esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhanced Collaborative Filtering for Personalized E-Government Recommendation
## 3 Proposed Negative Mixing CF
### Negative Mixing  
For an interaction \((u,i)\) and its selected interaction \((u^{\prime},i^{\prime})\in H^{+}_{(u,i)}\), we denote their latent feature vectors as \((e_{u},e_{i})\) and \((e_{u^{-}},e_{i^{-}})\), respectively. Inspired by the mixup [37], we introduce the idea of negative mixing to inject negative information \(e_{i^{-}}\) into positive embeddings. Specially, we compute the composite item feature \(e^{\prime}_{i}\) by a linear combination of \(e_{i}\) and its selected negative item feature \(e_{i^{-}}\)  
\[e^{\prime}_{i}=e_{i}+s\big{(}e_{i},e_{u^{-}}\big{)}e_{i^{-}} \tag{5}\]  
where score \(s\big{(}e_{i},e_{u^{-}}\big{)}\) is the decay factor of the selected item, which evaluates how much information is propagated from \(e_{i^{-}}\) to \(e_{i}\) conditioned to their mutually interactive user latent feature \(e_{u^{-}}\). Thus, decay factor \(s\big{(}e_{i},e_{u^{-}}\big{)}\) should contain the collaborative information between positive item latent vector \(e_{i}\) and user latent vector \(e_{u^{-}}\). Generally, the inner product can be more collaboratively informative than the connection. The calculation formula is  
\[s\big{(}e_{i},e_{u^{-}}\big{)}=\big{\langle}e_{i},e_{u^{-}}\big{\rangle} \tag{6}\]  
The expressiveness of the item latent feature has been enhanced because of the mix of the selected information. Taking \((i_{2}\to u_{2}\to i_{1}\to u_{1})\) or \((i_{2},u_{2})\to(i_{1},u_{1})\) as an example, \(i_{2}\) propagates its latent feature information to \(i_{1}\) via \(u_{2}\), which is beneficial for better estimation of \(u_{1}\)'s preferences. The interaction \((u_{2},i_{2})\in H_{(u,i)}\) is selected to provide implied information for positive interaction \((u_{1},i_{1})\). A high collaborative filtering score of \((u_{2},i_{1})\) suggests that more latent feature \(i_{2}\) can provide. The process is necessary for the binary rating data of e-government, which loses explicit preference expression. The mixed information introduces a deeper interaction to enhance the abstract expression of connected interactions.  
Figure 4: Information selection strategy with distance constraint.
# Registration of Dental Tomographic Volume Data and Scan Surface Data Using Dynamic Segmentation
## 1 Introduction  
In the dental computer-aided design and computer-aided manufacturing (CAD/CAM) industry, volumetric computed tomography (CT) images and scan surfaces are most commonly used. However, the two data types are very different, because their measurement techniques fundamentally differ. The volume data contains intensity information of the internal organs of the human body, while the surface data contains only the visible surfaces, that is, the teeth and the gingiva. Because of their different features, volume data and surface data are used for different dental applications. However, there are many applications which require both volume data and surface data and for the accurate registration of the volume and surface data is necessary. To achieve this we propose a novel registration method of volume data and surface data.
## 6 Conclusions  
In this study, a multistage computational model \(DETECT\)-\(LC\) is proposed for lung cancer tumor phenotyping and staging based on 3D CT volumes. \(DETECT\)-\(LC\) handles the challenge of choosing discriminative CT slices for constructing 3D volumes. Haralick radiomics and k-means clustering are used for this purpose. Then, ALT-CNN-DENSE _Net_ is developed for distinguishing the pathology and stage classes. For phenotyping, \(DETECT\)-\(LC\) gets a minimum accuracy of 0.92, sensitivity of 0.87, F1-score of 0.91 and AuC of 0.88 with the smallest dataset NSCLC Radiomics-Genomics. Similarly for staging,  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model** & **Acc** & **Sn** & **F1-Score** & **AUC** \\ \hline [PERSON] et al. [22] & 0.87 & NA & NA & NA \\ [PERSON] et al. [20] & 0.86 & 0.80 & NA & 0.82 \\ [PERSON] et al. [21] & 0.91 & 0.77 & 0.80 & 0.85 \\ \(DETECT\)-\(LC_{avg}\) & 0.94 & 0.88 & 0.94 & 0.81 \\ \hline \hline \end{tabular}
\end{table}
Table 11: State of the Art Staging Results on TCIA NSCLC Datasets.  
