esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Case Study
### Performance of Different Passenger Flow  
In this section, we use the three scenarios of the morning, noon, and evening peaks of the Line L, where the total numbers of passengers are 14,704, 5753, and 16,074, respectively, to analyze the Pareto fronts under different passenger flow conditions. The morning and evening peaks are called the large passenger flow scenario, and the noon peak is called the small passenger flow scenario. For a fair comparison, we compare the unit cost, which is the ratio of the cost of variable items to the passenger kilometers, and average passenger travel time perception (ATTP) of each scenario.  
The Pareto front of each scene is shown in Figure 23. The real timetable of the morning peak has the lowest unit cost, the noon peak has the highest unit cost, and the evening peak is in the middle. The ATTP decreases in the order of morning, noon, and evening.  
The Pareto front presents a nonlinear relationship between two objects in each scenario, but the curves are straighter in the morning and evening peaks than in the noon peak, which indicates that the ATTP is more sensitive to the change in the unit cost in the morning and evening peaks. This is because, when the unit cost is reduced, the train congestion in the large passenger flow scenario changes significantly, while in the small passenger flow scenario, there are few sections exceeding the congestion penalty threshold, and the average congestion penalty time is only approximately 43 s. In addition, in the small passenger flow scenario, the average waiting time, which is the second-largest factor affecting the average travel time, is approximately 300 s, which affords a large unit cost when reducing the average waiting time by increasing the service frequency.  
In region III, the maximum optimization rate of ATTP is 7.4% with a 126 s reduction in the morning peak, while it is 5.8% with a 92 s reduction in the noon peak and 2.3% with a 32 s reduction in the evening peak. The maximum optimization rate per unit cost is 14.4% with a 0.022 CNY/pkm reduction in the morning peak, while it is 3.7% with a 0.0125 CNY/pkm reduction in the noon peak and 6.8% with a 0.0125 CNY/pkm reduction in the evening peak.  
It is interesting that the optimization rates of the morning and evening peaks are mostly higher than that of the noon peak. One potential reason for this is that the temporal and spatial imbalances of the passenger flow in the afternoon peak are lower, i.e., TIT, SIT, and IS are all less than those in the morning and evening peaks. This situation may reduce the applicability of line plans with multiple strategies, which is consistent with the original intention of using line plans with multiple strategies to deal with the imbalances in trains and station loads (see Section 1).
## 5 Conclusions  
In this paper, we proposed an AGD detection method based on the fusion of meaningful word segmentation and n-gram sequence features. We used meaningful word segmentation to describe the composition of dictionary-based AGDs and used the mean and standard deviation to describe the word distribution of the domain name. We constructed additional 11-dimensional statistical features based on the word segmentation results. In addition, we used 3-gram and 1-gram sequences to characterize the sequence features of dictionary-based AGDs and character-based AGDs, respectively. Finally, we fused these four kinds of features to realize an end-to-end detection of AGDs, which significantly improved the detection effect of dictionary-based AGDs and accounted for the detection performance of character-based AGDs. We conducted experiments on three datasets, which separately contained only dictionary-based AGDs, only character-based AGDs, and both character-based and dictionary-based AGDs. The testing results demonstrated that our method achieved the highest accuracy on each of the three datasets and reached an accuracy of 97.25% on the full dataset, which was higher than that of the state-of-the-art methods.  
The recall rate of some dictionary-based DGA families was relatively low on the full dataset. We will further analyze these families to improve the detection performance, and then we will deploy them in the real-world network to detect the AGDs.  
