esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The ability to automatically generate code, i.e., program synthesis, is one of the most important applications of artificial intelligence (AI). Currently, two AI techniques are leading the way: large language models (LLMs) and genetic programming (GP) methods--each with its strengths and weaknesses. While LLMs have shown success in program synthesis from a task description, they often struggle to generate the correct code due to ambiguity in task specifications, complex programming syntax, and lack of reliability in the generated code. Furthermore, their generative nature limits their ability to fix erroneous code with iterative LLM prompting. Grammar-guided genetic programming (G3P, i.e., one of the top GP methods) has been shown capable of evolving programs that fit a defined Backus-Naur-form (BNF) grammar based on a set of input/output tests that help guide the search process while ensuring that the generated code does not include calls to untrustworthy libraries or poorly structured snippets. However, G3P still faces issues generating code for complex tasks. A recent study attempting to combine both approaches (G3P and LLMs) by seeding an LLM-generated program into the initial population of the G3P has shown promising results. However, the approach rapidly loses the seeded information over the evolutionary process, which hinders its performance. In this work, we propose combining an LLM (specifically ChatGPT) with a many-objective G3P (MaOG3P) framework in two parts: (i) provide the LLM-generated code as a seed to the evolutionary process following a grammar-mapping phase that creates an avenue for program evolution and error correction; and (ii) leverage many-objective similarity measures towards the LLM-generated code to guide the search process throughout the evolution. The idea behind using the similarity measures is that the LLM-generated code is likely to be close to the correct fitting code. Our approach compels any generated program to adhere to the BNF grammar, ultimately mitigating security risks and improving code quality. Experiments on a well-known and widely used program synthesis dataset show that our approach successfully improves the synthesis of grammar-fitting code for several tasks.  
program synthesis; large language models; grammar-guided genetic programming; grammar; multi-objective +
Footnote †: journal: _Algorithms_
## 3 Experimental Measurement Procedure and Results
### Experimental Measurement Procedure  
* RF Power Gain Measurement Take as a reference Figure 1. The RoF RF gain, \(\mathrm{G_{RF}}\), is measured as the ratio between the RF power at the LNA2 output, \(\mathrm{p_{RFout}}\), and the available RF power at the LNA1 input, \(\mathrm{P_{RFin}}\). It can be expressed in decibels (dB) by [2]: \[\mathrm{G_{RF}^{M}}=\mathrm{P_{RFout}}-\mathrm{P_{RFin}}.\] (1)
* Noise Figure Measurement The NF can be measured as the ratio between the output noise power spectral density (PSD) of the system, \(\mathrm{n_{out}}\), and the noise PSD at the output only due to the system input noise power, \(\mathrm{n_{in}}\). In dB, the measured NF is expressed by the following formula [2]: \[\mathrm{NF^{M}}=\mathrm{N_{out}}-\left(\mathrm{N_{in}}+\mathrm{G_{RF}}\right),\] (2) where \(\mathrm{N_{in}}\) is the system input thermal noise at the reference temperature, which is considered to be \(-174\) dBm.Hz\({}^{-1}\)[2].
* Spurious-free dynamic range measurement The third-order SFDR is a measurement of the dynamic range between the minimal RF output power and the maximum RF output power that is free from the third-order intermodulation signal. It is measured via a two-tone experiment with a \(\Delta\mathrm{f}\pm 2\) kHz and can be expressed in dB.Hz\({}^{2/3}\) as follows [2]: \[\mathrm{SFDR^{M}}=\frac{2}{3}(\mathrm{IIP_{3}}-\mathrm{NF}+\mathrm{N_{in}}),\] (3) where \(\mathrm{IIP_{3}}\) is the input third-order intercept point.
# Query Rewriting for Incremental Continuous Query Evaluation in HIFUN
## 3 Preliminaries--The HIFUN Query Language
### Analysis Context  
Now, the attributes \(d\), \(b\), \(p\) and \(q\) of our running example are \"factual\" or \"direct\" attributes of \(D\) in the sense that their values appear on the delivery invoices. However, apart from these attributes, analysts might be interested in attributes that are not direct but can be \"derived\" from the direct attributes. Figure 1 shows the direct attributes together with several derived attributes: attribute r can be derived from attribute b based on geographical information on the location of each branch; and attributes s and c can be derived from a product's master table. The set of all attributes (direct and derived) that are of interest to a group of analysts is called an analysis context (or simply a context).  
