esa-sceva/satcom-chunk-collection huggingface dataset:
# On Combining Feature Selection and Over-Sampling Techniques for Breast Cancer Prediction
## 4 Experimental Results  
### The KDD Cup 2008 Breast Cancer Dataset  
Figure 1 shows the AUC (area under the ROC curve) rates of different approaches. In addition, Figure 2 shows the type I errors of the different approaches, which represent the error of miss-classifying the malignant cases into the benign class. Note that IG+SMOTE and GA+SMOTE mean the combination order of performing feature selection first and over-sampling second, whereas SMOTE+IG and SMOTE+GA represent the opposite combination order. In addition, the baseline represents using the original training set without performing any feature selection or over-sampling steps to train the SVM classifier.  
Figure 1: AUC rates of different approaches over the KDD Cup 2008 breast cancer dataset.  
As we can see, the combinations of feature selection and over-sampling can allow the SVM to provide higher AUC rates and related lower type I errors than the ones with feature selection and over-sampling alone at the baseline. More specifically, the combination order of performing feature selection first and over-sampling second outperforms the opposite combination order. In particular, IG+SMOTE is the best combined approach, which causes the SVM to provide an AUC rate of 0.788 and a type I error rate of 0.003, which significantly outperforms the others (\(p<0.05\)). On the other hand, for the feature reduction result, using IG and GA produce the selection of 94 and 14, respectively.
# Passive Indoor People Counting by Bluetooth Signal Deformation Analysis with Deep Learning
## 3 BLE-Based Passive Human Sensing
### Data Gathering and Pre-Processing  
At each receiving node, \(n_{t}\) sample queues are managed (see Figure 4). Each sample refers to an advertisement frame transmitted by a beacon and received by the receiving node and contains a reception timestamp, the identifier of the transmitter (the beacon), the identifier of the receiving node, and the RSSI value measured upon reception.  
Since the transmitter and receiver are not time-synchronized, it is not feasible to leverage the phase shift of multiple messages with identical timestamps to enhance spatial resolution [31]. As a result, this work prioritizes robustness to noise over temporal resolu  
Figure 4: Diagram of the data processing workflow at a generic receiver \(r\). For each transmitter, the incoming samples are grouped into sets of \(k\). From each group, a representative sample is selected as the ”median”of the \(k\) values, and it retains its original timestamp. The notation \(\sim\)\(t_{j}\) indicates that the samples within each group are collected at times approximately equal to \(t_{j}\), meaning they share closely aligned timestamps.  
tion (i.e., the PHS sampling frequency). To achieve this, we aggregate the RSSI values from \(k\) messages, each transmitted by the same beacon \(i\) and received by receiver \(r\), that share closely aligned timestamps around time \(t\).  
This aggregation is performed by calculating the median of the \(k\) RSSI values, which we refer to as the Median RSSI Sample received by the receiver \(r\) at time \(t\) (\(MRS_{r,i}(t)\)).  
This procedure significantly mitigates the impact of abrupt, undesired fluctuations in RSSI, such as those caused by human movement, but it also reduces the temporal resolution by a factor of \(k\).  
At each receiver, and for every time instant \(j\) at which a message is received, the median RSSI values from different beacons that share similar timestamps are compiled into a single row, labeled with the representative timestamp \(j\). As a result, each receiver \(r\) maintains a list, denoted as \(MRSL_{r}\), where each row corresponds to a specific time instant \(j\) and contains a sequence of \(n_{t}\) values \(MRS_{r,i}(j)\) that are the median RSSI values received from the various beacons around that time.  
These \(n_{r}\) lists (\(MRSL_{r}\)) are then transmitted to a central Processing Node (PN), which merges them into a unified list and sorts the entries by their representative timestamps. This consolidated list is used as input for estimating the number of occupants. At this stage, the temporal resolution is sufficiently coarse that any lack of synchronization between the receivers and the central server becomes negligible. Given that BLE 4.x typically transmits advertising messages every 100 ms, the effective sampling rate at this point in the algorithm is \(1/(k\times 0.1)\) samples per second. The PN then feeds the merged list into a deep neural network model, which outputs an estimate of the number of people present in the monitored area.
## 3 System Overview  
In our work, we assume an indoor scenario, wherein a patient is confined in an intensive or superficial care unit. An authorized person is remotely monitoring the patient's health condition. We consider the transmission of ECG data, which particularly focus on heart rate variability. The proposed patch is connected to the ECG data sensing network. The signal is modulated in the LED or BLE and sent to the respective receiver. Static surveillance cameras are utilized to receive the LED data. Eventually, the data are transmitted to the authorized monitoring person using an eHealth gateway.