Conceptualization, S.C. and B.L.; methodology, S.C. and C.X.; validation, S.C., Y.C. and C.X.; writing--original draft preparation, S.C.; writing--review and editing, B.L. and Y.C.; supervision, B.L.; funding acquisition, B.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by State Key Laboratory of Software Development Environment grant number SKLSDE-2020 ZX-02.  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} DGA & Domain generation algorithm \\ AGD & Algorithmically generated domains \\ DNS & Domain name system \\ IP & Internet protocol \\ C\&C & command and control \\ TLD & Top-level domain name \\ NXDomain & Non-existent domains \\ SVM & Support vector machines \\ RF & Random forests \\ RNN & Recurrent neural network \\ CNN & Convolutional neural network \\ LSTM & long short-term memory \\ Std & standard deviation \\ ccTLD & Country code TLD \\ \end{tabular}
## 3 Numerical Results
### Thin-Walled Two-Cell Composite Box Beam  
The geometric properties of the thin-walled two-cell box beam are given in Figure 15, where \(a_{1}=0.2\) m, \(a_{2}=0.1\) m, and \(t=0.005\) m, while the material distribution of that beam is shown in Figure 16. As can be seen here, orthotropic material with different alignment angles is used. The composite material of that beam is arranged as [\(0^{\circ}/90^{\circ}/0^{\circ}\)] cross-ply, and orthotropic properties of these materials are detailed above.  
Large Deflection of Thin-Walled Two-Cell Composite Box Beam for Post-Buckling  
In this section, the buckling behaviour of the cantilever twin-cell composite box beam, where large deflections occur under axial load, is examined. The equilibrium curve of a two-cell box beam with polynomial degree L9 is shown in Figure 17 for both NL and L regimes.  
Figure 16: Thin-walled two-cell composite beam cross section.  
Figure 15: Thin-walled two-cell beam cross section.  
Figure 18 shows the distributions of axial and shear stress components at two different P* values of the composite box beam subjected to axial loading for the NL regime at L9. The variation of the axial stress component is similar for both loads. However, the stress level increases at the upper parts of the beam as the load increases. The distribution of the shear stress component along the z-axis at different load effects is similar.  
Figure 19 shows the distributions of axial and shear stress components of beam in L and NL regimes for different P* values in L9. The distributions of the axial stress components are similar for all loads in the L regime. However, the P* load affects the top of the beam at lower levels. The distribution of the same stress component in the NL regime is similar for all loads. In the shear stress distribution, the distribution does not change for different load values both in the L regime and in the NL regime.  
Figure 17: The equilibrium curves for L9 of two-cell composite beam subjected to axial loading, P* = 4_PL2_/_π2_EI_.  
Figure 18: Distribution of axial and shear stress components for (**a**) P* = \(-0.0086\) and (**b**) P* = \(-0.162\) loads in NL regime at L4 and L9 extensions at \(x=-a_{2}/2\) and \(y=L/2\) in the two-cell composite beam subject to axial loading; \(\sigma_{yy}^{*}=\sigma_{yy}/\sigma_{yy_{max}}\) and \(\sigma_{yz}^{*}=\sigma_{yz}/\sigma_{yz_{max}}\).
# Dependency Analysis based Approach for Virtual Machine Placement in Software-Defined Data Center
## 3 Problem Definition
### System Model  
We consider an SDDC in which there are multiple VMs on each PM, and they are connected to each other by an internal virtual network that is, in turn, connected to the physical network, as shown in Figure 2. Suppose that there are \(n\) VMs placed on \(m\) PMs in the data center. Let the set of VMs be represented by \(\mathbf{V}=\{v_{1},v_{2},\cdots,v_{n}\}\) and let \(v.c\), \(v.m\), and \(v.s\) be the resources (CPU, memory, and storage, respectively) required for running a VM \(v\). In addition, let the set of PMs be represented by \(\mathbf{P}=\{p_{1},p_{2},\cdots,p_{m}\}\), and let \(p.c\), \(p.m\), and \(p.s\) be the resources (CPU, memory, and storage, respectively) available on PM \(p\).  
