esa-sceva/satcom-chunk-collection huggingface dataset:
# Explanations for Neural Networks by Neural Networks
## 3 \(\mathcal{I}\)-Nets for Sparse Polynomials
(**a**) Dense polynomial representation: a value at position \(i\) represents the coefficient of the \(i\)-th monomial in the systematic enumeration. (**b**) Sparse polynomial representation: the polynomial is represented by pairs of the coefficient and index of the monomial in the systematic enumeration. enumeration of monomials shown previously. The coefficient at position \(i\) for \(i=\{0,\ldots,s\}\) belongs to the monomial index at position \(s+i\). Here, \(s\) defines the number of monomials with non-zero coefficients, which we also call the _sparsity_ of the polynomial in the following. The architecture of the output layer of an \(\mathcal{I}\)-Net for sparse polynomials is shown in Figure 3. For the coefficient outputs, we use linear output activations. Each index output is represented via \(\binom{n+d}{n}\) neurons with softmax activation (i.e., it can be seen as a separate classification task for each index). This way, indices can be constrained to be integers in the range \([0,\binom{n+d}{d}]\), which would not be possible directly when using linear activations. We use the sparse polynomial representation as explanations, i.e., as outputs of \(\mathcal{I}\)-Nets. Note that for a given \(\mathcal{I}\)-Net, the sparsity parameter \(s\) (which defines the dimensionality of \(\theta_{\lambda}\)) is fixed. For an efficient \(\mathcal{I}\)-Net training and evaluation, we need datasets where polynomial functions can act as reasonable explanations, i.e., datasets where we would choose the function family of polynomials for interpretability purposes in the first place. Therefore, Algorithm 1 shows how reasonable training data (i.e., parameters \(\theta_{\lambda}\)) for an \(\mathcal{I}\)-Net are generated. First, we sample parameter vectors \(\theta_{g^{*}}\) of sparse polynomials as target functions with fixed sparsity \(s\). Thereby, the coefficient values are sampled independently of \(\mathcal{U}(-\mathbf{1},\mathbf{1})\). The position indices in \(\theta_{g^{*}}\) are sampled from an urn without replacement with values \([0,1,\ldots,\binom{n+d}{d}]\). Then, for each \(\theta_{g^{*}}\), a number of sample points \(\mathbf{x}\) are drawn from \(\mathcal{U}(\mathbf{0},\mathbf{1})\), and the target function values \(y=g^{*}(\mathbf{x})\) are calculated to generate training data. Accordingly, we have \(N\) datasets with \(M\) samples of dimensionality \(n\) comprising random datapoints with function values for a randomly generated polynomial of degree \(d\) and sparsity \(s\). Next, a network \(\lambda\) is trained on the sampled data, so that \(\forall\mathbf{x}\in X:g^{*}(\mathbf{x})\approx\lambda(\mathbf{x})\). The set of parameters of these networks \(\lambda\) can then be used for training an \(\mathcal{I}\)-Net. The resulting \(\mathcal{I}\)-Net can generate explanations for sparse polynomials with fixed sparsity \(s\).
# Simplified Two-Dimensional Generalized Partial Response Target of Holographic Data Storage Channel
## 4 Simulation Results  
### Results of Proposed Model  
For the training process, the original data \(u[k]\) are randomly created with the size of 1,440,000 and the same probabilities for 0 and 1. Then, \(u[k]\) is encoded into a page-data \(a[j,k]\) with a size of 1200 \(\times\) 1200. The signal \(a[j,k]\) and the read-back signal \(y[j,k]\) are collected and used to estimate the coefficients of the GPR target and equalizer with the MMSE algorithm in Section 2. Then, these coefficients are used for the testing process. In the testing process, the coefficients from the GPR target are supplied to the serial detection in [13]. Simultaneously, we created 10 pages to evaluate the bit error rate (BER) performance of the system. In the first experiment, we investigate the constant \(c\) in (13). The results are presented in Figure 4. During this experiment, we fixed the blur at 1.8, SNR = 16 dB  