## 2 System Model
### M-MIMO Channel Model
where \(\sigma^{2}\) is the noise variance, \(\mathbf{I}_{N_{T}}\) is the identity matrix, \(\mathbf{W}\) is the MMSE's filter matrix defined by \(\mathbf{\hat{H}}_{LS}^{H}\mathbf{\hat{H}}_{LS}+\sigma^{2}\mathbf{I}_{N_{T}}\), and \(\mathbf{y}^{MF}\) is the output of the matched filter estimated from \(\mathbf{\hat{H}}_{LS}^{H}\mathbf{y}\). In this context, the MMSE detection process includes the inverse operation of the matrix; therefore, its computational complexity is extremely high and difficult to implement in multi-user MIMO systems, much less in massive antenna scenarios.
## 3 Analysis of Test Results
### P2 Beam Results  
P2 is a prestressed reinforced concrete beam subjected to trapezoidal cyclic loading. Due to its higher initial strength and stiffness than P1 beam, the deformations and the damage exhibit no significant changes during the early stages of cyclic loading which is less than 20 kN. Once a load of 40 kN is reached, multiple long cracks appear on the side of the P2 beam. Upon unloading and reloading up to 60 kN, multiple cracks form at the bottom of the beam, accompanied by slight splitting sounds. Additionally, the number of cracks on the side of the beam increases. Table 4 shows that strain gauges 3, 4, and 5 no longer provide readings, indicating that these gauges have been pulled apart, and through-cracks have formed. When loaded to 70 kN, the cracks at the mid-span rapidly expand with distinct explosive sounds. Upon unloading and further reloading to 74 kN, a significant number of explosive sounds occur, and the dial of the percentage gauge exceeds its range and continues to increase. The beam exhibits evident bending and a minor amount of concrete spalling, indicating that the P2 beam has experienced brittle failure. The flexural failure progression of the P2 beam is illustrated in Figure 6a-d. The significance of studying mid-span deflection and internal prestress in the P2 beam is limited due to its trapezoidal cyclic loading and thus is not further elaborated here.  
Figure 5: P1 test beam bending failure process diagram. (**a**) pre-test; (**b**) crack appearance; (**c**) multiple cracks; (**d**) beam bending. The red lines and frame in the figure indicate the crack locations.  
Figure 6: P2 test beam bending failure process diagram. (**a**) pre-test; (**b**) multiple cracks; (**c**) midspan through-cracking; (**d**) top concrete crushed. The red lines and circle in the figure indicate the crack locations.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**ID**} & \multirow{2}{*}{**Load (kN)**} & \multicolumn{6}{c}{**Strain**} & \multirow{2}{*}{**Deflection (mm)**} \\ \cline{2-3} \cline{5-8}  & & **1** & & **2** & **3** & **4** & **5** & \\ \hline
1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
2 & 5 & \(-\)212 & \(-\)190 & \(-\)53 & 153 & 12
# Removal of Fluoride and Arsenate from Aqueous Solutions by Aluminum-Modified Guava Seeds
## 3 Results and Discussion
### Effect of Adsorbent Dose on Fluoride Removal  
The effect of the biosorbent dose is an important parameter because it directly influences the removal efficiency of the system. In Figure 8 it can be seen that the percentage of fluoride removal rises as the biosorbent dose increases because of the increase in the number of active sites at high adsorbent material dosages until a dose of 70 g/L is reached. From this point, the removal of fluorides begins to decrease and stabilize, which can be attributed to a higher quantity of active sites not occupied by the F\({}^{-}\) ions, both on the surface and in the pores of the biosorbent and also to biosorbent crowding [26; 41]. Therefore, the optimal dose used for the further fluoride biosorption experiments was 70 g/L. A similar behavior was observed and reported previously [42] for the case of arsenate removal by AL-GSs at different dosages where the optimal biosorbent dose found was 60 g/L.
# Carrier Phase Dual One-Way Ranging Method Based on a Frequency Hopping Signal
## 6 Discussion  
As shown in Sections 3 and 4, for ISLs, the multifrequency carrier phase ranging method can achieve ranging accuracy exceeding that of PN codes. For continuous signal carrier phase measurements with multiple frequency points, even at a CNR of 40 dB-Hz, distance measurement accuracy can be achieved, which is significantly improved compared to the PN code ranging accuracy of 0.13 m. However, in this case, compared to the measurement algorithm itself, the hardware equipment used for measurement will have a greater influence on carrier phase measurement. Even if there is only a 1 ps delay measurement error between multiple channels, this will result in a ranging error of 0.29 mm for ranging accuracy. When the FH-DOWR is used, the ranging accuracy is only 0.247 mm at 40 dB-Hz. Although the measurement accuracy is reduced by two orders of magnitude compared to that of multiple frequency signals, the error impact caused by channel asynchrony is effectively reduced because multiple frequency points share the same RF channel. In addition, compared to the pseudocode ranging accuracy of 0.13 m, its performance improvement is also quite significant.