Let \(\mathbf{X}\) be an \(n\)-by-\(m\) binary matrix where each element, \(x_{i,j}\), is equal to 1 when \(v_{i}\) is placed on \(p_{j}\) and 0 otherwise.  
\[\mathbf{X}=\begin{pmatrix}x_{1,1}&\ldots&x_{1,m}\\ \vdots&\ddots&\vdots\\ x_{n,1}&\ldots&x_{n,m}\end{pmatrix}.\]  
Since the data center is an SDDC, the status of the flow information between the VMs is available via the SDN controller. Based on this information, a traffic information matrix can be created for the VMs. Let \(\mathbf{W}\) denote the \(n\)-by-\(n\) traffic information matrix with elements, \(w_{i,j}\), representing the data rate between \(v_{i}\) and \(v_{j}\).  
\[\mathbf{W}=\begin{pmatrix}w_{1,1}&\ldots&w_{1,n}\\ \vdots&\ddots&\vdots\\ w_{n,1}&\ldots&w_{n,n}\end{pmatrix}.\]  
The total data rate in the physical network can be computed as follows:  
\[D_{p}(\mathbf{X})=\sum_{i=1}^{n}\sum_{j=1}^{n}w_{i,j}\cdot(1-\sum_{k=1}^{m}x_ {i,k}\cdot x_{j,k}), \tag{1}\]  
where \((1-\sum_{k=1}^{m}x_{i,k}\cdot x_{j,k})\) is equal to 1 when the \(v_{i}\) and \(v_{j}\) are placed on different PMs, else it is 0. Thus, \(D_{p}\) in Equation (1) is the total sum of data rates between VMs that are placed on different PMs. The optimization problem for VM placement can be formulated as follows:  
Figure 2: VM placement cases in data center. (**a**) random VM placement; and (**b**) traffic dependency based VM placement.  
\[\begin{split}\mathbf{X}^{*}=\arg\min_{\mathbf{X}}D_{p}(\mathbf{X})\\ \text{subject to }&\sum_{i=1}^{n}v_{i}.c\cdot x_{i,k} \leq p_{k}.c\\ &\sum_{i=1}^{n}v_{i}.m\cdot x_{i,k}\leq p_{k}.m\text{ for }\forall k \in[1,m]\\ &\sum_{i=1}^{n}v_{i}.s\cdot x_{i,k}\leq p_{k}.s.\end{split} \tag{2}\]  
The constraints for the VM placement is that the sum of the necessary resources for the VMs in each PM is less than or equal to resources available on that PM. To solve this problem, a scalable VM-placement strategy based on the network traffic between the VMs is proposed. The processes of the proposed approach are described in detail in the following section.
## 3 System Model
### Data Preprocessing
#### 3.3.3 Sampling Data  
Based on the above formula, the center distance and elevation angle values between the UE and each cell were obtained, and standard scaling was used to standardize the distribution of features. There are data on the rate of change of the center distance and altitude angle between cells for each UE at 1 ms intervals. Sampling was performed with data per 10 ms to fit the propagation delay unit of NTN. In addition, N windows of past propagation delay intervals are used to predict the center distance and altitude angle between the UE and the cell after the propagation delay based on the location information that the UE has received from the satellite. Based on the window data for each of the past N propagation delays, the center distance and altitude angle between the UE and the cell after the propagation delay are predicted. Figure 11 shows the data sampling process.
###### Abstract  
Aiming at the problems of poor decomposition quality and the extraction effect of a weak signal with strong noise by empirical mode decomposition (EMD), a novel fault diagnosis method based on cascaded adaptive second-order tristable stochastic resonance (CASTSR) and EMD is proposed in this paper. In the proposed method, low-frequency interference components are filtered by using high-pass filtering, and the restriction conditions of stochastic resonance theory are solved by using an ordinary variable-scale method. Then, a chaotic ant colony optimization algorithm with a global optimization ability is employed to adaptively adjust the parameters of the second-order tristable stochastic resonance system to obtain the optimal stochastic resonance, and noise reduction pretreatment technology based on CASTSR is developed to enhance the weak signal characteristics of low frequency. Next, the EMD is employed to decompose the denoising signal and extract the characteristic frequency from the intrinsic mode function (IMF), so as to realize the fault diagnosis of rolling bearings. Finally, the numerical simulation signal and actual bearing fault data are selected to prove the validity of the proposed method. The experiment results indicate that the proposed fault diagnosis method can enhance the decomposition quality of the EMD, effectively extract features of weak signals, and improve the accuracy of fault diagnosis. Therefore, the proposed fault diagnosis method is an effective fault diagnosis method for rotating machinery.  