In Figures 5 and 6, the 1D GPR target model in [16] achieves similar BER performance at 0% and 10% misalignment, while the BER performance of the 2D SOVA model in [13] is significantly degraded at 10% misalignment. However, the proposed model performs almost the same, with 10% misalignment. These results show that the 2D GPR target can estimate the interference of the channel even if the misalignment effect appears. The 1D GPR target model in [13] used the partial response (PR) target, which has the fixed parameter of the target. Thus, when the misalignment appears, it cannot estimate the interference appropriately compared to the 2D GPR target. In addition, the proposed model is also compared to the DFE with reliability factor in [23], and the interference estimator with parallel structure in [22]. The interference estimator with parallel structure scheme achieves better BER performance compared to the proposed model because it is designed with the SOVA algorithm, which requires more complexity, instead of the simple VA used in the proposed model. Therefore, the proposed model can give a margin to the system designer for selecting a simple detection architecture. (Section 4.2 compares the complexity of various detection schemes.)  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**SNR** & \multicolumn{2}{c}{**G**} & \multicolumn{1}{c}{**v**} & \multicolumn{1}{c}{**b**} \\ \hline
10 dB & \(\left[\begin{array}{ccc}0.21&0.41&0.2\\ 0.41&0.85&0.4\\ 0.2&0.4&0.199\\ 0.21&0.417&0.2\\ 0.417&0.85&0.4\\ 0.2&0.4&0.197\\ \end{array}\right]\) & \(\left[\begin{array}{ccc}0.4877\\ 1\\ 0.4732\\ 1\\ 0.4705\\ 0.4928\\ 1\\ 0.4758\\ \end{array}\right]\) & \(\left[\begin{array}{ccc}0.4877\\ 1\\ 0.4732\\ 1\\ 0.4705\\ 0.4928\\ 1\\ 0.4750\\ \end{array}\right]\) & \(\left[\begin{array}{ccc}0.471&0.85&0.4\\ 0.447\\ 0.4705\\ 0.4928\\ 1\\ 0.4758\\ \end{array}\right]\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the target **v** and **b** with 1.8 blur and 10% misalignment.  
Figure 5: BER of the proposed 2D GPR target at 1.8 blur and 0% misalignment.
## 3 Results
### 3.5 Bunch Architecture and Botrytis cinerea Susceptibility  
#### 3.5.1 Bunch Architecture  
VSP was related to higher values for rachis weight, single berry weight and more berries per bunch, and hence, higher bunch weights, compared to SMPH training system (Figure 2). Shoot thinning increased bunch weight, while bunch thinning showed the opposite effect. Gibberellic acid and bunch thinning treatments were characterized by a higher rachis to bunch weight-ratio with smaller single berry weights, and thus, a looser bunch architecture was obtained. Whereas, shoot thinning resulted in a more compact bunch architecture.  
On average, a 35.1% lower single berry weight was recorded for SMPH, compared to VSP. Berry weight increased about 10% by means of shoot and biotechnological thinning and decreased about 27.4% with bunch thinning compared to SMPH. A significantly lower rachis weight was detected for all SMPH treatments compared to VSP (\(p<0.001\)). Across two seasons rachis weight in SMPH was significantly lower (\(-51.4\%\)), compared to VSP. Through shoot thinning rachis weight significantly increased by 35.6%, compared to SMPH. An increased rachis weight was observed in SMPH GA (+23.5%) while rachis weight decreased in SMPH BT (\(-18.4\%\)), but not significantly. On average, 30.2% less berries per bunch were recorded for SMPH compared to VSP. Berries per bunch decreased in SMPH GA (\(-13.5\%\)) and SMPH BT (\(-27.5\%\)), but increased with SMPH ST (22%) referred to SMPH. Compared to VSP a significant lower bunch weight was detected for all SMPH treatments (\(p<0.001\)). Average bunch weight in SMPH was significantly lower (\(-54.3\%\)) compared to VSP. In terms of SMPH BT, bunch weight significantly decreased about 44.1% and increased about 33.4% with SMPH ST compared to SMPH. SMPH GA and SMPH BT increased rachis to bunch weight-ratio by 26.0% (6.25%, \(\pm 0.41\)) and 45.9% (7.23%, \(\pm 0.39\)) respectively compared to SMPH and 33.83%, to 54.89%, respectively compared to VSP (\(p<0.001\)). To validate the rachis to bunch weight-ratio as a parameter for bunch compactness, the coefficient for determination of rachis to bunch weight-ratio and _B. cinerea_ infections in SMPH training system was determined in 2019 with R\({}^{2}\) = 0.67 for severity and R\({}^{2}\) = 0.86 for incidence (n = 12).
