esa-sceva/satcom-chunk-collection huggingface dataset:
# Line-Detection Based on the Sum of Gradient Angle Differences
## 4 Line-Detection Experiments with Simulated Images  
### Generation of Simulated Images  
To test the line-detection performance of the SD-, EC-, and proposed SGAD-based methods, a series of images was generated containing line features with fixed blurring factor \(\sigma_{b}=1\), varying line width \(w\), line normal angle \(\theta\), and noise \(\sigma_{n}\). For the given image center coordinates \((r_{o},c_{o})\) and line normal direction \(\theta\), normal distance from the origin to the center of the line \(\rho\) was calculated as follows:  
\[\rho=c_{o}\cos\theta+r_{o}\sin\theta, \tag{16}\]  
and the following two metrics were calculated for each pixel location \((r,c)\).  
\[\begin{split} D_{1}(r,c)&=\frac{c\cos\theta+r\sin \theta-\rho+\frac{w}{2}}{\sigma_{b}\sqrt{2}}\\ D_{2}(r,c)&=\frac{c\cos\theta+r\sin\theta-\rho- \frac{w}{2}}{\sigma_{b}\sqrt{2}}.\end{split} \tag{17}\]  
For the given contrast \(k\), described in (2), the intensity at each pixel was then calculated according to (4) as  
\[\mathbf{F_{b}}(r,c)=\frac{k}{2}\big{[}\mathrm{erf}(D_{1}(r,c))-\mathrm{erf}(D_ {2}(r,c))\big{]}. \tag{18}\]  
An image was generated by adding a noise image \(\mathbf{n}\), as follows:  
\[\mathbf{I}=\mathbf{F_{b}}+\mathbf{n}, \tag{19}\]where \(\mathbf{n}(r,c)\) was assumed to follow the normal distribution \(N(0,\sigma_{n}^{2})\).  
Figure 4 shows four typical examples of \(101\times 101\) pixel simulation line images with varying \(\theta\) and constant \(k=1\), \(\sigma_{b}=1\), \(w=4\), and \(\sigma_{n}=30/255\).  
Denoising is commonly applied before feature extraction from noise-contaminated images to ensure that the extracted features are of the highest possible quality. Therefore, in the present study, the following smoothing convolution is applied:  
\[\mathbf{I_{s}}=\mathbf{I}*\mathbf{s}, \tag{20}\]  
where \(\mathbf{s}\) is a two-dimensional Gaussian function with standard deviation \(\sigma_{s}\).
# Fast Stepwise Inertial Control Scheme of a DFIG for Reducing Second Frequency Drop
## 3 Frequency Support Schemes of a DFIG
### Proposed Stepwise Inertial Control Scheme of a DFIG  
To solve the conventional stepwise inertial control scheme issues mentioned above, we suggest a torque limit-based fast stepwise inertial control scheme of a DFIG using a piecewise function. This function includes the power reference in the FSP and RSRP, which are the same as in the conventional scheme. Nevertheless, the power reference for the RSRP and the activation moment of the RSRP are different from the conventional scheme.  
The control concept and characteristics of the proposed stepwise inertial control strategy are illustrated in Figures 3 and 4, respectively. After detecting a frequency disturbance, the DFIG increases \(P_{\textit{ref}}\) to \(P_{B}\), corresponding to operating point B. Afterward, the DFIG decreases \(P_{\textit{ref}}\) using rotor speed to prevent the rotor from stalling, and the operating point moving from point B to point C. The power reference of the proposed stepwise inertial control scheme from operating point A to operating point C is the same as in (15). As a result, the proposed frequency support preserves the frequency nadir during the FSP.  
