esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Efficiency Improvement in Reconfigurable Photovoltaic Systems: An Evaluation of Team Systems
## 2 Sizing Ratio in GCPV Systems
### Simulation Results  
The simulations presented in [27] were carried out for 27 European countries and have shown the existence of a single \(R_{S\,OPT}\) value per location. This optimal value can be greater or lower than one as a function of the latitude of the GCPV system location.  
\(R_{S\,OPT}\) values less than one are obtained in locations close to the equator (in low latitudes) and this implies a strategy of inverter oversizing (\(P_{INV}>P_{PVG(STC)}\)). Values greater than one for this parameter are obtained in locations with high latitudes and this suggests the use of inverter under-sizing strategy (\(P_{INV}<P_{PVG(STC)}\)).  
On the other hand, the intuited temporal dependence of \(R_{S\,OPT}\) leads to the inference that GCPV systems designed and implemented with real time adjustable sizing ratio could be more efficient, in terms of energy delivered to the grid, when compared to systems implemented with a constant value of \(R_{S}\).  
As an example, Figure 3 shows the obtained results from the \(R_{S\ OPT}\) calculation when annual and monthly periods are considered in Equation (2). These results are obtained using the simulation procedure presented in [27, 38], and previously described in Section 2.1. The simulation procedure considers a GCPV system located in Brussels (in terms of irradiance and ambient temperature), based on monocrystalline PV modules and mounted on a flat roof with good ventilation. Although only one location is considered, the pattern of evolution of the monthly value of \(R_{S\ OPT}\) is the same when locations in the northern hemisphere are considered.  
As can be appreciated in Figure 3, the value of \(R_{S}\) that maximizes the annual energy injected into the grid in non-reconfigurable GCPV systems is \(R_{S\ OPT}=1.58\). However, if the maximization of the monthly energy production is considered, the \(R_{S\ OPT}\) value obtained is different for each month. The greater values of \(R_{S\ OPT}\) (close to 2.00) occur in the months of low irradiation, while the lower values of \(R_{S\ OPT}\) (close to 1.30) appear in the months of high irradiation.  
As expected, a slight improvement in energy production is obtained when the monthly value of \(R_{S\ OPT}\) is used for the energy efficiency estimation over a year. This improvement is valued at about 30 basis points (bps).  
If the time interval used to determine the maximum value of energy efficiency is minimized, the existence of an instantaneous \(R_{S\ OPT}\) value can be deduced. This means that the \(R_{S\ OPT}\) value also changes over a day, and consequently, if the sizing ratio of a GCPV system could partially match the time evolution of \(R_{S\ OPT}\), the energy efficiency of the system would be improved. In this regard, and as noted above, the introduction of GCPV systems with dynamic adaptation of the sizing ratio appears as an interesting alternative to classical architectures based on a fixed \(R_{S}\).
# Advanced Construction of the Dynamic Matrix in Numerically Efficient Fuzzy MPC Algorithms
## 1 Introduction
### MPC Algorithms Based on Nonlinear Models
In the algorithms using the fuzzy Takagi-Sugeno model, described in [36; 39], both the free response and the dynamic matrix are obtained using the model obtained after the linearization. In the algorithms detailed in [33; 37], the (classical) free response is calculated using the nonlinear model. In [12], the advanced free response, calculated using the nonlinear model (which can have any form of the model generating outputs on the basis of input signals), takes into consideration the previously calculated trajectory of the future control signals (it can be improved iteratively if needed; the approach is similar, though slightly different in details, to the iterative prediction improvement in the iterative learning-based approaches to batch control described in [40; 41; 42]); the dynamic matrix is generated using the easy-to-obtain fuzzy model. The approach proposed in the article is an extension of the algorithms presented in [12]. It is done in such a way that the optimization problem solved by the MPC algorithm in each time step is the quadratic one. The modification is introduced in the method of dynamic matrix construction. In [12], elements in the dynamic matrix were obtained taking into consideration the current operation point. Changes of the operating point on the prediction horizon are taken into consideration in the proposed approach. This is done using the free response, generated using the nonlinear control plant model. The obtained prediction is, however, still linear with respect to control changes. Thus, the algorithms utilizing the proposed approach combine the computational simplicity of the LMPC algorithms with the control performance offered by the NMPC algorithms. The next section details the formulation of the Fuzzy MPC (FMPC) algorithms based on fuzzy and nonlinear models, with the advanced construction of the dynamic matrix. In Section 3, the operation of the FMPC algorithms exploiting the proposed advanced dynamic matrix is tested in a simulation example of the control system of a nonlinear chemical reactor with the inverse response. Conclusions are presented in the last section.