Actually, as we shall see shortly, the context is the interface between the analyst and the dataset, in the sense that the analyst uses attributes of the context in order to formulate  
Figure 1: Running example.  
queries (in the form of triples, as seen earlier). The users of a context can combine attributes to form complex grouping functions.  
In HIFUN one can form complex grouping or measuring functions using the following three operations on functions: composition, pairing and Cartesian product projection. An example is shown in Figure 2. As shown, in order to ask for the total quantities by region, we need to use the composition \(r\circ b\) as a grouping function in order to express the query: \((r\circ b,q,sum)\). The attribute \(r\) is derived, as the value can be computed from this of attribute \(b\) (e.g., from a specific branch name one can derive the region in which the branch exists). For pairing, in order to ask for a total quantities delivered by a branch _and_ product, we need to use the pairing \(\wedge\) operator as a grouping function in order to express the query: \((b\wedge p,q,sum)\) (e.g., its answer associates every pair (branch, product) with a total quantity). We assume now that the query \(Q=(b\wedge p,q,sum)\) has been evaluated and its results stored in a cache. Then we can compute the totals by branch and the totals by product from the result of Q, using the following rewritings:  
\[Q=(b,q,sum)=(proj_{C_{Branch}},(b\wedge p,q,sum),op) \tag{1}\]  
\[Q=(p,q,sum)=(proj_{C_{Product}},(b\wedge p,q,sum),op) \tag{2}\]
# Convergence Analysis of the Phase-Scheduled-Command FXLMS Algorithm with Phase Error
## 2 Active Sound Profiling Algorithms
### PSC-FXLMS Algorithm  
In practical applications, the command-FXLMS algorithm is highly robust to amplitude errors in the plant estimates, which represents a significant advantage over the ANE-based  
Figure 1: Block diagram of the command-FXLMS algorithm.  
algorithms. However, this algorithm has excessive control effort if the command and disturbance signals are out of phase. To overcome this shortcoming, [PERSON] et al. [13] have proposed the PSC-FXLMS algorithm, which is shown in Figure 2.  
The characteristic of the PSC-FXLMS algorithm is setting the command signal in phase with the disturbance signal. The command signal phase is set by the measured phase by performing a fast Fourier transform (FFT) on the modeled disturbance. The modeled disturbance signal, \(\hat{d}(n)\), can be expressed by  
\[\hat{d}(n)=e(n)-y(n)*\hat{s}(n) \tag{7}\]  
the FFT is performed to compute the phase of the disturbance signal, which is denoted as \(\phi_{\hat{d}}\); then, the command signal can be expressed by:  
\[c(n)=C\cos(2\pi f\frac{n}{F_{S}}+\phi_{\hat{d}}) \tag{8}\]  
where \(C\) is the predefined amplitude of the command signal, \(F_{S}\) is the sampling rate and \(f\) is the frequency of the disturbance signal, which can be obtained from the reference signal.
# Assessment of Characteristics of Acid Mine Drainage Treated with Fly Ash
## 3 Results and Discussion  
### XRF Analysis of Fly Ash  
The major minerals phases in the fly ash samples were found to be SiO\({}_{2}\), Al\({}_{2}\)O\({}_{3}\), CaO, MgO, MnO, Na\({}_{2}\)O, K\({}_{2}\)O and FeO, as shown in Table 2. Different percentages of the minerals were found in each of the four fly ash samples. The Anpara TPP sample showed the highest concentration of Al\({}_{2}\)O\({}_{3}\) and CaO (and K\({}_{2}\)O), and Shaktinagar TPP showed the highest concentration of SiO\({}_{2}\) (and MnO). Na\({}_{2}\)O, K\({}_{2}\)O, CaO and MgO can be considered as acid-neutralizing groups, and Al\({}_{2}\)O\({}_{3}\) and F\({}_{2}\)O\({}_{3}\) as amphoteric groups.  