In the RSRP (from operating point C to operating point D), the DFIG smoothly decreases the power reference based on the exponential function rather than instantly decreasing the correspondence with segment C-D in Figure 4. Therefore, the proposed stepwise inertial control scheme reduces the depth of the SFD and further reduces the wind turbine's mechanical stresses. The power reference for RSRP is represented as:  
\[P_{\textit{set}}=P_{\textit{MPPT}}+\Delta P_{\textit{RSR}}\times e^{-\alpha(t- t_{1})} \tag{18}\]  
\[\Delta P_{\textit{RSR}}=P_{\textit{set}}(\omega_{\text{C}})-P_{\textit{MPPT}} (\omega_{\text{C}}) \tag{19}\]  
where \(t_{1}\) is the instant of operating point C, \(\Delta P_{\textit{RSR}}\) is the difference between \(P_{\textit{set}}(\omega_{\text{C}})\) and \(P_{\textit{MPPT}}(\omega_{\text{C}})\), and \(\alpha\) is the regulation factor of the RSRP.  
Figure 3: Control concept of the proposed stepwise inertial control scheme.  
The reasons why the RSRP initiates at operating point C is explained as follows: the system frequency is in the rebounding phase around operating point C. According to [1], the depth of the SFD reduces if the RSRP initiates during the frequency rebound phase. Thus, the expression of the suggested piecewise function during the FSP and RSRP is:  
\[P_{set}=\left\{\begin{array}{ll}\frac{P_{\mathrm{Tlim}}(\omega_{\mathrm{D}})- P_{\mathrm{MPPT}}(\omega_{\mathrm{min}})}{\omega_{\mathrm{0}}-\omega_{\mathrm{min}}}( \omega_{r}-\omega_{\mathrm{min}})+P_{MPPT}(\omega_{\mathrm{min}})&0<t\leq t_{ 1}\\ P_{MPPT}+\Delta P_{RSR}\times e^{-\alpha(t-t_{1})}&t>t_{1}\end{array}\right. \tag{20}\]
## 2 Materials and Methods
### The Minimum Concentration of Polyphenols (MIC) That Inhibits the Growth of Staphylococcus Bacteria  
In order to determine the minimum concentration of polyphenols inhibiting the growth of bacteria of the genus _Staphylococcus_, a deep culture of the studied microorganisms was performed. For this purpose, 6 wells were cut (10 mm in diameter) into a plate from a 24-h culture of _Staphylococcus_ bacteria (density \(10^{8}\) CFU/mL) with a sterile crank and removed with a sterile preparation needle. Added to the holes created in this way was 100 \(\upmu\)L of appropriately diluted extracts from the pseudo-fruit pomace _Rosa rugosa_ Thunb., with a  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Species Name** & **Species Name** & **LOCK ID pr** & **Culture** \\
**(before Reclassification)** & **(after Reclassification)** & **NCBI No. * & **Temperature** \\ \hline _Lactobacillus acidophilus_ & No change & LOCK 0928 & 37 \\ _Lactobacillus brevis_ & _Leilactobacillus brevis_ & LOCK 0944 & 30 \\ _Lactobacillus brevis_ & _Levilactobacillus brevis_ & LOCK 0980 & 30 \\ _Lactobacillus brevis_ & _Levilactobacillus brevis_ & LOCK 0992 & 30 \\ _Lactobacillus casei_ & _Lactíaseibacillus casei_ & LOCK 0979 & 30 \\ _Lactobacillus rhamnosus_ & _Lactíaseibacillus rhamnosus_ & LOCK 0943 & 37 \\ _Lactobacillus brevis_ & _Levilactobacillus brevis_ & MG451814 * & 30 \\ \hline \hline \end{tabular}  
* NCBI database number; LOCK ID = strain number in the Pure Culture Collection of Industrial Microorganisms of the Institute of Fermentation Technology and Microbiology ROCK 105 (Lödz, Poland).  
\end{table}
Table 1: Change of the nomenclature and culture conditions of the lactic acid bacteria strains used in the research.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Species Nucleotide** & **Sequence Number** & **Isolate Symbol** \\ \hline _Staphylococcus epidermidis_ & MW040703 & R1A \\ _Staphylococcus haemolyticus_ & MW040704 & R2A \\ _Staphylococcus saprophyticus_ & MW040705 & R3A \\ _Staphylococcus saprophyticus_ & MW040701 & S1A \\ _Staphylococcus aureus_ & MW040702 & S2A \\ \hline \hline \end{tabular}
\end{table}
Table 2: Species affiliations of environmental isolates of the genus _Staphylococcus_.  
content in the extracts in the range of 0.0006-2.5 mg/mL. The negative control was 5% DMSO. The plates were then placed in an incubator (37 \({}^{\circ}\)C) for 18-24 h. After this time, the zone of inhibition of growth of the bacteria of the _Staphylococcus_ genus was measured. The results were given in mm \(\pm\) standard deviation (SD). The lowest concentration of polyphenols at which growth inhibition of the test bacteria was still observed was assumed to be the MIC value (mg/mL).