## 5 Experiments and Results
### Enhancing Model Explainability and Transparency with SHAP and LIME
#### 5.5.2 SHAP--Local Behavior Analysis
* A noteworthy observation here is that the SHAP force plot for this instance does not highlight \"IAT\", a feature previously identified as crucial in the global model. This discrepancy underscores the importance of local explanations, as the influence of each feature can vary significantly across individual data points. It suggests that while \"IAT\" is generally an important feature, its impact on this specific instance is minimal. Figure 12 shows the SHAP force plot for Test Sample 2, having a base value of 0.49 and the final prediction as \"Attack\". The following observations can be made:  
* The \"IAT\" (Inter-Arrival Time) feature, represented by a lengthy blue bar, negatively influences the prediction. This negative contribution shifts the classification away from benign traffic, increasing the likelihood of identifying the traffic as an attack. Longer inter-arrival intervals between packets often indicate irregular traffic patterns, which could signify malicious activity. * Negative contributions from features such as \"rst_count\", \"Header_Length\", and \"HTTPS\" strengthen the prediction of \"attack traffic\". A high count of reset packets often suggests denial-of-service attacks, as these packets are commonly used to terminate connections abruptly. Additionally, irregular or abnormal header lengths may indicate malicious behavior, such as attempts to obfuscate payloads or exploit protocol vulnerabilities. * The red-colored features, such as \"Variance\", \"ack_flag_number\", \"urg_count\", and \"flow_duration\", represent positive contributions to the prediction. These features suggest that the traffic is less likely to be an attack, reinforcing the classification as benign. Figure 11: SHAP force plot for testing Sample 1 (Local Explanation)—predicting “Class 0—Benign Traffic”. By integrating these observations with our broader understanding of the scenario, we can confidently conclude that the model is both intuitive and reliable in its predictions. For instance, when a flow contains a large number of packets with short time intervals between their delivery, the system becomes more likely to identify potential attacks.
## 4 PUPSO Approach with the Objective Function of LCOE
### Optimization Variables  
In this paper, the influence of geometric dimensions is considered to achieve a more economical design in the form of LCOE. The independent variables are shown in Figure 4 and their ranges are listed in Table 3. The ranges of the variables are set according to engineering and design experience. The thickness of each steel segment is assumed to be constant along the height direction. The range of the thickness of the steel section can be narrowed down with increasing design experience and determined in the cost estimate function to reduce the optimization variables and to accelerate the optimization  
\begin{table}
\begin{tabular}{c c c} \hline
**Title** & **Item** & **Unit Price** \\ \hline \multirow{8}{*}{Project condition} & Installed capacity & 50 MW \\  & Equipment fee & 5200 yuan/kW \\  & Other cost & 1200 yuan/kW \\  & Annual cost during operation & 80 yuan/kW (Year 1–5) \\  & & 120 yuan/kW (Year 6–20) \\  & construction period & 1 a \\  & Loan-to-value ratio & 80\% \\  & Depreciation life & 20 a \\  & Ratio of remaining value & 5\% \\  & Length of maturity & 15 a \\  & Interest rate & 4.9\% \\ \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 3: Flow chart of cost evaluation for the PCSH tower.  
computation. Based on the assumption that the length of each steel section is basically the same, the number of flanges is determined by the length and stress condition of the steel tower. For the PC segments, the thickness and diameter of the bottom cross section should not be smaller than the upper cross section of the segment.  
Apart from the aforementioned variables, there are some known dependent variables. The total length of the tower is 77.5 m. Therefore, the length of the steel segment determines the length of the concrete segments. The length of each steel section is the longitudinal dimension of the whole steel segment divided by the number of steel segments. The diameter of the steel tubular segment at the top of the tower is determined by the design of the nacelle and hub. Hence, their values are constant during the optimization. To simplify the problem and construction process, it is assumed that the generatrix of every section of the tower is a straight-line segment rather than a curved segment to easily determine the dimension of the tower at any height.
