esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology and Interpretation of Dilatometer Tests
The unit weight of soils (cohesive soils and non-cohesive soils) and their states are also presented in this diagram. Soils are classified as organic soils where the material index \(I_{D}\) < 0.6 and the dilatometer modulus \(E_{D}\) < 1.2 MPa. Based on the analysis of dilatometer test results for pre-consolidated cohesive soils and organic soils, [PERSON] [7] proposed a revision of the value of the material index I\({}_{\rm D}\) by taking into account the impact of pre-consolidation on the change of its value. The adjusted value of the material index \(I_{D(bar)}\) according to [PERSON]'s recommendations [7] can be determined from the following relationships (Figure 3):  
- for depth < 2.0 m at \(K_{D}\) > 2.5  
\[I_{D(bar)}=I_{D}-0.075\cdot(K_{D}-2.5) \tag{9}\]  
- for depth \(\geq\) 2.0 m at \(K_{D}\) > 2.5  
\[I_{D(bar)}=I_{D}-0.035\cdot(K_{D}-2.5) \tag{10}\]  
If \(K_{D}\) < 2.5 and/or \(I_{D}\)\(\leq\) 0.1 to \(I_{D(bar)}\) = \(I_{D}\). The nomogram chart proposed by [PERSON] [7] to determine the type of soil and its bulk density, based on the adjusted value of the material index \(I_{D(bar)}\) and the dilatometer modulus \(E_{D}\), is shown in Figure 4. The characterization of soil on the corrected material index \(I_{D(bar)}\)\(\leq\) 0.6 (Table 1) is based on undrained shear strength \(\tau_{fit}\), using the division proposed by [PERSON] and co-workers [25] (Table 2). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Soil Type** & \multicolumn{3}{c}{**Material Index \(I_{D}\) (-)**} \\ \hline \multirow{5}{*}{Organic soils and cohesive soils} & Peat/Sensitive clays & \textless{}0.10 \\  & Clay & 0.10–0.35 \\  & Silty clay & 0.35–0.60 \\  & Clayy silt & 0.60–0.90 \\  & Silt & 0.90–1.20 \\  & Sandy silt & 1.20–1.80 \\  & Silty sand & 1.80–3.30 \\  & Sand & \textgreater{}3.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Soil classification based on material index \(I_{D}\)[1]. Figure 3: Difference between material index (\(I_{D(Mar)}\)) and material index \(I_{D(bar)}\) for different clay content depending on the horizontal stress index \(K_{D}\)[7]. Figure 2: Chart for estimating soil type and unit weight for soil (normalized to \(\gamma_{w}\)) [1,2]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Description of Soil State** & **Undrained Shear Strength** \(\tau_{f\mu}\) **[kPa]** \\ \hline Very soft & \textless{}12.5 \\ Soft & 12.5–25 \\ firm & 25–50 \\ Stiff & 50–100 \\ Very stiff & 100–200 \\ Hard & \textgreater{}200 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Soil states based on undrained shear strength [25]. Figure 4: Chart for estimating mineral and organic soils [7].
## 4 Features and Issues of Existing Applications
### Objective, Application Domain and Test Environment
Thus, most existing applications for MMH tasks are used to assess postural risks or the fatigue of human workers [14; 15; 25; 26; 39]. Additionally, [PERSON] et al. [30] considered preventing collisions between human workers performing these MMH tasks and robots. In contrast, four research papers considered other tasks for human workers. [PERSON] et al. [29] and [PERSON] and [PERSON] [37] developed DT applications to prevent workplace collision with HRC. However, these applications were applied to human workers performing arbitrary actions rather than specific assembly or MMH tasks. In other words, these research papers focused on the technical demonstration of human-focused DT for OSH. Moreover, the applications developed by [PERSON] et al. [13] and [PERSON] et al. [36] allow human workers to perform daily operations, including travel within relatively large workplaces. Figure 8 shows that only about a quarter of the relevant research papers performed experiments in practical workplaces [13; 25; 27; 34; 35; 36; 42], while the applications in other research papers were studied under laboratory environments. This suggests that the conceptual design and validation of technical feasibilities are still essential matters for human-focused DT for OSH.
