser beam. The web tension can be adjusted between 10 MPa and 100 MPa, and the line speed v\({}_{l}\) can vary between 3 mm/s and 20 mm/s. Amongst the metallic sheets (Al, Ti, Mg alloys, etc.) which are commonly subjected to forming [12], we chose stainless steel since it has been widely studied in USP laser nanotexturing [13]. The chosen thickness value was the maximum compatible with our set-up. Handling of thicker coils will require the implementation of more powerful motorised rotators.
# A Review of the Influence of Steel Furnace Slag Type on the Properties of Cementitious Composites
## 3 SFS as a Cementitious Material
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Property**} & \multicolumn{3}{c}{**EAF Slag**} & \multicolumn{3}{c}{**BOF and LD Slag**} & \multicolumn{3}{c}{**LMF Slag**} \\ \cline{2-9}  & **Increase** & **Decrease** & **No Change** & **Increase** & **Decrease** & **No Change** & **Increase** & **Decrease** & **No Change** \\ \hline Compressive strength & [176, 177, 178] & [179] & [180] & [126] & [40, 41, 181, 182, 183] & & & \\ \hline Split tensile strength & [179] & & & & & & \\ \hline Flexural strength & [179] & & & & & & \\ \hline Modulus of elasticity & [176] & & & & & & \\ \hline Shrinkage & [176, 178] & & & & & & \\ \hline Abrasion resistance & [177] & & & & & & \\ \hline Water Absorption & & & & & & & \\ \hline Freeze/thaw resistance & & & & & & & \\ \hline \multirow{2}{*}{**Property**} & \multicolumn{3}{c}{**AOD Slag and Other Stainless Steel Slags**} & \multicolumn{3}{c}{**Other Steel Slags or Unspecified**} \\ \cline{2-9}  & **Increase** & **Decrease** & **No Change** & **Increase** & **Decrease** & **No Change** \\ \hline Compressive strength & [45, 46, 47] & & [184, 185, 186, 187, 188, 189, 190, 191] & [192, 193] & [194] \\ \hline Split tensile strength & & & & [186, 188] & [192] & & \\ \hline Flexural strength & & & & [186, 187, 189, 190] & [192] & & \\ \hline Modulus of elasticity & & & [186] & [192] & & \\ \hline Shrinkage & & & & [190] & & \\ \hline Abrasion resistance & & & [193] & & & \\ \hline Water absorption & & & [190, 193] & [186] & & \\ \hline Freeze/thaw resistance & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Effect of SPS used as a partial to full replacement of virgin coarse and/or fine aggregate in geopolymer or alkali-activated mortars or concretes relative to the control with virgin aggregates.
## 6 NDT Techniques Applied for Material's Degradation Assessment
### Tangential Magnetic Field  
Magnetic materials do generate magnetic stray fields, which depend on the magnetic field induced as well as on various material inherent phenomena including cracking, martensitic transformation, plasticity and possibly others, which can be considered even at a microscopic scale. In the case of austenitic steels, different magnetics-based measurements have been used in fatigue tests conducted at RT, to characterize the \(\alpha^{\prime}\)-martensite volume fraction. Such tests were monitored with a self-developed tangential magnetic field sensor, based on a Hall element positioned perpendicular to the specimen's longitudinal axis to allow changes in the tangential magnetic field around the specimen to be monitored. Thermocouples have been added to this monitoring device to compensate for the influences of temperature changes as well as a permanent ring magnet, to magnetize the specimen in the longitudinal direction. This sensor is able to detect changes in the tangential magnetic field, which mainly results from the material \(\alpha^{\prime}\)-martensite transformation.  
Three strain controlled CATs with total strain amplitudes \(\varepsilon_{a,t}\) of 0.25%, 0.5% and 0.7% are presented as an example in Figure 3, where the red line represents the controlled total strain amplitude \(\varepsilon_{a,t}\), the blue the resulting stress amplitude \(\sigma_{a}\), the brown line the plastic strain amplitude \(\varepsilon_{a,p}\) and the orange the change in tangential magnetic field \(\Delta M_{tang}\).  
As expected, when looking at the change in tangential magnetic field, it can be seen that the higher \(\varepsilon_{a,t}\) becomes, the higher the change in \(\Delta M_{tang}\) will be during the test, and the shorter the incubation period is. These changes in the tangential magnetic field occur due to the increase in the \(\alpha^{\prime}\)-martensite volume fraction.