# An Effective Hybrid Routing Algorithm in WSN:
## System Model  
### WSN Network Model  
WSN is a self-organizing network that consists of a number of distributed wireless sensor nodes and a sink node. Sensor nodes can sense the environment, perform basic computations on the collected data, and communicate with neighboring nodes via a radio transmitter. The data collected by sensor nodes from a target area is finally forwarded to the sink node one hop by one hop for further processing. In our ACHCM algorithm, there are 6 assumptions.  
1. All nodes in the network are randomly deployed in the monitoring area. After the deployment, the location no longer changes. The sink node has knowledge of the topology of the whole network.
2. All sensor nodes in the network are homogeneous, i.e., the node's computing power, communication capability, and initial energy are the same.
3. All sensor nodes are energy-constrained and cannot be replenished, while the sink node can be supplemented.
4. The radio transmission power of the node is adjustable, and the transmission power can be adjusted according to the distance.
5. Like other related works, Packet error and overtime retransmission caused by link errors are not considered.  
The sink node periodically carries out data collection, and all sensor nodes forward the data according to their own routing.
## 3 Pulse Shapes Typical of Experiments  
We have considered coherent Gaussian pulses so far. Such a pulse may be generated either by extracting a single harmonic order of HHG sources or by HHG-seeded FELs [80]. In practice, however, high-harmonic pulses are composed of several orders. Also, as is well known, the temporal pulse shapes of FEL operating in the self-amplified spontaneous emission (SASE) mode fluctuate from shot to shot [55; 56; 57; 58; 59]. In this section, we investigate the effects of these properties of practical pulses for the case of a He atom. While Gaussian pulses are characterized by a single time scale, _i.e._, the width of the (whole) pulse, both FEL and HHG pulses have two time scales: coherence time and mean pulse width for the former, and the width of each pulse in the pulse train and the overall pulse width for the latter. From the analysis of these pulses, we may also estimate to what extent the relative phase \(\delta\) can be controlled by the pulse shape.
## 4 IMel Spectrogram
### Generation of the Mel Spectrogram  
The commonly used Mel spectrogram contains the features of human auditory perception. It is based on the Mel frequency domain, in which linear frequencies are mapped to the Mel frequency. The equation below shows the relation:  
\[F_{Mel} =2595\ln(1+700) \tag{1}\]  
where \(f_{lin}\) is the frequency, expressed on a linear scale, and the unit is Hz; \(f_{Mel}\) is the frequency on the Mel scale, and the unit is Mel. In Figure 2a, \(f_{Mel}\) and \(f_{lin}\) are logarithms, and the resulting Mel filter banks are shown in Figure 2b. The resolution at the low frequency is higher than that at the high frequency, which is consistent with the characteristics of human hearing perception, and the Mel spectrogram based on the Mel filter banks has the same property. Therefore, the Mel spectrogram has been widely used and has achieved good results in the field of speech recognition. However, when applied to SER, it has certain limitations.  
Figure 1: Recognition network. The first channel extracts optimized features from the Mel spectrogram to highlight the low-frequency information, and the other channel extracts optimized features from the IMel spectrogram to highlight high-frequency information.  
Figure 3 shows [PERSON]'s two-dimensional expression of emotional space [53]. The abscissa is valence and the ordinate is arousal. Arousal indicates the intensity of emotions: a higher numerical value corresponds to a higher degree of emotional stimulation. The speech signals for high-aroused emotions, such as happiness and anger, include more quasi-periodic and low-frequency parts. The speech signals for Low-arousal emotions, such as sadness and tiredness, include more nonperiodic high-frequency parts and tail parts. The Mel spectrogram has a high resolution in the low-frequency part and is therefore suitable for high-arousal emotions. However, for some emotions with low arousal, more attention should be given to the high-frequency parts to achieve better SER. Therefore, in this paper, the IMel frequency domain is proposed and the IMel spectrogram, which is complementary to the Mel spectrogram and is generated by IMel filter banks, is obtained.