Figure 14: Summary of Staging results presenting confusion matrices and learning curves on three datasets (**a**) Radiomics (**b**) Radiomics Genomics (**c**) Radio Genomics. The learning curves present mean square error (mse) for training and testing and Accuracy for training and testing splits.  
the least results are obtained with NSCLC Radiomics-Genomics dataset with values 0.91, 0.88, 0.95 and 0.85 for Acc, Sn, F1-score and AuC, respectively. \(DETECT\)-\(LC\) shows a robust consistent performance across the three experimented TCIA NSCLC datasets with minor differences in performance. Also, the performance assessment conveyed the capacity of the pipeline to classify small highly imbalanced datasets exceeding the performance of the state of the art. Generally, \(DETECT\)-\(LC\) is shown to have superior performance relative to various similar solutions. Hence, \(DETECT\)-\(LC\) can provide an ample solution to different recognition tasks. As future work, data integration between different organs' CT will be considered to detect metastasis. Also, the staging study can be enlarged to include Stage IV. Also, other data types can be integrated with CT, such as genomes, in order to build a Radio-Genomic model and improve the diagnosis results. In addition, the model can be embedded in a user-friendly desktop application to aid doctors and non-medical experts in analyzing Lung CTs to expand its usability.  
Conceptualization of this study, K.M.F., S.M.Y. and N.M.; methodology, K.M.F., S.M.Y. and N.M.; software, N.M.; validation, K.M.F., S.M.Y. and N.M.; formal analysis, N.M.; investigation, N.M.; resources, K.M.F. and N.M.; writing--original draft preparation, N.M.; writing--review and editing, K.M.F. and S.M.Y.; supervision, K.M.F. and S.M.Y.; project administration K.M.F. and S.M.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The following abbreviations are used in this manuscript:  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:
## 3 Quantifying Transmission Reserve Requirements in Real-Time Dispatch  
### Basic Real-Time Dispatch Model with Corrective Measures  
As is observed in the previous section, the congestion constraint (18) in the risk-averse SUC formulation proposed is dependent on the key statistical features of the line flows in real-time operation, i.e., the mean and standard deviation of line flows, \(\overline{P}_{l}^{t}\) and \(\sigma_{P_{l}^{t}}\), respectively, which can be obtained by solving the P-OPF sub-problem. The uncertainties in the problem are generally represented by a set of scenarios \(s\in\mathcal{S}\) using the crude MCS, where the two key statistics of line flows are expressed as  
\[\overline{P}_{l}^{t}=\frac{1}{N_{s}}{\sum}_{s\in\mathcal{S}}\;\hat{P}_{l}^{t,s} \tag{20}\]  
\[\sigma_{P_{l}^{t}}^{2}=\frac{1}{N_{s}-1}{\sum}_{s\in\mathcal{S}}\left(\hat{P }_{l}^{t,s}\right)^{2}-\left(\overline{P}_{l}^{t}\right)^{2} \tag{21}\]  
where \(N_{s}\) is the number of Monte Carlo (MC) scenarios of set \(S\). The superscript \(s\) denotes the index for the scenario of random variables, \(s=1,2,\cdots,N_{s}\). Other constraints in the real-time dispatch model are given as follows:  
\[P_{g}^{\min}x_{g}^{t}\leq\hat{P}_{g}^{t,s}\leq P_{g}^{\max}x_{g}^{t},\;g\in \mathcal{G},\;t\in\mathcal{T},\,s\in\mathcal{S} \tag{22}\]  
\[{\sum}_{g\in\mathcal{G}}\;\hat{P}_{g}^{t,s}={\sum}_{n\in\mathcal{N}}\Big{(} \hat{W}_{n}^{t,s}-W_{n,cr}^{t,s}-D_{n}^{t,s}+D_{n,ls}^{t,s}\Big{)},\;t\in \mathcal{T},\,s\in\mathcal{S} \tag{23}\]  
\[\hat{P}_{l}^{t,s}={\sum}_{m\in\mathcal{N}}\;S_{l,n}\Big{(}{\sum}_{g\in \mathcal{G}_{n}}\;\hat{P}_{g}^{t,s}+\hat{W}_{n}^{t,s}-W_{n,cr}^{t,s}-D_{n}^{t,s}+D_{n,ls}^{t,s}\Big{)},\;t\in\mathcal{T},\,l\in\mathcal{L} \tag{24}\]  
\[-P_{l}^{\max}\leq\hat{P}_{l}^{t,s}\leq P_{l}^{\max},\;l\in\mathcal{L},t\in \mathcal{T},\,s\in\mathcal{S} \tag{25}\]  
\[W_{n,cr}^{t,s}\geq 0,\;D_{n,ls}^{t,s}\geq 0,\;n\in\mathcal{N},\;t\in \mathcal{T},\,s\in\mathcal{S} \tag{26}\]  
Equations (22)-(26) represent, respectively, the generation capacity, power balance and line flow limits in the real-time dispatch, with the consideration of remedial measures including wind curtailment and load shedding. The two remedies used are incorporated in both the constraints and the objective function of the real-time dispatch to recover the solution from infeasibility, which is a main difference from the conventional P-OPF. The procedure of conducting the proposed risk-averse SUC is depicted with the flowchart shown in Figure 1.  