The following chemical reactions were expected after mixing with AMD:  
1. Silica (SiO\({}_{2}\)) is stable below 870 \({}^{\circ}\)C. It will not react with any base or acid except hydrofluoric acid. Hence, SiO\({}_{2}\) will not have an impact on AMD.
2. Alumina (Al\({}_{2}\)O\({}_{3}\)) will also not have any impact on AMD, at least at room temperature.
3. Hematite (Fe\({}_{2}\)O\({}_{3}\)) will have negligible impact on AMD, up to the most extreme temperature and pressure found in the field.
4. Lime (CaO) present in fly ash will react with H\({}_{2}\)O to give Ca(OH)\({}_{2}\).
5. Magnesia (MgO) present in fly ash will react with H\({}_{2}\)O to give Mg (OH)\({}_{2}\), which will also neutralize acidic solutions
6. Na\({}_{2}\)O will react with H\({}_{2}\)O to give NaOH.
7. K\({}_{2}\)O will react with H\({}_{2}\)O to give KOH.  
Thus, considering the ability of CaO, MgO, Na\({}_{2}\)O and K\({}_{2}\)O to provide hydroxyl radicals in the solution, the acid-neutralization potential of fly ash may be calculated. The amount of SO\({}_{3}\) in the fly ash was only 0.18%, and for the sake of simplicity, its effect may be disregarded [32].
## 3 Adaptive Routing Optimization Algorithm
### Transmission Algorithm  
In OppNets, the transmission scheme of messages is based on the greed strategy in general. That is, compared to the current node with a message, an intermediate node has more opportunities to access the destination node. Then it is selected as a relay and can receive the message forwarded by the current node. In this paper, the network contains a number of communities. The messages are forwarded by ordinary nodes within the community, and are ferried by ferry nodes between communities.  
The transmission strategy in the community is: If the intermediate node \(v_{i}\) is met, then the following requirement applies: \(v_{i}\) is one of the ferry nodes or \(w_{i}>w_{s}\) (where, \(w_{i}\) is the metric sum of relationships between \(v_{i}\) and other nodes in the community). In the case whereby \(v_{i}\) is thought to be more active than the source node \(v_{s}\) and it has more opportunities to access the destination node, \(v_{s}\) forwards the message to \(v_{i}\) and \(v_{i}\) becomes a relay node. The transmission strategy between the  
Figure 2: Force diagram of the ferry node presented: (**A**) in a community; and (**B**) between communities.  
communities is: If the intermediate node \(v_{i}\) is in the ordinary node and is a member in the community of the destination node, the ferry node \(v_{f}\) forwards the message to \(v_{i}\) and \(v_{i}\) becomes a relay node.  
The Algorithm 1 explains the process of the adaptive routing. At a particular time, the node \(v_{s}\) needs to send a message \(m\) to the node \(v_{d}\) and encounters a node \(v_{i}\). If \(v_{i}\) is \(v_{d}\) or \(v_{i}\) has more opportunities to access \(v_{d}\) than \(v_{s}\), \(v_{i}\) is selected as a relay and receives \(m\) from \(v_{s}\). Otherwise, \(v_{s}\) continues carrying \(m\) until it meets the next node. Then, \(v_{i}\) with \(m\) also participates in the forwarding, and this process does not stop until \(v_{d}\) accepts \(m\).  