## 3 Multi-Objective Optimization Algorithms
### Network Throughput Feature  
Network throughput (\(u_{tput}\)) is the rate of successful data packets that are transmitted over the communication medium. \(u_{tput}\) is very important to determine the QoS of any network, as in case of  
Figure 2: Sampling and transmitting cycle of a sensor node [52].  
the \(u_{tput}\) increases, the network efficiency will be increased. This feature is affected by two factors, including packet payload length and packet error rate. The \(u_{tput}\) is given as follows [53]:  
\[u_{tput}=\frac{payload\cdot(1-PER)}{T_{flow}}, \tag{18}\]  
where, _payload_ is the size of data in the packet in byte, \(T_{flow}\) is the transmission latency, while the \(PER\) is the Packet Error Rate that can be calculated by the following equation [54]:  
\[PER=1-(1-BER)^{(Length-of-packet-in-bits)}, \tag{19}\]  
where, \(BER\) is the Bit Error Rate
## 4 Transformation Rules to Obtain Abstract Trace Systems  
In this section, we present a syntactic transformation algorithm, which, given a set \(\rho\) of events and an expression \(e\), transforms \(e\) into \(e^{\prime}\), where both \(e\) and \(e^{\prime}\) satisfy the same set of LTSC formulae with events occurring in \(\rho\). In general, the trace system corresponding to \(e^{\prime}\) is smaller than the one corresponding to \(e\). Our aim is two-fold: given a formula, to find a suitable set \(\rho\) and, given \(\rho\), to eliminate from an expression a suitable superset of \(\rho\). A suitable \(\rho\) depending on a formula is the following. **Occurring Events:**\(\mathcal{O}(\varphi)\).Given an LTSC formula \(\varphi\), \(\mathcal{O}(\varphi)\) is the union of all the events \(\alpha\) and the sets \(S\) appearing in the modal operators (\([\alpha]_{S}\psi\), \(\langle\alpha\rangle_{S}\psi\)) occurring in \(\varphi\). **Definition 7** (Transformation rule). : _Let \(\Sigma=(A,D)\) be a concurrent alphabet and \(\rho\subseteq A^{-}\) and \(e\) an expression over \(A^{-}\). We define \(\mathcal{T}_{\rho}(e)\) as:_  
\[\mathcal{T}_{\rho}(\alpha) = \left\{ \begin{array}{ll}nil&\alpha\
ot\in\rho\\ \alpha&\alpha\in\rho\end{array} \right.\] \[\mathcal{T}_{\rho}(e_{1}.e_{2}) = \mathcal{T}_{\rho}(e_{1}).\mathcal{T}_{\rho}(e_{2})\] \[\mathcal{T}_{\rho}(e_{1}+e_{2}) = \mathcal{T}_{\rho}(e_{1})+\mathcal{T}_{\rho}(e_{2})\] \[\mathcal{T}_{\rho}(e_{1}\|e_{2}) = \mathcal{T}_{\rho^{\prime}}(e_{1})\|\mathcal{T}_{\rho^{\prime}}(e _{2})\text{ where }\rho^{\prime}=\rho\cup(alph(e_{1})\cap alph(e_{2}))\] \[\mathcal{T}_{\rho}(rec(e)) = rec(\mathcal{T}_{\rho}(e))\]  
The previous rule maintains in the traces events belonging to a suitable superset of \(\mathcal{O}(\varphi)\): in fact, besides the occurring events of the formula, it maintains also the communication events. The complexity of the transformation operator \(\mathcal{T}\) is linear in the length of the specification. This result, together with Definition 6 of the satisfaction on traces, further reduces the complexity of the model checking of temporal logic formulae. The following theorem deals with the abstraction of a trace system induced by the formula \(\varphi\) and obtained in two steps: the first step syntactically reduces the event expression; the second one reduces the trace system, which is the semantics of the expression. Now, we extend the definition of projection to trace systems. **Definition 8**. : _Let \(TS=((A,D),T)\) be a trace system and \(B\subseteq A\);_  
