esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Proposed HEM-AL Scheme for Optimal Selection of Centroid and DCNN Weights  
### Solution Encoding and Objective Function  
In this work, the centroids are optimally chosen for attaining energy-efficient clustering. Furthermore, for disease diagnosis in IoT, optimized DCNN was deployed, where the weights were also elected optimally for better training. For optimization purposes, a new HEM-AL model is introduced in this work. The input solution for optimal centroid selection is illustrated in Figure 2, wherein, \(CH_{1}\), \(CH_{2}\) and \(CH_{3}\) represent the centroids for lung cancer, breast cancer and heart disease, respectively. The minimal and maximal values of lung cancer data form the bounds of \(CH_{1}\), minimal and maximal values of breast cancer data form the bounds of \(CH_{2}\) and minimal and maximal values of heart disease data form the bounds of \(CH_{3}\). The objective function \(Obj\) of the developed model for optimal centroid selection is given in Equation (24), wherein \(di\) and \(E\) is evaluated as shown in Equations (25) and (26), respectively. In Equation (25), \(Cl\) refers to the cluster.  
\[Obj=Min(di\ +\ E) \tag{24}\]Further, the nodes are clustered based on the centroid as shown in Equations (27)-(29). If \(d_{1}\) is small, the data belong to \(Cl_{1}\), if \(d_{2}\) is small, the data belong to \(Cl_{2}\) and if \(d_{3}\) is small, the data belong to \(Cl_{3}\).  
\[di=mean(Cl_{1}\ d_{1},\ C_{1})\ +\ mean(Cl_{2}\ d_{2},\ C_{2})\ +\ mean(Cl_{3}\ d_{3},\ C_{3}) \tag{25}\]  
\[E=[0.5-mean(Cl_{1},\ E)]+[0.3-mean(Cl_{2},\ E)]+[0.2-mean(Cl_{3},\ E)] \tag{26}\]  
\[d_{1}=\ di(CH_{1},\ D) \tag{27}\]  
\[d_{2}=\ di(CH_{2},\ D) \tag{28}\]  
\[d_{3}=\ di(CH_{3},\ D) \tag{29}\]  
The input solution to the adopted scheme for optimal weight selection is illustrated in Figure 3, wherein, \(W_{1}\), \(W_{2}\), \(W_{3}\), signifies the weight and \(nu\) represents the entire weight count. Furthermore, the objective of the developed model denoted by \(Obj\) is given in Equation (30), wherein \(Err\) signifies the error.  
\[Obj=Min\bigg{(}\frac{1}{Loss}\bigg{)}=Min(Err) \tag{30}\]
## 4 Experiments and Results
### Experimental Results
#### 4.3.1 Analysis of the Effectiveness of DFs  
For the first time, this chapter proposes DFs that can be obtained in both active and passive network communication environments, from the perspective of the software and hardware characteristics of industrial control devices. To validate the effectiveness of these fingerprints, this experiment divides the existing 12 fingerprint features into hardware fingerprints and software fingerprints based on the fingerprint extraction rules from the existing literature, and analyzes the effectiveness of different types of fingerprints from four different perspectives: Allen-Bradley, Honeypot, Others, and All. The hardware fingerprints consist of two features, and, while the software fingerprints consist of the remaining ten features.  
As shown in Figure 11, the DFs proposed in this chapter, which comprehensively consider the software and hardware characteristics of industrial control devices, achieve the best experimental results on various types of anomalous data. The PLCs in the Allen-Bradley dataset are produced by the same manufacturer as those used in the oilfield station, and different models of PLCs typically exhibit certain differences in processing performance and communication performance, as well as differences in configuration logic to meet different control needs. Therefore, the hardware characteristics of PLCs from different models and scenarios are more easily distinguishable. In contrast, PLCs from the same manufacturer usually implement a unified operating system or protocol stack, resulting in smaller software differences. Consequently, hardware fingerprints perform better than software fingerprints on the Allen-Bradley dataset. Although software fingerprints may be less effective in distinguishing industrial control devices from the same manufacturer, they are more effective in differentiating industrial control devices from different manufacturers, such as the Honeypot and Others datasets in this section, as Figure 12 clearly demonstrates. Overall, hardware fingerprints consist of fewer features and are more challenging to obtain, while software fingerprints can encompass richer features and are easier to acquire, making them more advantageous for device anomaly detection.