## 3 Regulation of DAPG Biosynthesis
### Regulation by the Gac/Rsm Regulatory Network  
The Gac/Rsm signal transduction pathway is well conserved in _Gammaproteobacteria_ and regulates, at the posttranscriptional level, the production of several antibiotics, such as hydrogen cyanide, pyoluteorin, phenazine and DAPG [48; 49]. The GacA/GacS two-component system governs a complex signal transduction pathway, which involves small non-coding regulatory RNAs (RsmX/Y/Z) and translational repressors (RsmA and RsmE). A _gaC_ or _gaC_ mutant is unable to produce DAPG [49; 50], indicating that GacA/GacS positively regulates DAPG production. The GacA/GacS two-component system is composed of a membrane-bound sensor kinase (GacS) and a cytosolic cognate response regulator (GacA). The Gac/Rsm transduction cascade is initiated by the reception of a signal, which remains to be characterized [51]. GacS undergoes autophosphorylation and subsequently activates GacA by phosphotransfer. Two membrane-bound sensor kinases, LadS and RetS, modulate GacS activity by influencing its phosphorylation state. LadS stimulates GacS activity whereas RetS negatively regulates GacS activity [51; 52; 53; 54]. Notably, RetS was shown to directly interact with GacS [55] to negatively regulate its activity at 35 \({}^{\circ}\)C, preventing the production of antibiotics [53]. Upon activation by GacS, GacA upregulates the expression of three small non-coding RNAs, RsmX, RsmY and RsmZ [56]. GacA activates _rsmXYZ_ expression by interacting with a conserved palindromic upstream activating sequence (UAS) present in the promoter of _rsmXYZ_[57]. Activation of _rsmXYZ_ expression also requires other transcriptional activators that may interact with phosphorylated GacA [57]. These three small non-coding RNAs have a high affinity for the translational repressors RsmE and RsmA [56; 58]. RsmA and RsmE are RNA-binding proteins from the RsmA/CsrA family that bind to specific structures located near the ribosome binding site in the leader sequence of target mRNAs, preventing ribosome binding and promoting mRNA decay [50; 59; 60; 61]. A typical hexaloop structure is present in the leader sequence of the _phLA_ mRNA and this sequence was shown to be recognizable by RsmA/RsmE [62]. Upon activation of GacA, RsmA and RsmE are sequestered by RsmXYZ, relieving translational repression of target mRNAs [63; 64].  
The GacS/GacA signal transduction pathway is influenced by the population density and/or the nutritional state of the cells [54; 56; 57; 65]. The expression of _rsmXYZ_ increases at the end of the exponential growth phase [56; 57; 66]. This could originate from the accumulation of a signal in the medium, that activates the Gac/Rsm regulatory network [56; 57; 66]. It could also be the result of the depletion of the nutrients present in the growth medium, which activates secondary metabolism. The alarmone guanosine tetraphosphate (ppGpp), a signal molecule produced under nutrient limitation, was precisely shown to stimulate the Gac/Rsm signal transduction pathway [54].
# One-Class LSTM Network for Anomalous Network Traffic Detection
## 4 Experimental Setup
### Datasets  
Although the proposed method applies to any feature representation context, our main concern is large-scale network traffic data. We compared all methods on three widely used real-world datasets, as summarized in Table 1. Each of the data sets was further processed to create a good anomaly detection task, as described in the next section.  