\[\Pi_{B}(TS)=((B\cup\{\lfloor\;\rfloor_{x}\;\;|\;\;\lfloor_{x}\in A\},\Pi_{B}( D)),\Pi_{B}(T))\]  
_where:_  
1. \(\Pi_{B}(D)=\{(a,b)\;|\;(a,b)\in D\text{ and }a,b\in B\}\)_;_
2. \(\Pi_{B}(T)=\{\Pi_{B}(w)\;|\;w\in T\}\)_, and_ \(\forall\;\lfloor\;\rfloor_{x}\in A,x=\{\Pi_{B}(w)\;|\;w\in x\}\)_._  
**Theorem 1**. : _Consider an expression \(e\) and an LTSC formula \(\psi\)._  
\[TS(e)\models\psi\quad\text{if and only if}\quad\Pi_{\mathcal{O}(\psi)}(TS( \mathcal{T}_{\mathcal{O}(\psi)}(e)))\models\psi\]  
_Proof: see the Appendix._  
**Example 8**.
## 1 Introduction
### Practical Applications and Common Findings
Despite these challenges, the trend in the literature is toward increasingly sophisticated and validated models, often coupled with modern monitoring (e.g., UAV and high-speed cameras) for both model input collection and output verification. Many studies have reported successful field implementations--from adjusting blast designs based on model insights to deploying software that can simulate flyrock before a blast is carried out [23; 26; 35]. These advance ments suggest that AI-driven flyrock prediction is maturing from academic research into practical technology [57]. Although this study does not directly apply artificial intelligence methods for flyrock prediction, the comprehensive literature review included in the introduction aims to highlight the dynamic development of this research area and underscore the need for integrating advanced computational tools with geospatial data acquired through UAV-based surveying. The presented case study represents a step toward such integration, demonstrating the potential of precise geometric analysis as a foundation for the future development of predictive models grounded in real-world spatial data.
# Determining Factors Influencing Operating Speeds on Road Tangents
###### Abstract  
Road traffic accidents remain a critical global issue with approximately 1.19 million fatalities each year, on which excessive and inappropriate speeds contribute significantly. Managing vehicle speeds is essential for improving road safety, yet predicting and understanding operating speeds remains a challenge. Among different road elements, tangents play a crucial role, as they serve as transition segments between curves and allow for free acceleration, making them particularly relevant for speed management and road design. This study investigates the operating speeds on both single- and dual-carriageway road tangents to identify the key influencing factors. Data were collected from 24 single-carriageway and 20 dual-carriageway road tangents in Croatia, comprising a total of 14,854 speed observations (filtered sample size). The analysis focuses on the impact of geometric, traffic, and roadside environment characteristics on operating vehicle speeds. The results reveal that for single-carriageway road tangents, the most influential factors were traffic volume and terrain type, while for dual-carriageway road tangents, the factors traffic flow density, average summer daily traffic, and heavy goods vehicle share. These findings provide essential insights for the future development of operating speed prediction models, enhancing road design guidelines, and improving speed management strategies.  