## 5 Field Tests and Discussion on Results
### Field Tests for NB-PLC-Based Demand Side Management System
#### 5.1.3 Field Test for Hybrid Systems  
The field test for the hybrid system includes both types, i.e., LV and MV channels along with the transformer bridge. Laptop B is used to inject the 1V RMS signal located on the energy meter of the LV channel and is received with Laptop D at the same phase but with the MV channel. It is noticeable that the signal has to pass through the transformer bridge while reaching its destination. The length of the hybrid NB-PLC channel is 1100 m. Figure 11 illustrates the locations and measurement setup of hybrid communication system. Figure 8a-c elucidates the SNR profiles for hybrid communication system of residential, commercial, and industrial consumers, respectively. The variations in SNR gains are from \(-15\) dB to 20 dB, which are high in residential consumers, however, the lowest gains can be seen for industrial consumers, whose details are discussed in Section 5.4.
## 4 Discussion
### Strengths and Limitations  
The national scope of the TLSA study strengthened the generalizability of the ADL and IADL status and healthy/risky behavior findings. The longitudinal and prospective design of the TLSA strengthened the effect of behavior changes with time, making it possible to evaluate the effects of behavioral changes on ADL and IADL decline with confidence.  
The limitations of this study should be considered when interpreting its results. The observed effects may be the result of physically active participants being generally healthier. Participants who were lost to follow-up were more likely to be functionally impaired, institutionalized, or in poor health, and their exclusion from the analysis may have underestimated the reported associations. This study was a secondary data analysis. In the TLSA, the data on participants' chronic diseases, physical functions, physical activity, and nutritional assessment was based on the subjects' self-reports. The accuracy of self-reported data varies from person to person, as well as the conditions being reported. The assessment of the participant's physical activity was highly subjective as quantitative measures of activity, duration, and intensity were not available, and the instruments used in the TLSA were not measured objectively. In the TLSA, general disease terms were used in the disease list. Data on individual diseases, including specific pathology and severity, were not collected. The severity of specific diseases and diseases affects the participants' functional status. However, we used the total number of diseases as an ordinal scale to evaluate multimorbidity effects without considering the different effects of specific diseases, severity, and combinations of diseases on physical functioning. Confounding effects should be considered in the analysis.
# Plasmonic Nanomaterials for Micro- and Nanoplastics Detection
## 4 SERS-Based MNPs Detection
### Detection of MNP Using SERS-Based Techniques
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Substrate** & **Plastics** & **EF** & \(\mathbf{\lambda_{\text{exc}}}\) **(nm)** & **LOD (\(\mu\)g/mL)** & **Real Sample LOD** \\ \hline \multirow{8}{*}{Klarite [88]} & & PS 360 nm: EF = 172 & & & \\  & & PS 500 nm: EF = 127 & & & \\  & & PS 1 \(\mu\)m: EF = 97 & & & \\  & & PS 2 \(\mu\)m: EF = 12 & & & \\  & PS-PMMA & PS 5 \(\mu\)m: EF = 20 & & & \\  & 360 nm–5 \(\mu\)m & PMMA 360 nm: EF = 20–30 & 785 nm & – & – \\  & & PMMA 500 nm: EF = 11–15 & & & \\  & & PMMA 2 \(\mu\)m: EF = 2–4 & & & \\  & & PMMA 5 \(\mu\)m: EF = 4–8 & & & \\  & & (Equation (2)) & & & \\ \hline \multirow{8}{*}{AuNRs and AgNWs on cellulose [85]} & & & & & \\  & & & & PS 84 nm: 100 ug/mL & \\  & & AuNRs/RC & & PS 444 nm 50 ug/mL & \\  & & CV: EF = 5.4 \(\times\) 10\({}^{6}\) & 785 nm & & \\  & & AgNWs/RC & & & \\  & & CV: EF = 1.8 \(\times\) 10\({}^{7}\) & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline \multirow{2}{*}{Sputtered Ag on SiO\({}_{2}\)[80]} & & \multirow{2}{*}{PS 200 nm–1 μm} & \multirow{2}{*}{\(p\)-ATP: EF = 2.3 \(\times\) 10\({}^{8}\)} & \multirow{2}{*}{633 nm} & \multirow{2}{*}{PS: 0.5 \(\mu\)g/mL in bottled, tap, and river water.} \\  & & & & & \\  & & (Equation (2)) & & & \\ \hline AAO, MoS\({}_{2}\) layer, and AgNPs [83] & & \multirow{2}{*}{RS 100–300 nm} & \multirow{2}{*}{R6G EF = 1.95 \(\times\) 10\({}^{10}\)} & \multirow{2}{*}{532 nm} & \multirow{2}{*}{R6G: 10\({}^{-12}\) M} & \multirow{2}{*}{–} \\  & & & & & \\ \hline \multirow{2}{*}{AuNSs@Ag [87]} & & & & & \\  & & & & & \\  & & & & & \\ \hline \multirow{2}{*}{Ag/CuO NW} & & & & & \\  & & PS 20 nm: EF = 1107.7 & & & \\  & & (Equation (2)) & & & \\ \hline \multirow{8}{*}{Ag nanowire array [67]} & & & & & \\  & & PS 50 nm: EF = 238,096.10 & & & \\  & & PS 100 m