## 3 System Model
### Problem Statement  
For task \(i\), the data-computing can be offloading onto ONU, OLT, and the Internet. Only one place can be selected for task \(i\) data-computing. This place constraint is  
\[C2:x_{1i}+x_{2i}+x_{3i}=1 \tag{11}\]  
The computing capacities of MEC-1, MEC-2, and MEC-3 are different. The sum of the required computing CPU frequency cycles for all the tasks can not exceed the CPU frequency cycles of the servers in ONU (MEC-1), OLT (MEC-2), and Internet (MEC-3). The CPU frequency cycles constraints are:  
\[C3:\sum_{i\in Neb(j,K)}\sum_{k=1}^{K}x_{1i}*f_{ji}^{ONU}\leq F_{j}^{ONU} \tag{12}\]  
\[C4:\sum_{j}\sum_{i\in Neb(j,K)}\sum_{k=1}^{K}x_{2i}*f_{ri}^{OLT}\leq F_{r}^{OLT} \tag{13}\]  
\[C5:\sum_{r}\sum_{j}\sum_{i\in Neb(j,K)}\sum_{k=1}^{K}x_{3i}*f_{i}^{Int}\leq F_{ C}^{Int} \tag{14}\]  
Equation (12) means that the occupied data-computing frequency on ONU \(j\) cannot exceed the physical frequency of ONU \(j\). Equation (13) means that the occupied data-computing frequency on OLT \(r\) cannot exceed the physical frequency of OLT \(r\). Equation (14) means that the occupied data-computing frequency on the Internet cannot exceed the physical frequency of the Internet. The links' abilities are limited. For links, the data rate constraints are:  
\[C6:\sum_{r}\sum_{j}\sum_{i\in Neb(j,K)}\sum_{k=1}^{K}x_{3i}*f_{i}^{Int}\leq F_{ C}^{Int} \tag{15}\]  
\[C7:\sum_{C}x_{3i}*R_{Ci}^{F}\leq R^{Int} \tag{16}\]  
Equation (15) means that, for each ONU-OLT link, the sum of the MEC-1 offloading data rate and MEC-2 offloading data rate cannot exceed the physical data rate of this link. Equation (16) means that, for each OLT-Internet link, the MEC-3 offloading data rate cannot exceed the physical data rate of this link.  
Then, the objective function of this optimization problem can be formulated as  
\[\begin{array}{ll}min&\left[\sum_{r}\sum_{j}\sum_{k}(x_{1i}*T_{1i}+x_{2i}*T_{2i} +x_{3i}*T_{3i})\right]\\ &s.t.~{}~{}C1-C7\end{array} \tag{17}\]
## 6 Experimental Results and Discussion
### Results Obtained Using Late Fusion strategies  
Finally, aiming to achieve better results, the sum, max and product combination rules were employed as a late fusion strategy. In all cases, the sum rule obtained the best results. Due to this, we decided to describe in Table 6 only the results obtained with this rule. The results described were obtained combining the three classifiers chosen among those with the best performance in the experiments described previously.  
The best overall results obtained in this work, i.e., 99.16% of accuracy, were obtained in two different scenarios. The first one occurred in the combination between \(LPQ_{7}\)-SVM, BinAC-5-NN and BinSC-Reg. It is worth mentioning that in isolation, these classifiers had reached, respectively, 91.67%, 95.83% and 92.50%, as can be seen in the first section of Table 6.  
The second scenario in which the best rate was obtained happened when the classifiers \(LPQ_{7}\)-SVM, AC-SVM and BinAC-5-NN were combined. In isolation, these classifiers had reached, respectively, 91.67%, 95.00% and 95.83%, as can be seen in the second section of Table 6.  
An accuracy of 98.33% was reached by combining \(LPQ_{7}\)-SVM, AC-SVM and BinSC-SVM. In isolation, these classifiers had reached, respectively, 91.67%, 95.00% and 82.50%, as can be seen in the third section of Table 6.
## 1 Introduction
### Detection Mechanism Based on AIEgens Fluorescence Sensing  
AIEgens-based fluorescent sensors offer a diverse range of detection mechanisms, depending on the chemical structure and optical properties of the target analyte [63; 64; 65; 66; 67; 68]. The main detection mechanisms include the following:  
(i) Chemical interaction-based detection: These sensors are equipped with chemically reactive groups capable of protonation or deprotonation. Interactions between the sensor and the target molecule can alter the optical properties or solubility of the mixture, resulting in a detectable signal. (ii) Interaction-based detection: This mechanism involves interactions such as metal-ligand coordination, electrostatic bonding, hydrogen bonding, and \(\pi\)-\(\pi\) interactions. These interactions can induce changes in the optical properties or effects between the sensor and the target substance, facilitating detection. (iii) Photophysical process-based detection: This approach relies on changes in photophysical processes in the presence of the target substance. Key processes include the following: (1) intramolecular charge transfer (ICT)--involves electron-donating and electron-accepting groups. Interaction with the target alters the electronic properties of these groups, affecting fluorescence; (2) photoinduced electron transfer (PET)--the interaction between electron-rich and electron-deficient components modifies the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energies, leading to changes in fluorescence; (3) Forster resonance energy transfer (FRET)--requires physical proximity between donor and acceptor molecules, with the emission spectra of the donor overlapping with the absorption spectra of the acceptor; (4) excited state intramolecular proton transfer (ESIPT)--excitation causes electron delocalization and hydrogen proton transfer through intramolecular hydrogen bonding, resulting in structural transitions and reciprocal isomerism; (5) internal filtering effect (IFE)--in the system, there must be a substance that can absorb excitation light or emitted light, and the absorption spectrum of this substance has an overlapping part with the excitation spectrum of the fluorescent substance. (iv) Biomolecular detection: This method includes lateral flow immunoassay devices or the conjugation of biomolecules (such as peptides, antigens, and antibodies) to AIEgens. This approach is used for the specific detection and identification of analytes through biomolecular interactions.