1. The NSL-KDD dataset is the benchmark dataset in the field of network security, and it can provide consistent and comparable evaluation results for different research  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Datasets** & **Instances** & **Anomalies** & **Features** \\ \hline \multirow{2}{*}{NSL-KDD} & KDDTrain+ & 67,343 (53.46\%) & 58,630 (46.54\%) & 41 \\  & KDDTest+ & 9711 (43.08\%) & 12,833 (56.92\%) & 41 \\ \hline \multirow{2}{*}{CIC-IDS 2017} & CIC-DDoS & 97,718 (43.29\%) & 128,027 (56.71\%) & 78 \\  & CIC-PortScan & 127,537 (44.52\%) & 158,930 (55.48\%) & 78 \\ \hline \multirow{2}{*}{MAWILab} & MAWILab-20200102 & 22,228,204 (97.06\%) & 673,825 (2.94\%) & 109 \\  & MAWILab-20201203 & 25,195,651 (96.80\%) & 832,028 (3.20\%) & 109 \\ \hline \hline \end{tabular} (1) The NSL-KDD dataset is the benchmark dataset in the field of network security, and it can provide consistent and comparable evaluation results for different research  
\end{table}
Table 1: Summary of the datasets used in the experiments.  
works [30]. Furthermore, the number of records in the NSL-KDD datasets is reasonable, and each record contains 41-dimensional features. The anomaly data mainly include thirty-nine types of network attacks across four categories.
2. The CIC-IDS2017 dataset contains benign data and the most up-to-date common attacks, so it resembles true real-world data [31]. The data contain a total of five days of network traffic in July 2017. The implemented attacks include Brute-Force FTP, Brute Force SSH, DoS, Heartbleed, Web Attack, PortScan, Infiltration, Botnet, and DDoS. In this study we randomly selected Friday's traffic as the experimental dataset, which contained two types of abnormalities, DDoS and PortScan.
3. MAWILab is a database that assists researchers in evaluating traffic anomaly detection methods [32; 33]. The dataset has been updated daily since 2001 to include new traffic from upcoming applications and anomalies; this has been ongoing for over 20 years. In this study, the data collected on the first collection day in January and December 2020 were selected as the experimental dataset. We used the code provided in [1] to process the original network traffic and extract the inherent features. The processed data contained 109-dimensional features, and the data distribution is shown in the table above.
## 3 Simulation and Results Discussion  
### Simulation Condition  
Firstly, the study has tried to analyze the environmental characteristics of the flight area by the above model in Section 2.4 for further parameter settings. Figure 5 display the variation of the maximum solar irradiance intensity with altitude in the south china sea (105\({}^{\circ}\)E, 4\({}^{\circ}\)N, data = 266). Besides, a realistic wind speed data as a function of the altitude from high altitude detection balloon isalso been shown. It can be concluded from the analysis that the maximum solar irradiance intensity will not change drastically with the altitude after reaching 20 km. Meanwhile, the wind speed in the environment from 17.5 km to 27.5 km is relatively low, which is beneficial to mission execution.  
After the above preparation works, a series of numerical simulations are computed to verify the proposed method. It will be very efficient to solve the flight trajectory optimization problem into a nonlinear programming problem. The parameters of initial configuration are listed in Table 2, which is referred from Zephyr 7 [2; 4; 8], as the Zephyr 7 is been introduced comprehensive and detailed in all the literatures among the high-altitude SPA. In addition, the accurate parameters of Zephyr 7 is more conducive to the advantage and reliability of the simulation results by this strategy.  
The set of simulation conditions and constraints of variables are listed in Tables 3 and 4, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Value** & **Description** \\ \hline \(m_{struct}\) (kg) & 37 & Mass of structure \\ \(m_{bat}\) (kg) & 16 & Battery mass \\ \(E_{bat}\) (Wh/kg) & 350 & Energy density of battery \\ \(b\) (m) & 22.5 & Span length \\ \(S_{W}\) (m\({}^{2}\)) & 25.3 & Wing area \\ \(S_{SC}\) (m\({}^{2}\)) & 20.24 & Area of photovoltaic cells \\ \(\eta_{SC}\) (\(\cdot\)) & 0.2 & efficiency of photovoltaic cell \\ \(\eta_{MPPT}\) (\(\cdot\)) & 0.9 & Efficiency of MPPT \\ \(\eta_{mot}\) (\(\cdot\)) & 0.85 & efficiency of aircraft motor \\ \(\eta_{plr}\) (\(\cdot\)) & 0.8 & efficiency of aircraft propeller \\ \(\eta_{latin}\) (\(\cdot\)) & 0.9 & Efficiency of battery for charging \\ \(\eta_{labout}\) (\(\cdot\)) & 0.9 & Efficiency of battery for discharging \\ \hline \end{tabular}
\end{table}
Table 2: The parameters of initial configuration for model.  