## 3 The Proposed Tool
### Workflow
#### 3.1.2 Employee Risk Classification Tool  
1. Risk Score Calculation: Each employee is assigned a risk score determined by their daily activities within the organization's network. The risk score is calculated according to Equation (1): \[RiskScore_{j}=\sum_{i=1}^{n}W_{i}\cdot\mid^{k}\] (1) where: \[\bullet\quad RiskScore_{i}\text{ is the RiskScore for the }j\text{-th record}.\] \[\bullet\quad The summation\ \sum_{i=1}^{n}\text{ indicates that we are summing over all features from 1 to }n.\] \[\bullet\quad W_{i}\text{ is the weight associated with the }i\text{-th feature}.\] \[\bullet\quad\mid^{k}\text{ is the indicator function, which equals 1 if the }i\text{-th feature }F_{ij}\text{ for the }j\text{-th record is 1 (indicating first-time abnormal daily activity) and 0 otherwise (indicating no abnormal activity).
2. Dynamic Employee Profiling: Following step 1, employees are dynamically profiled, with their profiles undergoing continuous updates to reflect their behavior within the organization's networks along with their calculated risk scores. In real-life situations, each employee's profile would dynamically evolve, continuously recording observed abnormal behaviors and their associated risk scores. For example, instances of logging in outside business hours would be recorded within the employee's profile, including the occurrence time and the calculated risk score. Any additional behavior would also be added, along with the cumulative risk score needed for the subsequent classification step.
3. Classification of Employees: Utilizing ML, each employee is classified into one of three risk levels (low, moderate, or high) based on the updated data obtained in step 2.
4. Administration Notification: Employees identified as moderate or high risk are reported to administration for necessary interventions, which may include additional training or enhanced monitoring.
## 1 Introduction
### Densest At-Least-\(k\)-Subgraph  
Our final result is a hardness of approximating the Densest At-Least-\(k\)-Subgraph (DALS) problem, which can be stated as follows. Given an edge-weighted graph, find a subset \(S\) of at least \(k\) vertices such that the induced subgraph on \(S\) has maximum _density_, which is defined as the ratio between the total weight of edges and the number of vertices. The problem was first introduced by [PERSON] and [PERSON] [38] who also gave a 3-approximation algorithm for the problem. Shortly after, 2-approximation algorithms for the problem were discovered by [PERSON] [39] and independently by [PERSON] and [PERSON] [40]. We show that, assuming SSEH, this approximation guarantee is essentially the best we can hope for:  
**Theorem 3**.: _Assuming the Small Set Expansion Hypothesis, it is NP-hard to approximate Densest At-Least-\(k\)-Subgraph to within \((2-\varepsilon)\) factor of the optimum for every constant \(\varepsilon>0\)._  
After our manuscript was made available online, we were informed that Theorem 3 was also proved independently by [PERSON] [41]. To the best of our knowledge, this is the only known hardness of approximation result for DALS. We remark that DALS is a variant of the Densest \(k\)-Subgraph (DLS) problem, which is the same as DALS except that the desired set \(S\) must have size exactly \(k\). DLS has been extensively studied dating back to the early 90s [10; 18; 20; 23; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52]. Despite these considerable efforts, its approximability is still wide open. In particular, even though lower bounds have been shown under stronger complexity assumptions [10; 18; 20; 50; 52] and for LP/SDP hierarchies [49; 53; 54], not even constant factor NP-hardness of approximation for DLS is known. On the other hand, the best polynomial time algorithm for DLS achieves only \(O(n^{1/4+\varepsilon})\)-approximation [49]. Since any inapproximability result for DALS translates directly to DLS, even proving some constant factor NP-hardness of approximating DALS would advance our understanding of approximability of DLS.