## 2 Data and Methods  
### Study Area  
Zambia is a nation with an extensive land resource base (58% classified as arable), much of it covered by savanna and savanna woodland, and possessing 40% of the water in Central and Southern Africa [51,52]. With only 14% of the resource base cultivated each year, and the agriculture sector accounting for almost 70% of labor force employment, Zambia is one of the global focal points for agricultural expansion [53]. The Southern Province of Zambia (Figure 1) is one of the key agricultural regions in the country. The province falls within the semi-arid/highland semi-arid agro-ecological zones and is dominated by smallholder farming, similar to the Central, Lusaka, and Eastern provinces of Zambia and comparable regions in south-central Africa, the Sahel, and east African steppe. The climate is sub-tropical, characterized by three seasons: cool dry (April-August), hot dry (August-November), and warm wet season (November-April). Average temperatures are between 14 \(\,\)C and 28 \(\,\)C with an average annual rainfall accumulation of 800-1000 mm (less than 700 mm in the South Chama and Lundazi regions of the extreme south). The province is dominated by clay soils supporting primary crops of maize, cassava, groundnuts, millet, seed cotton, and wheat. The population was estimated to be over 880,000 in 2010, an increase of approximately 38% from 1990 [54].  
Figure 1: Country of Zambia with provinces outlined in black and water bodies/seasonally annotated areas in blue. The Southern Province is highlighted in green.
## 3 Results
### Analysis of Feature Wavelengths  
The near-infrared spectra of seeds yielded a mixed spectrum that reflects their internal composition. Within this spectrum, specific wavelength ranges are where the seeds display spectral peaks. The positions and variations of these peaks correlate with internal physiological and chemical indicators of the seeds [16; 17]. Different feature variable selection methods employed in model development identify certain common spectral regions. Figure 14 portrays the feature wavelengths chosen via different variable selection algorithms.  
The feature variables selected via the SiPLS-RF algorithm for SVR modeling yielded the most accurate prediction results. The traceability analysis of the chosen variables revealed the wavelength points to be primarily concentrated around 5150 cm\({}^{-1}\), 4800 cm\({}^{-1}\), 4950 cm\({}^{-1}\), and 6920 cm\({}^{-1}\). The pronounced absorption peak at 5150 cm\({}^{-1}\) corresponds to the combined spectrum band of water's (H\({}_{2}\)O) anti-symmetric stretching and bending vibration, indicating its relation to the H\({}_{2}\)O content within the seed. This water activates the enzymes that control seed germination and is involved in the germination process itself.  
The points at 4800 cm\({}^{-1}\), 4950 cm\({}^{-1}\), and 6920 cm\({}^{-1}\) originate from the -OH in the carboxyl group (-COOH), and the combination frequency of -OH stretching vibration and -CH bending vibration at 4800 cm\({}^{-1}\) and 4950 cm\({}^{-1}\). At 6920 cm\({}^{-1}\), the carboxylic acid monomer features non-bonded or free -OH stretching vibration doubled, and -COOH was the functional group of oleic acid. Research has shown that under identical stress  
Figure 12: Process of optimizing feature variables in SiPLS–RF algorithm. (**a**) Iteration error variation. (**b**) Variable selection probability.  
Figure 13: Distribution of SiPLS–RF filtering feature variables.  
conditions, rapeseed seeds with high oleic acid content exhibit a lower germination rate than those with low or normal oleic acid content, thereby affecting seed vigor [18].  
The characteristic bands identified via the SiPLS-RF variable selection method mirror the information related to germination rate within the seed and reflect the relationship between relevant substances and changes in the germination rate. This provides preliminary insight into the mechanism for detecting germination rates in rapeseed seeds using NIR.