esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Comet Assay  
Mononuclear blood cells were used to perform the alkaline (pH > 13) comet assay, following the methods of [PERSON] (2004) [25], and a system of twelve gels per slide, as adopted by [PERSON] et al. (2021) [26], was used to increase the yield. Six slides precoated with normal-melting-point agarose were used for each treatment, with three replicates per animal. One set was used to run the assay with the repair enzyme, formamidopyrimidine DNA glycosylase (Fpg), and the other was used to conduct the assay without it. In order to precisely identify oxidative damage to DNA, particularly 8-oxoguanines and other changed purines, Fpg converted oxidized purines into DNA single-strand breaks. Peripheral blood was diluted in ice-cold phosphate-buffered saline (PBS), and this cell suspension was combined with 1% low-melting-point agarose. Twelve drops were placed on each of the twelve precoated slides, and the slides were refrigerated for solidification. The samples were then incubated in a lysis solution (2.5 M NaCl, 0.1 M EDTA, 10 mM Tris, 1% Triton X-100, pH 10) and rinsed (40 mM HEPES, 0.1 M KCl, 0.5 mM EDTA, 0.2 mg/mL bovine serum albumin, pH 8.0).  
The slides with and without Fpg treatment were then incubated for 30 min at 4 \({}^{\circ}\)C in an alkaline electrophoresis solution (0.3 M NaOH and 1 mM EDTA, pH > 13) and electrophoresed for 30 min at 25 V and 300 mA. The cells were then neutralized with PBS, followed by distilled water, and then dehydrated in 70% and absolute ethanol. A fluorescent microscope at 400\(\times\) magnification (Olympus BX41, Olympus America Inc., Hauppauge, NY, USA) was used to view the DNA stained with 4,6-diamidino-2-phenylindole (DAPI).  
The comets were then classified visually based on a five-class scale of severity (class 0, no damage, and class 4, highest damage) [25]. Three hundred nucleoids were classified per animal (100 per mini gel) and, using the following formula, the total score was expressed as a genetic damage index (_GDI_) with a range of 0 to 400 arbitrary units:  
\[GDI=\sum\%\text{nucleoids class }i\times i\]  
where \(I\) represents the number of each class.  
Untreated _GDI_ values were subtracted from the Fpg incubation scores (\(GDI_{\text{Fpg}}\)) to calculate the net enzyme-sensitive sites (NSSFpg).
## 5 Simulation Outcomes and Analysis
### Influence of the Number of Nodes  
We test the five algorithms as mentioned above in the case of varying the nodes. R is 25 m, and the ratio of anchor nodes is 20%. The number of network nodes varies from 50 to 100. A comparison of the localization errors of five algorithms is shown in Figure 9.  
It can be revealed from Figure 9 that the localization error shows an overall decreasing curve when the nodes are increased. Along with the increase in segmentation factor, the localization error of CMWN-DV-Hop progressively decreases. When the nodes are the same, CMWN-DV-Hop (a = 2) is slightly inferior to the three algorithms, including CS-DV-Hop, but outmatches DV-Hop. The two types of errors of CMWN-DV-Hop always reach their lowest when the segmentation factor is between 6 and 10, regardless of the number of nodes. When the segmentation factor is more prominent than or equal to 4, CMWN-DV-Hop delivers the best positioning property compared with the other three algorithms. Regardless of the number of nodes, CMWN-DV-Hop (a = 10) has the lowest average localization error, with a maximum reduction of 54.5890%, 53.0569%, and 54.4498% when in comparison with the other three proposed novel algorithms.
# Spinning Reserve Capacity Optimization of a Power System When Considering Wind Speed Correlation
## 5 Analysis of Examples
### The Effect of Wind Speed Correlation on Expected Energy Not Served  
Table 3 lists the influence of the wind speed correlation on EENS, when the confidence level \(\beta\) is 0.9. The wind power installed capacities are 90, 180, 270, and 360 MW, respectively.  
As shown in Table 3, with the increase of the wind speed correlation, the EENS increases. When the wind speed correlation goes up, the wind farm output is more volatile, especially in the case of a high positive correlation; in that situation, the prediction deviation of wind power becomes larger, leading to a greater standard deviation of electricity demand in the whole power system, which gives rise to the increase of EENS. Meanwhile, EENS goes up as the wind power capacity rises.