## 2 Related Work
The experimental findings support the assertion that, in terms of the convergence rate, error rate, and convergence time, the suggested strategy performs better than the current initialization methods [8]. [PERSON] et al. [27] developed a strategy known as the k-means initial population strategy, based on the k-means algorithm, to reconstruct the route by linking each cluster. The clusters are first sorted based on how far apart their centers are from one another, allowing the initial population to be made up of random travel paths. In order to connect with their neighbors, clusters disconnect a link at random. In order to improve the efficiency of the GA, [PERSON] et al. [28] developed a knowledge-based initialization approach (KI). KI picks up on the features of evolved populations and makes use of them to direct the evolution of initial populations. With this approach, advanced initial solutions without path crossover can be quickly developed. Different initialization strategies were tested using instances from the TSPLIB. According to the experimental results, the suggested initialization approach improved the GA more than previously published strategies, such as the random initialization, NN, GB, and VV techniques. To solve the large-scale TSP, an efficient GA with new initial population, crossover, and mutation methods was developed. Through five case studies, ranging from small-scale to large-scale TSPs, the effectiveness of the suggested GA is demonstrated. The basic idea of the suggested approach is to create a chromosome for which two neighboring genes should contain two extremely close cities, sometimes but not always closest to one another [47]. [PERSON] et al. [13; 22] presented a new regression-based method for GA population seeding in order to address TSPs. The proposed method splits a given TSP instance into smaller sub-problems. This is achieved by using the regression line and its perpendicular line, which enables the cities to be repeatedly clustered into four sub-problems; each city's location establishes which category or cluster it belongs to; the algorithm continues until the sub-problem's size is extremely small. As shown by the literature reviewed above, there are substantial reasons to identify better initial populations for GAs, such as reducing the GA search time required to discover an optimal solution and reducing the number of generations required to obtain a good solution. Identifying the optimal initial population is crucial for determining the optimal solution, and population diversity is required to prevent the GA's premature convergence.
## 5 Process Parameter Optimization of Laser Control System for MCR Frequency Modulation
### Experimental Verification of Laser Processing Optimal Parameters for MCR Frequency Modulation  
The optimized combination of laser processing control parameters for MCR frequency modulation are selected as the experimental parameters. The processed MCR is observed under 20\(\times\) and 50\(\times\) microscopes respectively, and the observation results are shown in Figure 14. It shows that the machined edge of the silver layer of the MCR electrode is relatively clean, and the black edge of the silver layer oxidation is less, which meets the requirements.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer Power/W** & **Laser Frequency/KHz** & **Processing Speed/(mm/s)** \\ \hline
6 & 500 & 200 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Optimal selection of laser control parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variance Source** & **Sum of Squares of Deviations** & **Freedom** & **Variance** & **F** \\ \hline A (Laser power) & 2.993 & 3 & 0.998 & 44.375 \\ B (Laser frequency) & 0.053 & 3 & 0.018 & 0.784 \\ C (Processing speed) & 1.174 & 3 & 0.391 & 17.409 \\ Experimental error & 0.135 & 6 & 0.022 & / \\ Comprehensive & 4.446 & 16 & / & / \\ \hline \hline \end{tabular}
\end{table}
Table 11: Variance analysis of over-frequency rate.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variance Source** & **Sum of Squares of Deviations** & **Freedom** & **Variance** & **F** \\ \hline A (Laser power) & 1277.545 & 3 & 425.848 & 33.558 \\ B (Laser frequency) & 28.34 & 3 & 9.447 & 0.744 \\ C (Processing speed) & 4192.445 & 3 & 1397.482 & 110.125 \\ Experimental error & 76.14 & 6 & 12.69 & / \\ Comprehensive & 86,325.194 & 16 & / & / \\ \hline \hline \end{tabular}
\end{table}
Table 12: Variance analysis of processing time.  
30720 MCRs in 480 rows, namely, 30 slices of wafers, are processed in the verification experiments. The average frequency modulation processing time of a single-row MCR is depicted in Figure 15. The average processing completion time is 293.79 s, which is lower than the target time of 320 s, reaching the requirements for production efficiency.  