# An Improved Few-Shot Object Detection via Feature Reweighting Method for Insulator Identification
## 3 Methodology
### Mask Generation Method Based on SKNet  
The input in the reweighted part is the stitching of the original image and the object mask. Since the mask used in the original model is the target location in the rectangular frame, the precise contour of the target cannot be located. To reduce the negative effects of background on the discriminability of the pixel feature vector of the insulator image, a selective kernel network-based attention mechanism is introduced to generate the detailed pixel-level mask adaptively.  
As shown in Figure 5, SKNet can be divided into three portions: Segmentation, Fusion, and Selection. In the segmentation part, the original input image passes two convolution kernels with sizes 3 \(\times\) 3 and 5 \(\times\) 5, respectively, to generate two feature graphs, U1 and U2. In the fusion part, firstly, the feature map is fused by element-wise summation, then the feature information is embedded by global average pooling, dimensionality is reduced by the full connection layer, and the binary classification task is completed by the Softmax activation function. Finally, two feature diagrams, S1 and S2, are generated, respectively, in the selection part, and the output feature mask is obtained by element-wise multiplication and weighting.  
Figure 4: Heat map comparison of CSPDarknet53 and Improved SCconv.  
Figure 5: SKNet structure of attention mechanism network.  
The mask used in the reweighting module of this method is generated by SKNet after training with the above method. The comparison of the generated mask is shown in Figure 6. As shown in this figure, the mask generated by the selective kernel attention mechanism network is increasingly focusing on the fine features of the insulator target region and can extract pixel-level feature information more effectively. This helps optimize the model's differentiation performance for small differences in insulators on different scales.  
Figure 6: Comparison of mask images generated before and after improvement. (**a**) Original insulator images; (**b**) The mask images generated by the original method; (**c**) The mask images generated by SKNet.
## 2 Challenges in Underwater Communications
### Channel Attenuation  
The attenuation in underwater communications is the result of absorption loss and spreading loss [12]. This, in effect, decreases the strength of the desired signals as the routing process carries on. As a result, it becomes difficult to extract the desired data from the received signal at the final destination. The attenuation in dB of an acoustic wave of frequency \(f\) in kHz at a distance \(d\) from the source is denoted by \(A(d,f)\) and is modeled by:  
\[A(d,f)=A_{o}d^{k}\alpha(f)^{d} \tag{6}\]  
where \(A_{o}\) is the normalization constant, \(\alpha\) is the absorption coefficient and \(k\) is the spreading factor. The geometry in which an acoustic wave spreads away from the source determines the value of \(k\). Cylindrical and spherical spreading geometries are represented by \(k=1\) and \(k=2\), respectively. In practice, \(k=1.5\)[12].
## 5 Water Treatment Employing Bioadsorbents
### Bioadsorbents Based on Fruits and Vegetables
#### 5.2.2 Industrial Contaminants
Orange peels were tested as an effective bioadsorbent (87% removal) for violet 17 at pH 2 and showed the ability to recover more than 60% of the adsorbate with increasing pH [95]. Procion orange, direct red 23, and direct red 80 were effectively adsorbed by non-treated orange peels with a maximum removal efficiency of 49% [80], 92%, and 91% respectively, at initial dye concentrations of 50 mg/L [81]. Langmuir showed a better fit in most cases, indicating monolayer adsorption [10]. Rhodamine-B was adsorbed by orange peels as well, displaying a removal efficiency and q\({}_{\text{m}}\) of 63.5% and 3.2 mg/g, respectively, while calcined coir pith wastes removed 43.6-79.4% of this dye [47]. Additionally, a maximum adsorption capacity of 13.2 mg/g for rhodamine-B was achieved with banana peels at pH 7, following a Freundlich adsorption behavior [67]. Grapefruit peels were an excellent adsorbent for crystal violet, with higher desorption efficiency (\(\leq\)98%) after several cycles of regeneration with NaOH. Sorption equilibrium was reached within 60 min, resulting in 96% removal of the dye [75]. Durian peels showed an important adsorption capacity of 63.2 mg/g for acid green 25 at 30 \({}^{\circ}\)C; the good adjustment of the Langmuir model indicated that this biomaterial had equal adsorption activation energy for each dye molecule [74].