However, the execution time involved to conduct the above optimization problem could be undoubtedly large, due to a significant number of MC scenarios required to reach an acceptable accuracy. A more computationally efficient method, namely PCM, is developed herein, and its extended version that is adaptable to high-dimensional and correlated cases will be covered in the following subsection.
# Heavy Metals in Post-Exploitation Reservoirs--The Bagry Lake Case Study (Poland)
###### Abstract  
The paper presents the research study on the concentration of Fe, Mn, Cu, Pb, Zn, Cd and Ca in the sediment as well as in the supernatant (layer of water just above the sediment) of the Bagry Lake, Cracow (Poland); Ca was not included in the supernatant analysis. There are a number of works in the literature on the concentrations of metals in the water environment of flow-through dam reservoirs and factors influencing their distribution. These reservoirs were created for various purposes, e.g., water supply, flood protection, leveling the flows in the rivers and recreational purposes. Since it is difficult to find information on metals and factors influencing their distribution in the aquatic environment of post-exploitation reservoirs, this work aims to fill this gap. The additional purpose of the work was to assess the potential influence of metals' presence in the sediment of Bagry Lake on benthic organisms. Correlations between metals in the sediment and their volatile and mineral fractions as well as in particle size fractions of \(\geq\)0.06 mm and \(<\)0.06 mm were determined. Except for cadmium, metals showed a positive correlation with the sediment volatile fraction of granulometry \(<\)0.06 mm (a clay-silty fraction). It has been shown that metals, in both supernatant and sediment, are of an anthropogenic origin related to previous on-take operations, as well as a nearby modern technical infrastructure. Statistical models were used to measure the strength of relationships between the concentration of various metals in the sediments. A cluster analysis was used to study interactions between the concentration of metals in supernatant, in sediment and in its volatile and mineral fractions as well as in fractions with different granulation. It was found that individual metals and sediment fractions form pairs of quite strong clusters, e.g., Fe sediments and Mn sediments, Fe supernatant and Mn supernatant, Cu supernatant and Pb supernatant, volatile fraction and mass fraction of particles \(<\) 0.06 mm and mineral fraction and mass fraction of particles \(\geq\) 0.06 mm. The equilibrium model \"supernatant--sediment mineral fraction--sediment volatile fraction\" was developed for metals in various granulometric fractions of the sediment as well as in the supernatant. The model estimated a metal concentration in both fractions of the sediment (separately) as well as an impact they had on the metal concentration in the supernatant. It also showed how changes in a metal's concentration in both fractions are reflected in concentrations of metals in the aquatic environment.  
heavy metals; reservoir; sediment; supernatant; absorption model; cluster analysis 2023
# Degree-Constrained Minimum Spanning Hierarchies in Graphs
## 5 Algorithm Design
### A Heuristic
#### 5.2.4 The Connection of the Hierarchies  
Let us suppose, that the spanning hierarchy \(H_{c}\) of \(S\) with the central node \(c\) must be connected to the hierarchy \(H_{p}\) spanning the parent star \(S^{\prime}\) with the central node \(p\). The edge \(\{p,c\}\) is used \(dp(c)\leq D(c)\) times in \(S^{\prime}\). It is the number of adjacent edges of \(c\) in \(H_{p}\). Moreover, \(c\) has \(d_{c}(c)>0\) adjacent edges in \(H_{c}\). Finally, \(d_{p}(c)+d_{c}(c)\) neighbor edges must be connected to \(c\). If \(d_{p}(c)+d_{c}(c)\leq D(c)\), a unique occurrence of \(c\) is sufficient for the connections. If \(d_{p}(c)<D(c)\), the first occurrence of \(c\) ensures the \(d_{p}(c)\) connections to the parent node \(p\) and \(D(c)-d_{p}(c)-1\) edges in \(H_{c}\). The remaining edges in \(H_{c}\) must be connected to the next occurrences of \(c\) in the defined order. If \(d_{p}(c)=D(c)\), the first occurrence of \(c\) can have only \(D(c)-1\) times occurrences of \(\{p,c\}\) as adjacent edges and the last occurrence of \(\{p,c\}\) is connected to the second occurrence of \(c\), etc. In the example of Figure 7, \(D(c)=3\) and \(d_{p}(c)=2\). The first occurrence of \(c\) is connected twice to \(S_{p}\) (these connections are indicated with dashed lines).  