# Evaluation of YOLO Object Detectors for Weed Detection in Different Turfgrass Scenarios
## 3 Results and Discussion  
The Analysis of variance revealed significant differences on models performance metrics (Table 3). Among these models, YOLOv5s, YOLOv6n, and YOLOv6l achieved the highest precision (P) scores with values of 0.9445 \(\pm\) 0.0281, 0.9456 \(\pm\) 0.0146, and 0.9414 \(\pm\) 0.023, respectively. Notably, these three models exhibited significantly higher precision scores compared to EfficientDet, which yielded the lowest precision score of 0.9033 \(\pm\) 0.0244 (\(p\)!0.05). Conversely, no significant differences in precision scores were observed among the other models. Regarding the recall (R) metric, YOLOv7 yielded the best results with a score of 0.9552 \(\pm\) 0.0136. No significant differences were found between YOLOv7 and YOLOv7x, as well as all scales of YOLOv8 models. However, YOLOv7 displayed significant differences when compared to EfficientDet (\(p\)!0.01) and all scales of YOLOv5 and YOLOv6 models (\(p\)!0.001, except for YOLOv5n with a \(p\)!0.05). For the mean average precision at an intersection over union (IOU) threshold of 0.5 (mAP_0.5), YOLOv8 and YOLOv7 demonstrated the highest performance with scores of 0.9594 \(\pm\) 0.0214 and 0.955 \(\pm\) 0.0263, respectively. No significant differences were observed between these models and all scales of YOLOv6, YOLOv7x, and YOLOv8 (except for YOLOv8s with \(p\)!0.05), as well as YOLOv5s and YOLOv5l. Conversely, all other models exhibited significantly lower mAP_0.5 values, with EfficientDet performing the worst (\(p\)!0.01, with a score of 0.8931 \(\pm\) 0.0312). In terms of the mAP_0.5 to 0.95 metric, all YOLOv5 models (except YOLOv5n with \(p\)!0.001) achieved the best results, ranging from 0.8841 \(\pm\) 0.0795 for YOLOv5m to 0.8606 \(\pm\) 0.0442 for YOLOv5l. YOLOv8l exhibited a significantly lower mAP_0.5 to 0.95 score (\(p\)!0.01) compared to other YOLOv8 models, but it was the best-performing model within the YOLOv8 series, achieving a score of 0.8043 \(\pm\) 0.015. No significant differences were found between YOLOv8l and other YOLOv8 models, as well as YOLOv7. Notably, all other models displayed significantly lower mAP_0.5 to 0.95 scores, with YOLOv5n performing the worst (\(p\)!0.001) at a value of 0.7002 \(\pm\) 0.012. Figure 2: Trial framework for models’ train and evaluation on different turfgrass scenario. According to results from the five-fold cross-validation experiment, ten models (YOLOv5n, YOLOv5s, YOLOv5m, YOLOv6n, YOLOv6l, YOLOv7, YOLOv7x, YOLOv8s, YOLOv8l, and YOLOv8x) were trained on the 'Weeds' public dataset for 100 epochs (Table 2) and best performing models for each YOLO version were selected to be tested on the four different test datasets. Performances of the ten models are resumed and depicted in Figure 3.