```
1:source node \(v_{s}\), destination node \(v_{d}\), ferry node \(v_{f}\), message \(m\), community C, metric \(w\)
2:while\(v_{d}\) without \(m\)do
3: current node \(v_{c}\) (\(v_{s}\), \(v_{f}\) or relay node \(v_{r}\)) encounters node \(v_{i}\) without \(m\);
4:if\(v_{i}\) is \(v_{d}\)then
5:\(v_{i}\) accepts \(m\) from \(v_{c}\) and the forwarding process ends;
6:else
7:if\(v_{c}\) and \(v_{d}\) are in the same C then
8:if\(v_{i}\) is in the C and \(w_{id}>w_{cd}\)then
9:\(v_{i}\) is selected as \(v_{r}\), and \(v_{c}\) forwards \(m\) to \(v_{i}\);
10:endif
11:else
12:if\(v_{c}\) is \(v_{f}\)then
13:if\(v_{i}\) and \(v_{d}\) are in the same C then
14:\(v_{i}\) is selected as \(v_{r}\), and \(v_{c}\) forwards \(m\) to \(v_{i}\);
15:endif
16:else
17:if\(v_{i}\) is \(v_{f}\) or \(w_{id}>w_{cd}\)then
18:\(v_{i}\) is selected as \(v_{r}\), and \(v_{c}\) forwards \(m\) to \(v_{i}\);
19:endif
20:endif
21:endif
22:endif
23:endwhile
```  
**Algorithm 1**An Adaptive Routing Optimization Algorithm
# Liquid-Crystal-on-Silicon for Augmented Reality Displays
## 2 LCoS Working Principles
### Mixed-Mode Twist Nematic (MTN) Mode  
In a twisted-nematic (TN) mode [25; 26], the LCs form twisted structures by the surface alignment and the doped chiral agent. For example, in a 90\({}^{\circ}\) TN cell, the top and the bottom LCs are aligned orthogonally with a twist angle \(\phi=90^{\circ}\), as illustrated in Figure 4a. Such a molecular alignment can be achieved by mechanical rubbing on polyimide (PI) [15; 27] or by the photo-alignment technique [28; 29]. For intensity modulation, the LC cell is sandwiched between two crossed polarizers. The polarization axis of the front polarizer has an in-plane angle \(\beta\) with respect to the front LC alignment. When \(\beta=0^{\circ}\), the polarization rotation effect takes place, which guides the incident x-linearly polarized light to y-linear polarization. The Gooch-Tarry first minimum condition \(d\Delta n/\lambda=\sqrt{3}/2\)[30] determines the thinnest cell gap for a given LC birefringence to achieve maximum transmittance. When a longitudinal electric field is applied, the LC molecules (\(\Delta\varepsilon>0\)) are reoriented toward the vertical direction and a dark state is produced (Figure 4b). TN cells work well as a normally-white intensity modulator. It can  
Figure 3: The LC director orientations in VA mode: (**a**) Voltage-off state; (**b**) Voltage-on state.  
also be used for phase-only modulation [31], but the voltage should be controlled below the optical threshold [32], leading to a slow response time [33]. For intensity modulation, the response time of the TN mode and VA mode could be comparable, depending on the employed LC material and the cell gap. A major drawback of TN mode is the compromised contrast ratio (CR \(\sim\)1000:1).  
In a reflective LCoS, the doubled optical path leads to a smaller \(d\Delta n\) requirement. However, when \(\beta=0^{\circ}\) the normalized optical efficiency is only 70% [6]. Two methods can be used to boost the optical efficiency: (1) to increase \(\beta\) to about 20\({}^{\circ}\), and (2) to decrease the twist angle to 80\({}^{\circ}\) or 70\({}^{\circ}\)[34]. At \(\beta\approx 20^{\circ}\), both the polarization rotation effect and birefringence effect coexist. This is called the mixed-mode twisted nematic (MTN) mode [6]. The optical efficiency increases to \(\sim\)88%. Decreasing the twist angle can enhance the optical efficiency to nearly 100%, but the contrast ratio is decreased dramatically. Therefore, 90\({}^{\circ}\) MTN a favored choice, especially for field sequential color (FSC) displays, because of its merits in high contrast ratio, low operation voltage and fast response time. By removing the spatial color filters, FSC operation could triple the optical efficiency and resolution density.
## 2 Theoretical Behavior of Distributed versus Concentrated Volt/var Local Control Strategy
### Distributed Var-Sinks  
Figure 4 shows the equivalent circuit of an LV feeder with distributed var-sinks. A feeder with \(n\) buses is connected to the MVG through the DTR. One of the feeder ends is connected to the LV bus of the DTR, the so-called feeder-head-bus (fhb). This bus bar is not counted as a feeder bus. The other end is called the feeder-end-bus (feb), and is numbered bus 1. Based on these assignments, the segment number of the feeder is equal to the number of its buses \(n\). Since Equation (6) includes only \(X\), each feeder segment is modeled by a reactance of equal value. It is also assumed that the same reactive power \(Q\) (var-sink) is absorbed on each bus. This corresponds to the real case when the same-sized PV facilities are installed to each customer, and the \(cos\varphi(P)\) control is used. We assume that all of the customers are in the same weather zone, so their PVs produce the same \(P\), while the inverters absorb the same \(Q\).  