As stated before, the heuristic does not work in cases where the conditions in Theorem 1 are not met in a star. This means that the proposed heuristic does not work in these cases, even if an eventual degree-constrained spanning hierarchy exists in the MSTSL.
## 3 Overview of LoRa and Zigbee
### Zigbee  
Zigbee is a low-cost, low-power wireless IoT network technology that was designed as an open worldwide standard. The IEEE 802.15.4 physical radio specification underpins the Zigbee standard, which runs in unlicensed bands such as 2.4 GHz, 900 MHz, and 868 MHz. Due to the benefits of Zigbee technology, such as low cost and low power operating modes and topologies, this short-range communication technology is best suited for a variety of  
Figure 2: LoRa Frame Format.  
applications as compared to other proprietary communications, such as Bluetooth and Wi-Fi as shown in Figure 3.  
The Zigbee protocol architecture, as shown in Figure 4, is made up of a stack of levels, with physical and MAC levels defining IEEE 802.15.4 and Zigbee's network and application levels completing the protocol.  
_The physical layer (PHY)_ deals with various parameters such as output power, several channels, receive sensitivity, chirp modulation, channel rejection, and transmission. When transmitting and receiving signals, this layer performs modulation and demodulation processes. The majority of Zigbee applications run at a data rate of 250 kb/s in the 2.4 GHz ISM band. In the _Medium access control (MAC)_, the radio frequency (RF) data exchanges between nearby devices are managed (point to point). Transmission retry and acknowledgment management, as well as collision avoidance strategies, are all part of the MAC (CSMA-CA). _The network layer_ supervises all network-related operations. Routing features are added, allowing RF data packets to travel via numerous devices (many hops) on their way from source to destination (peer to peer).
## 3 Results and Discussion
### Effect of Contact Time  
As shown in Figure 5b, the adsorption amount (\(Q_{t}\), mg/g) on Mag-PVP increased with an increase in time at the initial stage and nearly reached a plateau after approximately 10 min. The high adsorption rate may be caused by the following factors: the Mag-PVP diameter was very small (approximately 160 nm) and the adsorption property of Mag-PVP was enhanced through the grafting modification of Fe\({}_{3}\)O\({}_{4}\) nanospheres with DVB and 2-VP. Mag-PVP reached the adsorption capacity of 33.3 mg/g in 10 mg/L of BPA solution. The fast adsorption rate and high adsorption capacity demonstrated that Mag-PVP was an efficient adsorbent for the adsorption of BPA.
## 4 Experimental Results
### Results  
Experiments were conducted on the developed CNN model in two different ways. In the first CNN model trial, five (Adware, Banking, SMS, Riskware, and Benign) categories were classified in the output layer. According to the classification with the CNN model with five categories, a weighted average Accuracy of 89%, Precision of 89%, Recall of 89%, and F1-score of 89% were obtained. In the second trial, the CNN model was tested as two outputs (Benign and Malware) and the results showed a weighted average Accuracy of 91%, Precision of 90%, Recall of 91%, and F1-score of 89%. In order to compare the success of the study, classification was performed with ResNet50, DenseNet121, and MobileNetV2 CNN models and the obtained results are shown in Table 8.  
As can be seen in Table 8, the best obtained results were with the CNN model we developed. The closest obtained results to the developed model were with the ResNet50 model with an Accuracy of 88%, an F1-Score of 88%, a Precision of 88%, a Recall of 88%,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Name** & **Accuracy (\%)** & **F1-Score (\%)** & **Precision (\%)** & **Recall (\%)** \\ \hline Our model with 5 outputs & 89 & 89 & 89 & 89 \\ Our model with 2 outputs & 91 & 89 & 90 & 91 \\ DenseNet121 with 2 outputs & 79 & 80 & 88 & 79 \\ MobileNetV2 with 2 outputs & 76 & 66 & 82 & 76 \\ ResNet50 with 2 outputs & 88 & 88 & 88 & 88 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Experiment results of our models and ResNet50, DenseNet121, and MobileNetV2 models.  
The second closest results were obtained with the DenseNet121 model, with an Accuracy of 79%, an F1-Score of 80%, a Precision of 88%, and a Recall of 79%. The worst results were obtained with the MobileNetV2 model: an Accuracy of 76%, an F1-Score of 66%, a Precision of 82%, and a Recall of 76%.