Figure 5: Variations of maximum solar irradiance and wind velocity with altitude.
# A Background Subtraction Algorithm in Complex Environments Based on Category Entropy Analysis
###### Abstract  
Background subtraction is a mainstream algorithm for moving object detection in video surveillance systems. It segments moving objects by using the difference between the background and input images. The key to background subtraction is to establish a reliable initial background. In this study, we propose a background subtraction algorithm based on category entropy analysis that dynamically creates color categories for each pixel in the images. The algorithm uses the concept of a joint category to build background categories that can adapt to the color disturbance of the background. Furthermore, in order to overcome dynamic background environments, this paper proposes the concept of color category entropy to estimate the number of necessary background categories and establish sufficient and representative background categories to adapt to dynamic background environments. In addition, recent mainstream methods for background subtraction were implemented and analyzed in comparison with our algorithm.  
Keywords:computer vision; background subtraction; object detection; category entropy analysis; internet of things (IoT); dynamic background environments +
Footnote †: journal: Applied Sciences
# An Energy Efficient Sink Location Service for Continuous Objects in Wireless Sensor Networks
###### Abstract  
In wireless sensor networks (WSNs), detection and report of continuous object, such as forest fire and toxic gas leakage, is one of the major applications. In large-scale continuous object tracking in WSNs, there might be many source nodes simultaneously, detecting the continuous object. Each nodes reports its data to both a base station and mobile workers in the industry field. For communication between the source nodes and a mobile worker, sink location service is needed to continuously notify the location of the mobile worker. But, as the application has a large number of sources, it causes a waste of energy consumption. To address this issue, in this paper, we propose a two-phase sink location service scheme. In the first phase, the proposed scheme constructs a virtual grid structure for merging the source nodes. Then, the proposed scheme aggregates the merging points from an originated merging point as the second phase. Simulation results show that the proposed scheme is superior to other schemes in terms of energy consumption.  
energy-efficiency; mobile sinks; continuous objects; wireless sensor networks; routing protocols; location service +
Footnote †: journal: Sensors
## 5 Simulations and Experiments
There are a few angles that must be defined properly for the purpose of turning walk. In Figure 16, the green line marks the COM angle (tangent direction of the turning motion) which equals the value of the upbody angle. It was obtained based on Formula (21) and Formula (22). The blue line is the foot angle, and the foot angle is planned before plotting the COM trajectory. In the single support period, the foot of the swing leg turns while the support leg maintains its previous state and in the double support period, the foot maintains its previous state. All these angles increase at the same average speed in one walk cycle so that at the end of every walk cycle, the direction of the feet and upbody is the same. We next applied the proposed method to an actual humanoid robot identical to the one modeled in the simulation platform and under the same parameters as the simulation. As shown in Figure 17, \(D_{\text{step}}=0.33\) m, \(T_{\text{step}}=1.2\) s, and the robot achieved turning walk at a speed of 1 km/h. Figure 16: Angle data for gait planning. Figure 15: Hip yaw angle characteristics. Then, we measured the actual ZMP of the robot calculated by the force sensors and converted it to the IRF, as shown in Figure 18. Another experiment was also done which generated the robot's COM trajectory directly by the LIP as is shown in Figure 19. By using the proposed method, The actual ZMP trajectory of the robot was able to better follow the planned ZMP both in the lateral direction and the longitudinal direction. This agrees with the results of the simulation results. In the simulation shown in Figure 13, the COM trajectory deviated to one side in order to make the actual ZMP trajectory follow the planned ZMP trajectory. However, the traditional LIP does not take into account the effect of the robot's own rotation on the actual ZMP, so in the lateral direction, the actual ZMP is offset to one side by a distance form the planned ZMP. In addition, in the longitudinal direction, there are no changes in dynamics, so the actual ZMP can also follow the planned ZMP well. Figure 19: ZMP reference vs. actual with previous LIP (**a**): Lateral ZMP, (**b**): Longitudinal ZMP. Figure 17: Experiments in the real humanoid robot. Figure 11:0 ZMP reference vs. actual with proposed method (**a**): Lateral ZMP, (**b**): Longitudinal zero moment point (ZMP).