## 3 Optimization Method
### Optimization Algorithm  
The non-dominated sorting genetic algorithm-II (NSGA-II) is employed to search for the best solutions for the optimization objectives, whose solving procedure is illustrated in Figure 3. The NSGA-II is improved by three approaches compared with NSGA-I. First of all, the use of fast nondominated sorting reduces the complexity of computation and combines the parent population with the offspring population, so that the next generation population is selected from both of the two generations, and the best individuals are all retained. Secondly, elitist preserving ensures that some excellent individuals will not be discarded in the process of evolution, thus the accuracy of the optimization results is improved. Thirdly, crowding distance is employed as the comparison standard among individuals in the population and, as a result, the individuals can be evenly extended to the whole Pareto domain, ensuring the diversity of the population [30]. Moreover, detailed codes for this solving procedure are presented in the Supplementary Materials.  
In solving a multi-objective optimization problem, the result is usually not a single global optimum solution which optimizes every objective function simultaneously. Generally, the Pareto frontier is employed to show the distribution of these multi-objective optimization solutions. These solutions can also be called Pareto optimal solutions. To be specific, the Pareto frontier for the plate-fin heat exchanger optimization consists of a horizontal axis of heat transfer area and a vertical axis of pressure drop. Besides, for a given Pareto optimal solution, there is no other solution which satisfies every objective function better than it in variable space, also called the non-dominated solution [31]. In this paper, the NSGA-II is implemented in the platform of MATLAB.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Value** \\ \hline Mass flow rate of exhaust (kg/s) & 21.77 \\ Mass flow rate of thermal oil (kg/s) & 13.07 \\ Inlet pressure of exhaust (kPa) & 105.5 \\ Inlet temperature of exhaust (K) & 783 \\ Inlet temperature of thermal oil (K) & 397 \\ Outlet temperature of thermal oil (K) & 673 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Thermodynamic conditions for plate-fin heat exchanger inlet and outlet.
# GlassBoost: A Lightweight and Explainable Classification Framework for Tabular Datasets
## 6 Discussion
\begin{table}
\begin{tabular}{l c} \hline \hline
**XAI Model** & **Accuracy** \\ \hline
**GlassBoost (maximum depth = 4, \(d=8\))** & **0.9835** \\ Random Forest & 0.9852 \\ Gradient Boosting & 0.9907 \\ Rule-based & 0.9728 \\ KNN & 0.9785 \\ Linear regression & 0.9635 \\ SVM & 0.9868 \\ Adaboost & 0.9889 \\ XGBoost & 0.9905 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the accuracy of GlassBoost with XAI models introduced in [37]. ing time is proportional to \(N(D+1)(\log_{2}N-\frac{D}{2})\), and the evaluation time is proportional to \(N(D+1)\), where \(N\) is the number of features. Our approach significantly reduces the number of input features: for example, in the CyberSecurity dataset, we reduced 62 features to at most 8 while maintaining strong performance using a decision tree of depth 4. Based on this empirical evidence and the theoretical facts from [2], we assert that GlassBoost qualifies as a lightweight and efficient model well-suited for resource-constrained environments. It is also worth noting that the training phase, although computationally more intensive, is performed only once. Subsequently, the evaluation of new samples is highly efficient, as the resulting model is a shallow decision tree with minimal inference overhead. Additionally, GlassBoost can quickly adapt to data or concept drift as long as these shifts do not substantially alter the set of important features. However, if the underlying data distribution changes to the extent that the key features lose their relevance, a new feature set must be identified. This can be accomplished by reapplying XGBoost to updated datasets that capture evolving data patterns. Finally, GlassBoost can be applied to many datasets, provided they include tabular data. While this study focused on intrusion detection, the approach broadly applies to any classification or detection task where a balance between explainability and performance is desired. It is particularly well-suited to domains that require interpretable decision-making pipelines, such as healthcare, finance, and IoT, where insight into model behaviour is as important as accuracy. Moreover, unlike most methods that only quantify the contribution of each feature to the model's final decision, GlassBoost goes a step further. It determines feature importance and provides a transparent, rule-based explanation of the entire model using simple IF-THEN rules, enhancing interpretability and trustworthiness.