## 7 Sensor Data Fusion Based on TCPF
### Association of Stratified Sampling with Sensor Confidence  
In order to further improve the fusion filtering accuracy of the algorithm, a new design was made for the hierarchical sampling step of the TCPF algorithm in this paper. The confidence factor \(\varsigma_{i}^{\text{ZGB}}(k)\) of Equation (30) was associated with the hierarchical sampling of the TCPF algorithm, and a new weight optimization equation was obtained. The specific steps are as follows:  
Step 1 substitute the confidence factor into the calculation of the weight optimization.  
\[\psi^{\prime}_{i}(k)=\frac{\varsigma_{i}^{\text{ZGB}}(k)-1}{\varsigma_{i}^{ \text{ZGB}}(k)}\widetilde{\omega_{i}}(k)+\varsigma_{i}^{\text{ZGB}}(k)\cdot \alpha(k),\widetilde{\omega_{i}}(k)<\alpha(k) \tag{63}\]  
Step 2 the data samples are stratified according to the weights of multi-sensor sampling particles after optimized combination.  
\[\begin{cases}\psi^{\prime}_{i}(k)=\frac{\varsigma_{i}^{\text{ZGB}}(k)-1}{ \varsigma_{i}^{\text{ZGB}}(k)}\widetilde{\omega_{i}}(k)+\varsigma_{i}^{\text{ ZGB}}(k)\cdot\alpha(k),&\text{if }\widetilde{\omega_{i}}(k)<\alpha(k),\\ \psi^{\prime}_{i}(k)=\widetilde{\omega_{i}}(k),&\text{Divide particles into }l_{b}\text{ layer}\\ \end{cases} \tag{64}\]  
The confidence factor of the positioning data was associated with the stratified sampling in the TCPF algorithm, so that the sensor positioning data with a large confidence factor would be sampled first when the TCPF algorithm was used to sample and fuse the positioning data, which would improve the sampling efficiency of the TCPF algorithm. The larger the confidence factor data, the higher the credibility, which improved the reference value of samples from sensor information fusion, further improved the fusion filtering accuracy of the TCPF algorithm, and finally realized the precise positioning of the unmanned ship.
# Compact Broadband Antenna with Vicsek Fractal Slots for WLAN and WiMAX Applications
## 2 Antenna Geometry Design
### Parametric Analysis of the Suggested Antenna
#### 2.3.1 The Feed Line Length \(L_{f}\) and Width \(W_{f}\) Variation Effects  
The effect of the microstrip feed line length \(L_{f}\) on the suggested antenna performance is studied by changing the \(L_{f}\) from 13 to 15 mm and the feed line width from 2.6 mm to 3.5 mm. Figure 5 illustrates the variation of \(S_{11}\) versus frequency for various lengths and widths of the feed line, \(L_{f}\) and \(W_{f}\). It can be noticed from the figure that by increasing the length of the feed line, the bandwidth increases from 3.8 GHz to 4.35 GHz. The simulations confirm that to obtain good impedance matching and the desired broadband, the optimal values for the length and width of the feed line are \(L_{f}=15\) mm and \(W_{f}=3.5\) mm.
# Path Planning for Localization of Radiation Sources Based on Principal Component Analysis
## 3 Experiments
### Simulations
#### 3.1.4 Performance Comparison  
To evaluate the effectiveness of the proposed method, a performance comparison was conducted with an information-driven search and the uniform search methods described in [8]. These methods are based on the use of the GM counter as a gamma detector. The  
Figure 15: Simulation results of additional investigation: (**a**) The result obtained when the threshold was set to 1000 (**b**) The result obtained when the threshold was set as 3000. The black line depicts the determined path when the threshold was set to 1000, and the red line is the path with the threshold set to 3000. It was confirmed that the area around the sources was clearly divided through the additional investigation.  
simulation condition was determined as being the same as that in [8]. A single source was placed in a 100 m \(\times\) 100 m rectangular search area. The source strength was 5.7 MBq. The distance between measurement points was assumed to be 15 m, and the threshold for switching the principal component vector was set to 30.  
Figure 16 presents the simulation results with the proposed method. The red dot is the radiation source, and the black line represents the determined path. It was confirmed that the robot accurately estimated the direction of the existing source despite the large area. The robot approached the source and measured gamma rays while circumnavigating the source. Table 1 shows a performance comparison with previous methods.  
Note that the localization errors of the previous methods are the average errors over 50 Monte Carlo runs. Conversely, the error of the proposed method is the result of a single trial. It was confirmed that the proposed method achieves better localization accuracy with fewer measurement points than the previous methods do. However, the proposed method counted at each location for a longer duration overall. Therefore, the need for fewer measurement points is advantageous if it is time-consuming for the robot to navigate between points.