## 3 Control Law and Stability
### The Design of Adaptive Fuzzy Control Based on the Backstepping Method
Then the following equation can be obtained from Equation (38):  
\[\dot{V}\leq-\sum_{i=1}^{n}a_{i}e_{i}^{2}-\sum_{i=1}^{n}\frac{k_{i}}{2r_{i}} \widetilde{\theta}_{i}^{T}\widetilde{\theta}_{i}+\sum_{i=1}^{n}\frac{2k_{i}}{ r_{i}}\widetilde{\theta}_{i}^{T}\widetilde{\theta}_{i}^{*}+\frac{1}{2}\sum_{i=1}^{n} \epsilon_{i}^{2}+\sum_{i=1}^{n}\frac{1}{2}\rho^{2}w_{i}^{2}=-d_{0}\sum_{i=1}^ {n}\epsilon_{i}^{2}+b_{0}+\frac{1}{2}\rho^{2}w_{i}^{2} \tag{41}\]  
Integrating the above equation in [0, \(T\)], because of \(-\frac{1}{d_{0}}V(T)\leq 0\), the convergence result of the system is as follows:  
\[\sum_{i=1}^{n}\int_{0}^{T}e_{i}^{2}(s)ds\leq\frac{1}{d_{0}}V(0)+\frac{1}{d_{0} }b_{0}T+\sum_{i=1}^{n}\frac{1}{2d_{0}}\int_{0}^{T}\rho^{2}w_{i}^{2}dt \tag{42}\]  
According to the above equation, the system converges, and the convergence accuracy of the final error depends on the upper bounds of the perturbation and approximation errors.
## 5 Evaluation Experiment
### Experiment B  
Experiment B was performed to evaluate the position estimation using two microphone arrays: M1 and M2. Figure 11 depicts the setup of the sound source positions and the microphone arrays for Experiment B. The experiment field was a square area of 30 m in length and width. The coordinates of M1 and M2 were, respectively, (0, 30) in the upper left corner and (30, 0) in the lower right corner. The microphone frontal orientations of M1 and M2 were, respectively, horizontal and vertical. For this arrangement, the positions on the diagonal between M1 and M2 were undetermined because the intersection of \(L1\) and \(L2\) was indefinite in the condition of \(\theta_{1}=-\theta_{2}\). Therefore, the estimation target comprised 12 positions at 10 m intervals along the vertical and horizontal axes, excluding coordinates on the diagonal.  
Figure 12 depicts the angle estimation results for 8 of 12 positions. The respective output waves exhibited a distinct peak, especially for shallow angles.  
Figure 11: Setup of sound source positions and microphone arrays for Experiment B.  
Table 7 presents the experiment results: \(\theta_{g1}\), \(\theta_{g2}\), \(\theta_{m1}\), \(\theta_{m2}\), \(E_{1}\), and \(E_{2}\). The mean error values of M1 and M2 were, respectively, \(1.25^{\circ}\) and \(0.92^{\circ}\). The highest error of M1 was \(7^{\circ}\) at P7. This error increased the mean M1 error.  