## 1 Introduction
### Challenges in Peak Detection  
In practice, the beat signal received at the radar is contaminated by various forms of noise and interference. At low SNR levels, noise peaks may dominate or obscure the actual  
Figure 1: Comparison of clean (**left**) and noisy (**right**) beat signals in time and frequency domains. In low SNR scenarios, the spectral peak becomes masked by noise and clutter, challenging traditional detection methods.  
target returns, leading to false alarms or missed detections. Furthermore, in multipath-rich environments or in the presence of extended targets, the superposition of returns can cause peak broadening and ambiguity in detection. These challenges necessitate the development of robust peak detection algorithms that can operate reliably under a wide range of conditions.
# Classification of Children's Sitting Postures Using Machine Learning Algorithms
## 2 Related Work  
### Sitting Posture Classification  
Several methods have been adopted to measure sitting posture and classify it. Camera, is one of the method used to identify and capture sitting postures [22]. Sensors can also be used by being attached to the user's back to recognize sitting postures based on motion data [23; 24; 25]. However, the former introduces issues with respect to privacy and unnatural behavior owing to negative feelings caused by being monitored [26], and the latter has drawbacks of the possibility of inducing unnatural movements owing to the attached sensors [27]. The use of pressure sensors mounted on the parts of a chair (e.g., seat and backrest) has been employed to classify sitting postures to overcome these issues. Previous studies have been performed for classifying sitting postures of users by attaching pressure sensors to chair structures such as the seat and backrest as a method to overcome the aforementioned issues. Table 1 shows the previous studies in which posture classification was performed, including the number of pressure sensors used, the locations in which they were installed, participant information, the number of predicted sitting conditions, the algorithm used, and the overall accuracy. Although the number of sensors used in previous studies varied, these studies can be largely divided into two types depending on the location of installation of the sensor. In one type, the sensor was attached to the seat only [26; 28; 29], and in the other type the sensor was attached to both the seat and backrest [9; 30; 31; 32]. In addition to the pressure sensor, [PERSON] et al. [8] used additional sensors such as kinetic related and temperature sensors to classify the sitting postures. Most of the previous studies attempted to classify the sitting upright posture in which the waist is straight, and the feet are placed flat on the floor, the postures in which the upper body is tilted forward, backward, left, or right, and the postures in which the left or right leg is crossed. In the case of the classification of various other postures, the postures in which the legs are crossed were segmented (e.g., one leg over the other, one leg over the other with one foot on the other, one foot on the seat under the other leg's thigh) [32]. In addition, there were also cases wherein the movement pattern of the upper body and whether the user was seated were predicted [28; 31]. Various methods have been adopted for classifying the sitting postures, and they include the use of PCA [32], Naive Bayes classifier [10], hybrid cascade sitting posture classifier [30], SVM [29; 33], kNN [9], dynamic time warping-based classification [26], and density-based methods [28]. [PERSON] et al. [30] have compared the classification performance obtained on applying DT, SVM, and multilayer perception.
## 6 Simulation and Results Discussion
### Results and Discussion
#### 6.2.2 Throughput vs. Delivery Ratio  
It measures the average rate of successful data delivery over the communication channel. The throughput value rises gradually with the number of nodes, and the packet-delivery ratio rises along with the throughput. Figure 7 depicts the throughput and delivery ratio for both the securities-enabled dynamic secure broker (DSB) system and other existing  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Bandwidth**} & \multicolumn{4}{c}{**End-to-End Delay**} \\ \cline{2-5}  & **EE-OHRA** & **EA-DRP** & **NETAR** & **QoSDSBS** \\ \hline
10 & 49.1023 & 47.75 & 45.15 & 43.141 \\
15 & 47.2451 & 44.73 & 42.57 & 39.5123 \\
20 & 34.691 & 31.69 & 27.91 & 24.4581 \\
25 & 24.742 & 22.16 & 19.13 & 15.762 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of bandwidth vs. end-to-end delay.  
Figure 6: Bandwidth vs. end-to-end delay.  
algorithms. Our model, QoSDSBS, outperforms with an increase in the delivery ratio for increased throughput with DSB, as shown in Table 4. The proposed algorithm attains better results than the compared algorithms, EE-OHRA, EA-DRP, and NETAR. However, the NETAR algorithm competes with the proposed algorithm, but it fails to attain the maximal delivery ratio.