operating speed; road safety; road tangents; traffic accidents +
Footnote †: journal: Applied sciences
## 4 Experiments and Discussion
### Realization and Experimental Results of LoRa Repeat Planning for Solving No Signal
Therefore, we added an additional signal repeat station between the weather station and the original signal repeat station, as shown in Figure 11, to obtain all sensing data in the web server again, as shown in Figure 12. Similarly, we evaluated the correspondence between the distance and the signal strength using three times the average of the RSSI from the LoRa-support solar micro-weather station at the mountain edge without rain in Table 5. Because of the attenuated 20% due to heavy rain, we set up a signal repeat station every 1 km along the mountain edge. Finally, Figure 13 shows the realization of multiple LoRa-support solar micro-weather stations with repeat stations and communication technology. \begin{table}
\begin{tabular}{c c c c c} \hline
**Distance** & **RSSI on** & **RSSI on** & **RSSI on** & **RSSI on** \\
**(km)** & **Mountain Slope 1** & **Mountain Slope 2** & **Mountain Slope 3** & **Mountain Slope 4** \\ \hline
0.1 & \(-64\) dBm & \(-63\) dBm & \(-62\) dBm & \(-63\) dBm \\ \hline
0.2 & \(-68\) dBm & \(-70\) dBm & \(-67\) dBm & \(-69\) dBm \\ \hline
0.3 & \(-74\) dBm & \(-75\) dBm & \(-71\) dBm & \(-72\) dBm \\ \hline
0.4 & \(-83\) dBm & \(-83\) dBm & \(-80\) dBm & \(-78\) dBm \\ \hline
0.5 & \(-104\) dBm & \(-102\) dBm & \(-103\) dBm & \(-101\) dBm \\ \hline
0.6 & \(-122\) dBm & \(-121\) dBm & \(-122\) dBm & \(-120\) dBm \\ \hline \end{tabular}
\end{table} 
Table 3: Correspondence between distance (_d_) and signal strength using the average RSSI from the LoRa-support solar micro-weather station on a mountain slope without rain. Figure 10: Realization of one LoRa-support solar micro-weather station, one repeat station, and the communication technology. Figure 11: Realization of one LoRa-support solar micro-weather station, two repeat stations, and the communication technology. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Distance** & **RSSI on** & **RSSI on** & **RSSI on** & **RSSI on** \\
**(km)** & **Mountain Slope 1** & **Mountain Slope 2** & **Mountain Slope 3** & **Mountain Slope 4** \\ \hline
0.1 & \(-77\) dBm & \(-78\) dBm & \(-76\) dBm & \(-75\) dBm \\ \hline
0.2 & \(-81\) dBm & \(-80\) dBm & \(-79\) dBm & \(-81\) dBm \\ \hline
0.3 & \(-90\) dBm & \(-89\) dBm & \(-87\) dBm & \(-89\) dBm \\ \hline
0.4 & \(-98\) dBm & \(-96\) dBm & \(-97\) dBm & \(-98\) dBm \\ \hline
0.5 & \(-118\) dBm & \(-120\) dBm & \(-121\) dBm & \(-119\) dBm \\ \hline
0.6 & \(-132\) dBm & \(-131\) dBm & \(-132\) dBm & \(-130\) dBm \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Correspondence between distance (_d_) and signal strength using the average RSSI from the LoRa-support solar micro-weather station on the mountain slope in the case of heavy rain. Figure 12: Real-time data on illuminance, atmospheric pressure, ultraviolet light, and carbon dioxide.
## 4 Results and Discussions
### Hybridization of K-Means with MOA  
From this study, it can be observed that the K-means clustering algorithm has been widely hybridized with various MOA to improve the process of data clustering. The advantages of K-means in terms of simplicity and low computational complexity have been harnessed to improve the clustering capability of many of the MOA. The ability of many of the MOA in global optimum search enhanced the performance of K-means in escaping local optimal convergence leveraging on their optimization capability. Hybridizing K-means with MOA provides a balance between exploration and exploitation in the search algorithm to improve the quality of the final cluster result. There are noticeable improvements in general clustering performance and efficiency in relation to cluster results.  
Specification of the number of clusters as a user parameter is a major challenge in cluster analysis. The various hybridization of nature-inspired meta-heuristics techniques with K-means clustering algorithms that handled automatic clustering problems were presented. From the study, it can be seen that only a few of the hybrid algorithms addressed the problem of automatic clustering. Different methods were adopted in estimating the optimal number of clusters in any given dataset. In most of the automatic hybrid algorithms, the correct number of clusters were optimized from the initial population, which were either randomly generated or deterministically selected from the data objects.  
Automatic specification of cluster number in the K-means with MOA hybrid algorithm conspicuously enhanced the performance of the former algorithm by reducing the number of iteration operations required to obtain an optimal result compared with the traditionalalgorithm. Most initialization problems associated with traditional K-means, such as user-specified parameters of k and random selection of cluster centers, were resolved through the generation of optimized initial cluster centroids, which was made possible by the optimization process of the MOA. The number of optimum cluster centers invariably gives the number of clusters to be generated.  