The over-frequency rate of each wafer after frequency modulation is recorded and shown in Figure 16. The average defect rate is about 1.401%, which is lower than the over-frequency rate index of 1.56%, meeting the over-frequency rate requirements.  
The amount of fault alarms of laser processing systems for MCR frequency modulation is recorded every week, and the statistical results are listed in Table 14. The fault alarm rate \(\tau\) is 0.2 times per week, meeting the reliability requirements.  
Figure 14: Results of frequency modulation processing under optimized parameters (the black edge of the silver layer oxidation is less): (**a**) 20\(\times\) microscope observation; (**b**) 50\(\times\) microscope observation.  
Figure 15: Frequency modulation processing time per row under optimized parameters.
## 4 Isoprenoid Production in Methanotrophs
### C30 and C40: Carotenoids  
Carotenoids are isoprenoid compounds that are often used as feed or food additives because of their unique color. In addition, since they have a long double bond chain and a ketone group or a hydroxyl group, they have strong antioxidative effects [52; 53]. Currently, most carotenoids are produced through chemical synthesis, but there is a growing interest in natural synthesis as well as in the use of medicines and health foods.  
Carotenoids are classified into C30 and C40 carotenoids depending on the number of carbon atoms. The most commonly known C40 carotenoids are lycopene, \(\beta\)-carotene, canthaxanthin, and astaxanthin. Many studies have been conducted on ways to biosynthesize these compounds. Studies have also been carried out to biosynthesize C40 carotenoids in methanotrophs (Figure 1). Farnesyl pyrophosphate, which is a precursor of carotenoids, was prepared through the MEP pathway of methanotrophs, and the gene of the subsequent process was expressed. [PERSON] et al. synthesized canthaxanthin, adonixanthin, and astaxanthin by expressing _crtE_, _crtB_, _crtI_, _crtY_, _crtW_, and _crtZ_ genes in _Methylomonas_ sp. 16a. The content of astaxanthin was increased by controlling the number of copies of _crtW_ and _crtZ_ genes to synthesize up to 2.4 mg/g DCW (Table 2) [46; 47]. Additionally, lycopene was naturally produced in _Methylomonas_ sp. ZR1, as confirmed by HPLC-DAD and HPLC-MS/MS analysis methods [54].  
C30 carotenoids are less studied than C40 carotenoids. However, it is known that C30 carotenoids such as diapolycopene, diapolycopen-dial, and diaponeurosporene have higher antioxidative effects than tocopherol [55]. Therefore, C30 carotenoids can sufficiently replace C40 carotenoids. C30 carotenoid biosynthesis related genes _crtN_, _ald_, and _crtNb_ are contained in _Methylomonas_ sp. [24]. It was confirmed that C30 carotenoid was synthesized via mutagenesis analysis. A colorless mutant was isolated through deletion of a promoter upstream of the C30 encoding gene cluster [46]. Methanotrophs can naturally synthesize C30 carotenoids so that high productivity could be achieved by applying the recent studies on metabolic engineering and systems biology of methanotrophs.
# Design and Optimization of Non-Coplanar Orbits for Orbital Photovoltaic Panel Cleaning Robots
## 2 Construction of Models
### Shortcomings and Prospects  
In this paper, significant progress has been made in the field of non-coplanar orbit research; however, there remains considerable room for improvement. The primary limitations are as follows: (1) This study uses a single-segment Bezier curve in the non-coplanar trajectory design, which may result in unsatisfactory trajectory errors during simulation. (2) This study focuses exclusively on one approach to non-coplanar orbit design and optimization. A comparative analysis with other state-of-the-art techniques would enhance the credibility of this article. (3) The current study is limited to the simulation stage and has not yet been validated in practice.  
In future work, segmented or higher-order Bezier curves may be employed to design non-coplanar trajectories. Additionally, other methodologies can be explored, and it is essential to consider integrating Analytic Hierarchy Process (AHP) techniques into the design process to enhance efficiency. Furthermore, advanced methods for aeronautically assisted rendezvous-intercept trajectory optimization can be utilized to improve the optimization of non-coplanar orbits. It is also important to construct photovoltaic (PV) panels and non-coplanar orbits in real environments in conjunction with additional field tests and environmental data to experimentally validate the effectiveness and practicality of the proposed methodology.