## 4 Discussion
### Justification of the Results
#### 4.2.1 Different Speakers  
The results, as shown in Figure 12 and Table 4, indicate that the two-way speakers that were used in the measurements have roughly similar results especially for RT and EDT measurements, which may indicate that similar results may be expected for other two-way speakers. However, it can be seen that the Behringer speaker performs better than the Genelec for RT and EDT measurements. A possible explanation, might be due differences between speakers, such as different cross over frequencies, resonances, energy storage in the cone, different internal volume, phase inaccuracies e.g., This is also presented in the frequency domain (Figure 13) where the excitation of a space with different speakers results in differences of the level spectrums with respect to the dodecahedron speaker. The same is presented in Figure 10, which also occurs for different points in space. It can be seen that while there are differences between the proposed method and measurement with a dodecahedron speaker, there is a similar excitation of the resonant frequencies of the space with both methods. This is an indication of the similarities of the sound fields created from the proposed method and the dodecahedron speaker that is manifested in the frequency domain. Probably some speakers utilizing the proposed method will resemble better the sound field created from a dodecahedron speaker and provide better results.  
The Philips speaker which is a one way design has the least satisfying mean absolute error compared with the other speakers for RT measurements, especially for the low frequency range. This is probably due to the low signal to noise ratio caused by the low output of the speaker in this frequency range. However, one unanticipated finding was that the Philips speaker performed better than the other speakers for C\({}_{80}\) and D\({}_{50}\) measurements in the high frequency range. Since Philips is a one-way driver design, it seems possible that this result is due to the directivity of the speaker which is closer to that of the dodecahedron speaker. Nonetheless these results need to be interpreted with caution.
# Optimal Base Station Density of Dense Network:
## 4 Throughput of MAC Layer in SmBS Tier
### Throughput Analysis of an SmBS Cell  
Combining Equations (13) and (14), we then get the throughput of the SmBS as follows:  
\[f_{\mathrm{m}}=\frac{a\lambda_{\mathrm{u}}\lambda_{\mathrm{data}}e^{-\frac{a \lambda_{\mathrm{u}}\lambda_{\mathrm{data}}}{\hat{\mathcal{C}}\lambda_{\mathrm{ M}}+\lambda_{\mathrm{m}}}}}{\left(\hat{\mathcal{C}}\lambda_{\mathrm{M}}+\lambda_{ \mathrm{m}}\right)\left(1+a-e^{-\frac{a\lambda_{\mathrm{u}}\lambda_{\mathrm{ data}}}{\hat{\mathcal{C}}\lambda_{\mathrm{M}}+\lambda_{\mathrm{m}}}}\right)}. \tag{15}\]  
According to Equation (3), the throughput of an SmBS can be given as  
\[S_{\mathrm{m}}=\frac{2\pi^{2}W_{\mathrm{m}}aP_{\mathrm{m}}^{1/2}\lambda_{ \mathrm{u}}\lambda_{\mathrm{data}}e^{-\frac{a\lambda_{\mathrm{u}}\lambda_{ \mathrm{data}}}{\hat{\mathcal{C}}\lambda_{\mathrm{M}}+\lambda_{\mathrm{m}}}} \lambda_{\mathrm{m}}^{3}}{\left(\hat{\mathcal{C}}\lambda_{\mathrm{M}}+\lambda_ {\mathrm{m}}\right)^{2}\left(1+a-e^{-\frac{a\lambda_{\mathrm{u}}\lambda_{ \mathrm{data}}}{\hat{\mathcal{C}}\lambda_{\mathrm{M}}+\lambda_{\mathrm{m}}}} \right)}\kappa(\lambda_{\mathrm{m}}), \tag{16}\]  
where \(\kappa(\lambda_{\mathrm{m}})=\int\limits_{\eta}^{\infty}\int\limits_{\eta}^{ \infty}\frac{e^{-\frac{a^{3}P_{\mathrm{m}}\lambda_{\mathrm{m}}^{2}}{4\hat{ \mathcal{C}}\lambda_{\mathrm{m}}^{2}}}}{2I^{3/2}}r\exp(-\pi\lambda_{\mathrm{m}} r^{2})\log\left(1+\frac{P_{\mathrm{m}}r^{-4}}{I+\sigma^{2}}\right)drdI\).  
From Equation (16), it is observed that \(S_{\mathrm{m}}\) is a function of SmBS density \(\lambda_{\mathrm{m}}\). It means that we can maximize the throughput \(S_{\mathrm{m}}\) via controlling \(\lambda_{\mathrm{m}}\). Since it does not have a simple form for \(\kappa(\lambda_{\mathrm{m}})\), for fast calculation of the optimal SmBS density \(\lambda_{\mathrm{m}}\), we propose a fitting function to approach \(\kappa(\lambda_{\mathrm{m}})\), which is shown in Appendix D.  