In some of the hybrid algorithms, parallelization of the K-means algorithm and quantum processing was made possible for faster convergence, handling distributed datasets, improved multidimensional datasets clustering, and reducing computational complexity. The issues of outlier detection, noise handling, discovering non-globular clusters, and non-linear partitioning were solved by some of the hybrid algorithms, as well as efficient clustering of large and high dimensional datasets.  
Furthermore, the various hybridized algorithms were tested on either synthetically generated datasets, UCI datasets, or some real-life datasets. The datasets used with the corresponding hybrid algorithm can be found in Table 2. The performance of the hybridized algorithms was also measured using different cluster analysis performance metrics. This is also included in Table 2.
###### Abstract  
A planetary gearbox is more complex in structure and motion than a gearbox with a fixed shaft, making it difficult to monitor and make a fault diagnosis in practice. Components must be frequently inspected to avoid excessive wear, but there is no simple way to directly measure wear. The most direct method is to log vibration and temperature signals using external sensors. Wireless sensors offer more space advantages than a wired one, so this study developed a measurement system that features a three-axis MEMS accelerometer, temperature sensing and wireless modules that are integrated into a planetary gearbox. Along with the system, a virtual instrument (VI) utilizing graphics programming language LabVIEW was developed to acquire and display data time and frequency domains to detect the gear's faults. To determine the root cause of vibrations in a planetary gearbox, determine the vibration signal model of amplitude modulation (AM) and frequency modulation (FM) due to gear damage and derive the characteristic frequencies of vibrations for a planetary gearbox, the characteristic frequencies and AMFM modulation were summarized in closed form. Different degrees of each gear damage were then detected in the planetary gearbox. The vibration signal model was validated by experiments to indicate the sideband around the gear meshing frequency and its feasibility for fault diagnosis of a planetary gearbox with the wireless embedded sensor.  
Keywords:planetary gearbox; wireless sensor; virtual instrumentation; mechanical wear; vibration
## 3 Results and Discussion
### Toxicants in Oily Sludge Heat Treatment Residue
#### 3.1.2 Heavy Metal Contents in Heat Treatment Residues  
In addition to organic compounds, the number of heavy metals in heat treatment residues also determines their toxicity, as they interact with biomolecules to produce toxic effects [32]. The alkanes and heavy metals in heat treatment residues can form a complex mixture. More importantly, the interaction of the alkanes with the heavy metals in the residue inhibits the survival of the microorganisms that degrade hydrocarbons [30], and hydrocarbons are known to affect heavy metal transport and migration [33]. Hydrocarbon decomposition leads to the release of free radicals and reactive oxygen species, which can damage the cellular structure of microorganisms, thus affecting the ecological structure [34]. The coexistence of heavy metals with alkanes in soils also leads to the conversion of productive agricultural land into wasteland [35]. This ultimately leads to oxygen depletion in water, disrupts aquatic biota [36], and increases the accumulation of heavy metals in sediments [37]. The phytoremediation of oil pollution in the presence of high concentrations of heavy metals has also been rarely studied [38]. Therefore, analyzing heavy metal levels in heat treatment residues is crucial. The analysis results are shown in Figure 2.  
The order of the heavy metals in the three residues based on the highest content was Cr > Ni > Cu > Zn > Pb > As > Cd > Hg. Additionally, compared to the pyrolytic residue and high-temperature oxidation residue, the contents of Zn, Cr, Ni, As, and Cd in the thermal-washing residue were higher. According to the risk screening values of the GB/T 15618-2018 soil environment quality risk control standard for the soil contamination of agricultural land, when the pH value of agricultural soils exceeds 7.5, the Cd, Hg, As, Pb, Cr, Cu, Ni, and Zn contents should not exceed 0.8, 3.4, 25, 170, 250, 100, 190, and 30 mg/kg, respectively. Therefore, the contents of Zn, Cr, and Ni in the three treatments far exceeded this limit, and the Cu levels in the pyrolysis and thermal flushing residues also exceeded this limit.  
Figure 2: Heavy metal contents in the three types of heat treatment residues (HTTOR: high-temperature oxidation residue; PR: pyrolytic residue; HWR: thermal-washing residue).