The voltage drop over the whole feeder is equal to the sum of the voltage drop of each feeder segment as in:  
\[\Delta U_{Total}=\Delta U_{\Sigma}(n)=\sum_{i=1}^{n}\Delta U_{i}, \tag{8}\]  
Figure 4: Equivalent circuit of a low-voltage (LV) feeder with distributed var-sinks.  
where \(\Delta U_{Total}\)--total voltage drop of a feeder; \(n\)--number of feeder segments; \(\Delta U_{\Sigma}\)--voltage drop over the whole feeder; \(\Delta U_{i}\)--voltage drop in the feeder segment \(i\).  
The total voltage drop on a feeder is calculated by Equation (9) as follows:  
\[\Delta U_{Total}=U_{1}-U_{fhb}=U_{feb}-U_{fhb}, \tag{9}\]  
where \(U_{fhb}\)--voltage at the feeder-head-bus bar; \(U_{feb}\)--voltage at the feeder-end-bus bar.  
The total voltage drop of a feeder with distributed var-sinks, \(f_{d}(n)\), is function of \(n\) as long as the \(X\), \(Q\), and \(U_{1}=U_{feb}\) are constant. It is derived from Equations (6) and (8), and is defined by the following recursive formula:  
\[\Delta U_{Total}=\Delta U_{\Sigma}(n)=f_{d}(n)=-\frac{Q\cdot X}{U_{1}}-\sum_{k =2}^{n}\frac{k\cdot Q\cdot X}{U_{1}-f_{d}(k-1)}. \tag{10}\]  
The list of the first four terms of Equation (10) is given in the Appendix A.
## 5 Photovoltaic Power Prediction Based on Objective Optimization IHO-ELM  
### Extreme Learning Machine (ELM)  
The ELM algorithm randomly selects hidden layers and input weight biases, solving the problems of slow learning rate, long iteration time, and the need for manually setting learning parameters in traditional neural networks [28]. Assuming there is a set of training samples \((h_{i},l_{i})\), \(h_{i}=(h_{i1},h_{i2},\ldots,h_{in})^{T}\in R^{n}\) are input vectors for network samples and \(l_{i}=(l_{i1},l_{i2},\ldots,l_{in})^{T}\in R^{m}\) is the output vector of the network. The general form of the standard ELM with L hidden layer neurons is shown in Equation (25).  
\[f_{L}(h)=\sum_{i=1}^{L}\beta_{i}G_{i}(h)=\sum_{i=1}^{L}\beta_{i}G_{i}(a_{i},b_{ i},H) \tag{25}\]  
In the formula, \(\beta_{i}\) are output weights, \(\beta_{i}G_{i}(a_{i},b_{i},H)\) activates the function, \(a_{i}\) is the input weight of the network, and \(b_{i}\) is the unit threshold for the \(i\) hidden layer. However, the connection weights and thresholds between the hidden layer and the input layer of the ELM can affect its prediction accuracy. Therefore, this paper uses the IHO to optimize these two parameters.  
The steps to optimize the weight and threshold parameters in the ELM using the IHO optimization algorithm are as follows; the flowchart is shown in Figure 5.  
Step 1: Initialize the population and randomly set the initial position and velocity of each individual in the population.  
Step 2: Determine fitness and calculate the fitness of each individual.  
Step 3: Compare the fitness of each individual with their historical best position and update their position if the historical best position is good.  
Step 4: Compare the fitness of the individual in the population with the global optimal position of the population and update its position if the global optimal position is good.  
Step 5: Adjust position and speed.  
Step 6: If the end condition is met, the position is good enough, or the maximum number of iterations is reached, the above iteration process ends. Otherwise, continue with Step 5.  
Figure 5: Flowchart of IHO-ELM.