It is worth noting that some objective parameters that indicate the condition of network, such as the power of SmBS \(P_{\mathrm{m}}\) and the user density \(\lambda_{\mathrm{u}}\), also have influence on \(S_{\mathrm{m}}\). Thus, \(S_{\mathrm{m}}\) should also be analyzed under different network conditions. We will do it in Section 5.
# Cognitive Interference Cancellation with Digital Channelizer for Satellite Communication
## 1 Introduction
### Notations  
For a random variable \(x\), \(x\sim\mathcal{N}(\mu,\sigma^{2})\) means that \(x\) is Gaussian distributed with mean \(\mu\) and variance \(\sigma^{2}\). \(\mathbb{E}[x]\) denotes an expectation of the random variable. \(Q(x)\left(=\frac{1}{\sqrt{2\pi}}\int_{x}^{\infty}e^{-t^{2}/2}dt\right)\) represents the tail probability of a Gaussian distribution.
## 2 Materials and Methods
### Training and Validation Data  
The largest earthquake in the period of this study is the \(M_{w}\) 9.1 Japan earthquake that occurred on 11 March 2011, UTC 5:46, with an epicenter of latitude 38.297\({}^{\circ}\) N and longitude 142.373\({}^{\circ}\) E. This magnitude of \(M_{w}\) 9.1 is set to define the extent of the geographic area to search for anomalies during the whole study; that is, according to Equation (1), an  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Group Number** & \
\begin{tabular}{c} **Approximate** \\ **Magnitude \(M_{w}\)** \\ \end{tabular} &
\begin{tabular}{c} **Magnitude \(M_{w}\)** \\ **Limits** \\ \end{tabular} & **Number of Events** \\ \hline
1 & 5.5 or less & \textless{}5.75 & 2362 \\
2 & 6.0 & [5.75, 6.25) & 736 \\
3 & 6.5 & [6.25, 6.75) & 466 \\
4 & 7.0 & [6.75, 7.25) & 224 \\
5 & 7.5 & [7.25, 7.75) & 74 \\
6 & 8.0 & [7.75, 8.25) & 24 \\
7 & 8.5 & [8.25, 8.75) & 3 \\
8 & 9.0 & \(\geq\)8.75 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distribution of earthquakes under study in terms of their magnitudes.  
Figure 4: Histogram of earthquake magnitudes.  
abnormal area with a radius \(r\) of 8103 km is conservatively used to define the increments in latitude \(\Delta\varphi\) and longitude \(\Delta\lambda\) from the epicenter by means of Equations (3) and (4), after equating the distances along the meridian \(d_{m}\) and the parallel \(d_{p}\) to this radius \(r\), to define an area of influence (refer to Figure 2, but in this case, a larger extension).  
Now the idea is to go one by one through the whole earthquake list compiled previously (refer to Table 1 and Figure 4) and, for each earthquake, to construct the following:  
* 60 rows, corresponding to the latitude range explained before;
- 73 columns. Since the length of the parallel arc depends on its latitude different numbers of columns were obtained for the different latitudes, with high latitudes needing the whole column range in the GIM, 73. Completing with zeros the other column ranges was necessary to have a consistent size;
- 84 values in the third dimension, corresponding to the 7 days with data every 2 h in the GIM files;
* A predicted variable, denoted as the \(Y\) variable, which is obtained from the earthquake list as the highest magnitude of the earthquake occurring in the area from 7 days before to 4 days after the earthquake under analysis. This is because there could be aftershocks even larger than the selected earthquake, or previous events of greater magnitude, and the predictor should capture them. The earthquake magnitude is categorized in one of eight categories named as group number in Table 1 and this category is stored as the \(Y\) variable.  
It is worth mentioning that some earthquakes in the list could not be analyzed due to the lack of GIMs for those days (data gaps already mentioned in Section 2.1) leaving the total list of events to be predicted with available GIMs at 3815.  
From the whole set of (X, Y) variables, 80% and 20% were selected as training and validation sets, respectively.