Figure 12: Angle estimation results obtained at eight positions (P1, P2, P4, P5, P8, P9, P11, and P12).  
Figure 14 presents scatter plots of the GT coordinates and estimated coordinates. The distribution results for \(x\) and \(y\) are presented, respectively, in the left and right panels. Compared to Experiment A, the arrangement of the microphone array for Experiment B used 90\({}^{\circ}\), which is half of the effective measurement range. However, the estimated angle error increased as it approached 90\({}^{\circ}\). The error distribution trend demonstrated that the estimated position coordinates were calculated as a larger value than the GT position coordinates.
# An Improved Heteroscedastic Modeling Method for Chest X-ray Image Classification with Noisy Labels
## 3 Methodology
### The Proposed GMM-HM
#### 3.2.3 Noisy-Aware Classification  
The noise-aware classification network consists of two branches for dealing with the clean and noisy samples, respectively. The clean branch is as same as the classical classification networks, e.g., ResNet [1]. The noisy branch is placed in a multivariate normally distributed latent variable on the final hidden layer of the classifier. The clean and noisy branches share the weights of the CNN backbone. The clean and noisy samples are fed into the corresponding branches to be classified.  
Let the predicted logits \(r(x)\in R^{C}\) of an image \(x\) as \(r(x)=f(x;\theta)\), where \(f\) represents the deep networks with parameter \(\theta\). We omit the subscript of \(x\) for simplification. Next, to make a clear distinction in this section, we denote the samples coming from clean set \(\mathcal{S}_{clean}\) and noisy set \(\mathcal{S}_{noisy}\) as \(x_{c}\) and \(x_{n}\), respectively. The predicted logits of clean samples \(r(x_{c})\) are directly input into the classifier. The logits of noisy samples \(r(x_{n})\) are first fed into the heteroscedastic layer to compute the heteroscedastic representation \(u(x_{n})\), which is then fed into the final classifier.  
The \(u(x_{n})\in R^{C}\) is computed as follows. We compute the deterministic item as \(v(x_{n})=W_{v}r(x_{n})+b_{v}\), where \(W_{v}\) and \(b_{v}\) are the parameters of a \(1\times 1\) convolutional layer. The stochastic item \(\epsilon(x_{n})\in R^{C}\) of the heteroscedastic layer is implemented with a low-rank approximation method by \(\epsilon(x_{n})=V(x_{n})V(x_{n})^{T}\), where \(V(x_{n})\) is a \(C\times R\) matrix, and \(R\ll C\). In practice, the \(V(x_{n})\) is computed as an affine transformation of \(r(x_{n})\) as \(V(x_{n})=W_{V}r(x_{n})+b_{V}\), where \(W_{V}\) and \(b_{V}\) are the parameters of a \(1\times 1\) convolutional layer. In order to ensure the positive semi-definiteness of the covariance matrix, a \(C\)-dimensional vector \(d^{2}(x_{n})\) is added to the diagonal of \(V(x_{n})V(x_{n})^{T}\),  
\[\epsilon=d(x_{n})\odot\epsilon_{C}+V(x_{n})\epsilon_{R} \tag{6}\]  
where \(d(x_{n})=W_{d}r(x_{n})+b_{d}\) is a diagonal correction matrix. \(W_{d}\) and \(b_{d}\) are the parameters of a \(1\times 1\) convolutional layer. \(\epsilon_{C}\in R^{C}\) and \(\epsilon_{R}\in R^{K}\) are sampled from \(\mathcal{N}(0_{C},I_{C\times C})\) and \(\mathcal{N}(0_{R},I_{R\times R})\), respectively. \(\odot\) denotes the element-wise multiplication. Finally, the latent vector \(u(x_{n})\) of input \(x_{n}\) is computed by  
\[u(x_{n})=v(x_{n})+d(x_{n})\odot\epsilon_{C}+V(x_{n})\epsilon_{R}. \tag{7}\]