# Event Detection and Spatio-temporal Analysis of Low-Altitude Unstable Approach
## 2 Methodology
### Methods and Models
#### 2.2.1 Unstable Approach Detection  
More than 20 experienced pilots were surveyed, and flight parameters such as the airspeed, descent rate, configuration, heading, glide path, and slope were considered for the detection of UA events; these parameters are interpreted and presented in Tables 1 and 2, respectively. All the parameter thresholds were iteratively verified with empirical data collected from 10,000 flights via the analysis ground station (AGS) platform. When the data satisfied one of the conditions, it was empirically recognized as a suspicious UA event. In particular, a radio height under 200 feet was chosen in this study, as UA events occurring under this condition could be highly risky and cause a real accident.  
According to the rules in Table 2, all the UA events could be preliminarily detected via the empirical rules. In this study, QAR data from 10,000 flights were utilized, of which UA events were found to occur in 5000. We then distributed them randomly in the questionnaire for the pilots to mark their risks. As an output, a score ranging from 0 to 1 was provided for each suspicious UA event. A score close to 1 means a high risk potentially caused by UA, and a score equal to 1 indicates a UA accident.
## 4 Design of an Optimization Framework for the IMS
### Actions Queue Index  
Application requests are classified into a set of priority classes (W, D, OP1, OP2, and OP3, described in Section 3) on the basis of their relevance regarding safety, mobility, and the impact on the level of driver cognitive load [34]. Every one of these three dimensions is ranked by its magnitude (from 'low' to 'high') given a-priori statically [36]. Hence, their combination can be interpreted as an indicator for the base priority of an application request. For example, the priority of a turn indicator's request is described by:  
* safety relevance = high;
* mobility relevance = medium;
* workload impact = medium.  
A comparison between the base priority of several possible application requests allows us to determine their relative weights on the actions queue \(\big{(}p_{\text{App. }i}\text{, with }i\in\{1,\ \ldots,\ m\}\big{)}\). More specifically, each action weight represents the relative effect on waiting queue derived by not executing the action and queuing it. Through these, the actions queue index can be calculated as a weighted sum of the application requests' \(\big{(}\text{App. }i\text{, with }i\in\{1,\ \ldots,\ m\}\big{)}\) contributions to the queue:  
\[J_{Aq}=\sum_{i=1}^{m}\big{(}p_{\text{App. }i}\cdot cos(x_{i})\cdot\text{App. }i\big{)}+\big{[}\text{LQue}\cdot\big{(}p_{Q,\ \text{App. }i}\cdot cos(x_{i})\cdot\text{App. }i\big{)}\big{]} \tag{3}\]  
Moreover, when a long waiting queue is assessed (\(\text{LQue}=1\)), to each application request is associated a multiplier \(p_{Q,\ \text{App. }i}\), with \(i\in\{1,\ \ldots,\ m\}\), that increases the cost of not executing the corresponding action.
## 3 Case Study
### Analysis of the Cost Deviation Factor Results  
In order to investigate the influence of deviation factors on the optimization cost and uncertainty, the economy, robustness, and generality of the system studied in this paper are considered. For a typical spring day, different \(\beta(\beta\) = 0.01, 0.02, 0.03, 0.04, 0.05) values were used for simulation analysis. From the results presented in Table 2, it can be observed that as \(\beta\) increases both the optimization cost and the uncertainty radius also increase. This can be attributed to the more pessimistic approach taken by decision makers towards the uncertainty associated with PV and load. As \(\beta\) increases, the economic performance gradually decreases while the conservatism and the ability to manage risks increase. And in the range of maximum uncertainty radius, the planning cost is not greater than \((1+\beta)C_{0}\). The development trend is consistent with the image change trend which proves the effectiveness of the IGDT robust model.  
In order to verify the universality of the model proposed in this paper, this paper chooses to simulate the influence of deviation factors on the cost and uncertainty on a typical summer day. The simulation results are shown in Table 3. As shown in Table 3, as with a typical spring day, costs and uncertainty increase with the increase in the bias factor. Furthermore, the validity and versatility of the model in this paper are verified.