## 3 Proposal Transmission Structure for an IP-Based Terrestrial Broadcasting System  
### Resource Efficiency and Transmission Delay in Broadcasting Systems  
In terrestrial broadcasting systems, transmission delay can occur when transmitting media as follows: (i) creating original media by shooting, (ii) compressing the generated original media, (iii) transmitting compressed media through a physical link, (iv) wirelessly transmitting media over a physical link, and (v) receiving media and playback over the user's terminal. This paper does not discuss (i), (ii), and (v) because these are not delayed occurrences due to media transmission.  
Item (iv) is a physical link, where the resources are fixed, and the delay is constant. The delay occurring in (iv) consists of the transmission delay and propagation delay. The transmission delay is determined by the bandwidth and the volume of transmitted data. For example, 1 Mbyte of data transmitted through a 1 Mbps bandwidth has a transmission delay of 8 s. The propagation delay is determined by the signal speed and the physical distance between the transmitter and receiver but is a very short time for terrestrial waves.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Broadcast** & \multicolumn{2}{c}{**ATSC**} & \multirow{2}{*}{**DVB-T**} & \multirow{2}{*}{**DVB-T**} & \multirow{2}{*}{**DVB-H**} & \multirow{2}{*}{**ISDB-T**} & \multirow{2}{*}{**DTMB**} \\ \cline{2-2}
**Network** & & & & & & & \\ \cline{2-2} \cline{6-7}  & \multirow{2}{*}{19.39} & 0.029 to & 3.7 to & 7.4 to & 3.7 to & 3.651 to & 4.81 to \\  & & 21.5 & 31.7 & 50.3 & 23.751 & 23.234 & 32.49 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Capacity of broadcast network at 6 Mhz (Mbit/s) [22].  
For radio signal propagation at 3 \(\times\) 10\({}^{8}\) m/s, a propagation delay of 1 ms occurs when sending the signal to a receiver 30 km away.  
Therefore, the delay and resource efficiency occurring in (iii) are analyzed in this paper. As mentioned, because the delay occurring in (iv) is constant, only the delay in (iii) must be analyzed. The resources that can be allocated in (iii) cannot exceed the resources in (iv). In addition, because (iv) plays a role in transmitting the media in (iii) as it is, the efficiency of resources generated in (iii) is the same as the efficiency of resources generated in (iv).
## 2 Data and Methods
### Statistical Mono-Window Model  
The third LST model we tested is an SMW model. SMWs consist of empirical approaches that relate TOA brightness temperatures of a single atmospheric window channel to LST [16,22,25], generally via a simple linear regression. Here, we linearized the radiative transfer equation, while at the same time maintaining an explicit dependency on surface emissivity:  
\[\mathrm{T_{s}}=A\ \frac{\mathrm{T_{b}(\theta)}}{\varepsilon_{c}}+B\frac{1}{ \varepsilon_{c}}+C \tag{4}\]  
where Tb is the TOA brightness temperature in channel c and \(\varepsilon_{c}\) stands for the corresponding spectral surface emissivity.  
We estimated the regression coefficients A, B and C for different classes of TCWV and VZA. Following [PERSON] _et al._[12] for the operational LSA SAF GSW model and [PERSON] _et al._[22] for a single-channel LST model for the GOES satellite series, the calibration/validation of Equation (4) made use of synthetic radiances obtained with the radiative transfer model MODTRAN 4.0. We selected MODTRAN and not RTTOV to tune the SMW model, as we assume the line-by-line MODTRAN model to be slightly more accurate than the \"broad band\" RTTOV model. In contrast to the PMW model, the processing speed of the radiative transfer model is more or less irrelevant for SMW, as the radiative transfer simulations are only computed once to establish the model coefficients.  