## 3 Experimental Methods
### Interface Electronics Fabrication  
Figure 1 shows an overview of the SAW-based NO\({}_{2}\) sensor system, including the interface electronics. It consists of two SAW devices, two oscillators, a mixer, a low-pass filter (LPF), a comparator, an FPGA, and a PC. On the printed circuit board (PCB), the SAW NO\({}_{2}\) sensor and the SAW reference device are placed and wire-bonded to designated pads. The oscillators output the center frequencies of the SAW devices, which are then fed into the mixer. The mixer output provides the frequency difference between the two oscillators. The signal is then passed through the LPF and the comparator, which converts the analog signal into a square wave. The FPGA processes this signal in real time and transmits the square wave data to the PC, which displays the real-time frequency difference between the two SAW devices. Changes in ambient temperature and humidity affect the central frequencies of both sensors equally. However, by subtracting these changes in the mixer, the system features a built-in compensation circuit that prevents output frequency shifts due to variations in ambient temperature and humidity.
## 2 Power Flow Model for Hybrid AC/DC Distribution Network  
### AC Distribution Network Model  
The DistFlow equations are used to describe the power flows in AC distribution system [19]:  
\[P_{i,t}^{\text{AC}}=\sum_{k(i,:)\in\mathcal{L}_{\text{AC}}}P_{k,t}^{\text{AC}} -\sum_{l(:,i)\in\mathcal{L}_{\text{AC}}}\left[P_{l,t}^{\text{AC}}-\left(I_{l, t}^{\text{AC}}\right)^{2}\right]\forall i\in\mathcal{N}_{\text{AC}}, \tag{1}\]  
\[Q_{i,t}^{\text{AC}}=\sum_{k(i,:)\in\mathcal{L}_{\text{AC}}}Q_{k,t}^{\text{AC} }-\sum_{l(:,i)\in\mathcal{L}_{\text{AC}}}\left[Q_{l,t}^{\text{AC}}-\left(I_{l,t}^{\text{AC}}\right)^{2}\right]\forall i\in\mathcal{N}_{\text{AC}}, \tag{2}\]  
\[\left(I_{i,t}^{\text{AC}}\right)^{2}=\left(I_{j,t}^{\text{AC}}\right)^{2}-2(r _{k}P_{k,t}^{\text{AC}}+x_{k}Q_{k,t}^{\text{AC}})+\left(I_{k,t}^{\text{AC}} \right)^{2}(r_{k}^{2}+x_{k}^{2})\ \forall k(j,i)\in\mathcal{L}_{\text{AC}}, \tag{3}\]  
\[\left(I_{k,t}^{\text{AC}}\right)^{2}\left(U_{j,t}^{\text{AC}}\right)^{2}= \left(P_{k,t}^{\text{AC}}\right)^{2}+\left(Q_{k,t}^{\text{AC}}\right)^{2} \forall k(j,i)\in\mathcal{L}_{\text{AC}}, \tag{4}\]  
\[\left(P_{k,t}^{\text{AC}}\right)^{2}+\left(Q_{k,t}^{\text{AC}}\right)^{2}\leq \left(S_{k,\text{max}}^{\text{AC}}\right)^{2}\ \forall k(j,i)\in\mathcal{L}_{\text{AC}}, \tag{5}\]  
\[U_{i,\text{min}}^{\text{AC}}\leq U_{i,t}^{\text{AC}}\leq U_{i,\text{max}}^{ \text{AC}}\ \forall i\in\mathcal{N}_{\text{AC}}, \tag{6}\]  
where \(\mathcal{L}_{\text{AC}}\) represents the set of AC branches; \(\mathcal{N}_{\text{AC}}\) represents the set of AC nodes; \(t\) is the scheduling time; \(k(i,:)\) represents branch \(k\) starting with node \(i\) and \(l(:,t)\) represents the branch \(l\) ending with node \(i\); \(r_{l}\) and \(x_{l}\) are the resistance and reactance of branch \(l\); \(P_{i,t}^{\text{AC}}\) and \(Q_{i,t}^{\text{AC}}\) are the injection active and reactive power at node \(i\); \(I_{k,t}^{\text{AC}}\), \(P_{k,t}^{\text{AC}}\), and \(Q_{k,t}^{\text{AC}}\) are the phase current, active, and reactive power at branch \(k\); \(U_{j,t}^{\text{AC}}\) and \(U_{i,t}^{\text{AC}}\) are the phase voltage at node \(j\) and \(i\); \(U_{i,\text{min}}^{\text{AC}}\) and \(U_{i,\text{max}}^{\text{AC}}\) are the minimum and maximum voltage at node \(i\); and \(S_{k,\text{max}}^{\text{AC}}\) is the maximum load flow of AC branch \(k\).