# Security Assessment of Agriculture IoT (AIoT) Applications
## 2 Materials and Methods
### Process Step: SuC Identification
#### 2.4.3 Sub-Suc  
If the overall SuC is very complex, a good approach is to divide the overall system into sub-SuCs, where each individual sub-SuC receives its own cybersecurity assessment. The overall SuC in Figure 1 will be divided into the following sub-SuCs:  
* Livestock SuC (SuC-A) Livestock with sensors (Field)--Farm--Middleware--Farm management
* Soil-sensor SuC (SuC-B) Soil sensors (Field)--Farm--Middleware--Farm Management \(\rightarrow\) see Figure 1
* Drone SuC (SuC-C) Drone with spectral sensors (Field)--Farm--Middleware--Farm management
* Vehicle SuC (SuC-D) Tractor with sensors and actuators (Field)--Farm--Middleware--Farm management Data SuC (SuC-E) Farm management--Middleware--Farm--Personal With tablet (Field)  
The documentation of all five sub-SuCs would go beyond the scope of this document. In this case, only \"Soil-sensor SuC\" is used as an example (see Figure 4) to document the cybersecurity assessment process further on. Furthermore, in this paper, the cybersecurity assessment focuses on field sensor security in segment 1 (Field) and segment 2 (Farm). This reduces the scope of the work and the size of the paper and gives a sufficiently detailed overview of the cybersecurity assessment process. On the other hand, segment 1 (Field) and segment 2 (Farm) are of great interest to the farm staff, as these are their areas of responsibility.  
The sub-SuC \"Soil-sensor SuC\" defines the architecture, the components and the sensor communications data flows of a soil sensor application. It defines the sensor to the middleware and the middleware to the mobile MMT data communication. It provides a set of sensors in the field zone which transfer sensor data via Long Range Wide Area Network (LoRaWAN) or by using the Bluetooth Low Energy (BLE) transmission protocol to the cloud repository in the middleware. The farmer on the field side can request postprocessed environmental data via a telecommunication link on a mobile MMT device (smart mobile phone, tablet with 3G/4G functionality).  
**Asset List:**  
Field (Environment Sensor: LoRaWAN communication protocol, Soil sensor: BLE communication protocol, Mobile MMT: Tablet Computer--Data/Mission Display), Farm house (LoRaWAN/BLE Gateway, Router, Farm Computer, Family Computer), Middleware (LoRaWAN Network server, Cloud repository, DDS).  
**Data communication links:**  
LoRaWAN, BLE, WLAN, 3G, 4G.
## 7 Discussion and Conclusions  
A novel formulation to trace the power flow was developed in this study. The formulation was based on basic circuit theory and power balance equations. The active and reactive power consumed by any load or distributed by any generator can be traced in every line and every direction. The real power, reactive power, loss, counter flows, and mutual coupling terms are all considered and are balanced. The proposed method has the following advantages:  
- A converged power flow solution is the only requirement with network parameters;  
- It traces all electrical components for the line, generation, and load in a power network;  
- It considers real, reactive power and mutual coupling terms together;  
- It traces the power for either direction, i.e., from generators to load or vice versa;  
- The proposed method can calculate contributions of each generator at any location for both the active and reactive power flow;  
- Likewise, the proposed method can calculate contributions of each generator for counter flows and mutual coupling systems at any location, which has never been discussed before;  
- All calculated electrical components can satisfy the circuit theory and the power balance equations in an interrelated, non-separable manner;  
- It shows that power will flow in every network conductor instead of a single direction, in accordance with the basic physics and basic circuit theory;  
- The customer could verify for each GENCO the quantity of power they are buying from;  
- Contributions of renewable sources can be calculated in a more justifiable way--it can help making energy strategies to see the impact of a particular green power at a chosen location;  
- Counter flow might be awarded with positive contributions in alleviating network congestions and reduce the loss;  
- Losses can be traced and allocated with rigorous calculation;  
- LUF and LRF were derived rigorously to provide a justifiable base for tariffs, so the usage-of-transmission can be considered more effectively;  
- As positive flow and counter flow co-exist in the same line, there are new opportunities to further improve the system security;  
- The proposed algorithm provides a lot of potential for new applications in many aspects, and it also provides a new and powerful tool for the energy market.  
[PERSON]-[PERSON] provided the project idea, conceptualization, related experiences, writing, and editing. [PERSON]-[PERSON] performed the data curation, system model, and analysis and conducted simulations. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Tan Kah Kee College, Xiamen University, grant number JG2021 SRF01.  
Not applicable.  
The authors declare no conflict of interest.