MODTRAN simulations were performed for a range of clear sky atmospheric profiles and surface variables representative for global conditions [32]. The synthetic radiances were split into two subsets: (1) A training dataset for determining the statistical mono-window coefficients (Equation 4); and (2) an independent dataset for model verifications. The training dataset comprises 116 carefully-chosen profiles to encompass the bivariate distribution of TCWV and LST. A total of over 845,000 simulations was obtained by varying the viewing geometry and surface conditions for each profile over the following ranges: (1) VZA from 0\({}^{\circ}\) to 75\({}^{\circ}\); (2) surface emissivity between 0.926 and 0.998; and (3) surface temperatures ranging from near surface air temperature minus 15 K to near surface air temperature plus 15 K. Following the approach of [PERSON] _et al._[22], coefficients A, B and C in Equation (4) were then determined for 8 different TCWV classes (0 cm-6 cm in steps of 7.5 mm) and 15 VZA classes (0\({}^{\circ}\)-75\({}^{\circ}\) in steps of 5\({}^{\circ}\)).  
We applied the above described SMW model to the extracted SEVIRI 10.8-\(\upmu\)m clear-sky TOA brightness temperature time series. As for the PMW and GSW model, surface emissivities (\(\varepsilon_{c}\)) for the SMW are taken from the operational LSA SAF dataset [26].
## 2 Materials and Methods
### Inversion of LIDFa and \(V_{cmo}\) from In Situ Measurements
#### 2.3.2 V\({}_{\text{cmo}}\) Inversion from Diurnal GPP Observations  
The V\({}_{\text{cmo}}\) values across the growing season were retrieved from the diurnal observations by inverting the SCOPE model with the LUT method. V\({}_{\text{cmo}}\) is a crucial leaf biochemical parameter for calculating photosynthesis and fluorescence emission in the SCOPE model. It changes with different vegetation types [59; 60], plant functional types [61], and different days of the year [62]. According to [25], V\({}_{\text{cmo}}\) influences carbon assimilation of photosynthesis (i.e., PQ) and thus fluorescence emission efficiency. Therefore, V\({}_{\text{cmo}}\) may be estimated from varying net CO\({}_{2}\) fluxes. [PERSON] et al. have successfully estimated the V\({}_{\text{cmo}}\) by fitting a commonly used model to measured net CO\({}_{2}\) fluxes [63]. In our study, the simulated GPP can represent the net CO\({}_{2}\) fluxes, because the respiration rate is set to zero. Thus, the V\({}_{\text{cmo}}\) values were retrieved by comparing simulated against observed GPP. The inversion of V\({}_{\text{cmo}}\) intends to find the optimal V\({}_{\text{cmo}}\) value for each fieldwork day (hereafter denoted as the daily V\({}_{\text{cmo}}\)). The schematic overview of V\({}_{\text{cmo}}\) inversion is shown in Figure 3.  
Figure 3: Schematic overview of the V\({}_{\text{cmo}}\) inversion procedure.  
The daily V\({}_{\rm{cmo}}\) was retrieved using the least RMSE method, which intends to find the diurnal GPP values that are most consistent with the measured ones. With the inverted daily LIDFa as inputs, SCOPE was run with an LUT with different V\({}_{\rm{cmo}}\) values at half-hour time intervals for each fieldwork day in the 2015-2016 period. Based on the literature [59; 61], the range of V\({}_{\rm{cmo}}\) should be set to 10-200 \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\) for C3 crops like wheat with a step 10 \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\). Thus, for every fieldwork day, 20 sets of half-hour GPP data under 20 V\({}_{\rm{cmo}}\) conditions could be collected from the SCOPE model outputs. Next, for the \(j\)-th V\({}_{\rm{cmo}}\) value, we calculated the RMSEs between the simulated half-hour GPP values \(GPP_{\rm{sim}}\) with the measured ones \(GPP_{\rm{mea}}\), as shown in Equation (5)  
\[RMSE_{\rm{G}}(j)=\frac{\sum\limits_{m=1}^{M}|GPP_{\rm{sim}}(j,m)-GPP_{\rm{ mea}}(m)|}{M} \tag{5}\]  
in which \(m\) indicates the \(m\)-th measurement of GPP during one fieldwork day, and \(M\) is the total measurement times. Similar to the LIDFa inversion, the V\({}_{\rm{cmo}}\) that produces the least RMSE was selected as the inverted daily V\({}_{\rm{cmo}}\) for model input.