## 2 Materials and Methods  
For the research was used sanitary waste obtained from waste products of one of the Polish sanitary fittings factory. Ceramic waste was ground into two 0-4 mm and 4-8 mm fractions and subjected to sieving analysis according to [43]. Ceramic cullet before and after crushing is shown in Figure 1.  
It allowed to obtain the following results of individual fractions \(0\div 0.125\) mm \(-\) 1.14%, \(0.125\div 0.25\) mm \(-\) 2.79%, \(0.25\div 0.5\) mm \(-\) 6.29%, \(0.5\div 1.0\) mm \(-\) 11.21%, \(1.0\div 2.0\) mm \(-\) 21.79%, \(2.0\div 4.0\) mm \(-\) 28.07% and \(4.0\div 8.0\) mm \(-\) 28.57%. The grain-size distribution curve of the studied aggregate is shown in Figure 2.  
As part of the aggregate tests performed, its crushing strength based on [44] was determined. The test was carried out on an aggregate of 4 \(\div\) 8 mm. The average crushing index was 6.75%, which means that the aggregate from sanitary ceramic waste is resistant to crushing.  
The mineral content of ceramic aggregate used to the designed concrete mixes was also studied method using X-ray diffractometer Panalytical X'pertPRO MPD with goniometer PW 3020. The diffractogram of phase content of aggregate is shown in Figure 3. The main mineral content of ceramic aggregate constitutes mulit recognized by characteristic interplanar distances d\({}_{\mathrm{hkl}}\) = 5.376; 3.425; 3.390; 2.882; 2.427; 2.294; 2.208 A. In addition, quartz occurs collaterally recognized by d\({}_{\mathrm{hkl}}\) = 4.255; 3.344, 2.456; 2.283; 2.237; 2.128 1.981 A and cristobalite d\({}_{\mathrm{hkl}}\) = 4.055; 3.140; 2.847; 2.486 A and calcite d\({}_{\mathrm{hkl}}\) = 3.861; 3.040; 2.283; 2.096 A. Together with crystal phases in the used aggregate, there was also an amorphous substance (aluminosilicate glaze) whose occurrence on diffractograms is visible by the increase of their background within the scope of angles from 15 to 35 (20).  
Figure 1: Ceramic cullet: (**a**) before crushing; (**b**) after crushing, fraction 4–8 mm.  
Figure 2: The grain-size distribution curve of aggregate.  
Characteristics of Concrete Mix  
Under the iterative method of designing a concrete mixture with assuming the limitation of the substrates of the mix to the popularly used cements, there were designed 2 concrete mixtures, which differed only in the applied cement. The first series was based on the \"Gorkal 70\" aluminous cement, while the second series, based on the Portland cement 32.5R.  
In each series, only aggregate from sanitary ceramic waste was used, it was crushed to the two previously described factors. Below, in Table 1 is presented the composition of concrete mixtures referred to 1 m\({}^{3}\). All prepared samples have been treated in accordance with the standard requirements.
## 2 Materials and Methods: Ten-Koh Mission Description
### Satellite Platform
#### 2.2.3 AdS  
The attitude determination subsystem philosophy is outlined in Figure 3. Raw measurements from coarse sun-sensors, gyroscopes and magnetometer are processed on-ground in order to estimate the attitude. Because attitude control has not been implemented, attitude determination on-board was not required, which further simplified the on-board software.  
The 12 coarse sun-sensors were implemented using S4349 photodiodes sampled with the AD7927 ADC, with one diode placed on each solar panel of the satellite. Triple modular hot-redundant A3G4250d gyroscopes were used to provide attitude-rate measurements, while the magnetic field is sensed by the HMC2003 three-axis magnetometer and two 16-bit ADCs. All the ADS sensors are sequentially sampled over a SPI data bus by one PIC microcontroller in order to ensure that all the readings are synchronized, which is required for attitude determination. A detailed description of the ADS subsystem is included in [17].  
While the spacecraft is in view of the ground station, the ADS sensor data can be downlinked in real-time and combined with downlink signal measurements done by the ground station. This enables higher attitude determination accuracy during ground station passes.  
Figure 3: Attitude determination philosophy for the Ten-Koh satellite. The magnetometer instrument is used for space environment purposes as well as for ADS.