fault diagnosis; cascaded adaptive stochastic resonance; empirical mode decomposition; second-order tristable state; chaotic ant colony optimization; feature extraction +
Footnote †: journal: Applied  
1
# Middleware for Plug and Play Integration of Heterogeneous Sensor Resources into the Sensor Web
## 2 Sensor Integration into Observation Platforms
### Protocols and Standards
#### 2.2.2 Sensor Model Language  
The OGC Sensor Model Language (SensorML) permits to encode detailed sensor descriptions within an XML file [29]. Its main goal is to enhance interoperability, making sensor descriptions understandable by machines and shareable between intelligent nodes. Moreover, additional information related to specific deployments can also be encoded using this standard. Thus, both sensor configuration and measurement operations can be addressed with the SensorML standard.  
It is highly flexible and modular as it can describe almost every sensor related property or sensor-related process. However this flexibility and modularity can prove a double-edged sword, as the same information can be encoded in different ways, increasing the difficulty to generate smart processes capable of interpreting SensorML definitions. For this reason there is ongoing work to develop marine SWE profiles of SensorML that define more precisely how this standard should be applied in ocean observing applications [30].  
By combining the OGC PUCK protocol and a SensorML description file, it is possible to automatically detect a sensor and retrieve its description encoded in a single standardized file without any a-priori knowledge about the sensor. If an interpreter software can interpret this file, it can identify the sensor, configure it and retrieve its data, meeting the requirements 2, 3 and 4.
# Overexpression of _RrGGP2_ and _RrDHAR_ Increases Ascorbic Acid Content in Tomato
###### Abstract  
Ascorbic acid (AsA) is the most abundant antioxidant in plants and is an important nutritional index for agricultural products. Some plants, such as _Rosa raxburghii_ Tratt., contain exceptionally high levels of AsA, but are relatively unpalatable. In view of its role in human health, as well as plant growth and development, we examined the effects of two important AsA regulatory genes from _R. raxburghii_ in tomato, with the aim of producing a crop of higher nutritional quality. _RrGGP2_ and _RrDHAR_ were cloned from _R. raxburghii_ fruit. The overexpression vectors were made using 355 promoters and mediated by _Agrobacteriumum tunefacians_ to obtain the overexpression lines. A PCR and qRT-PCR verified that the two genes had been inserted and overexpressed in the tomato leaves and fruits. The results showed that the overexpression of _RrGGP2_ increased tomato leaf and fruit AsA content by 108.5% and 294.3%, respectively, while the overexpression of _RrDHAR_ increased tomato leaf and fruit AsA content by 183.9% and 179.9%. The overexpression of _RrGGP2_ and _RrDHAR_ further changed the expression of genes related to AsA metabolism, and the upregulation of one such gene, _SIGGP_, may have contributed greatly to the increase in AsA. Results here indicate that _RrGGP2_ contributes more towards fruit AsA accumulation in tomato than _RrDHAR_.  
Rosa raxburghii Tratt; tomato; ascorbic acid; _RrGGP2_; _RrDHAR_
## 4 Discussion
### Potential of Spaceborne SIF for Drought Monitoring over Water-Limited Ecosystems
As opposed to the fluctuated temporal series and spatial speckling of GOME-2 SIF signal (Figures 4-6 and 9), SIF retrieved from remarkable radiometric performance of TROPOMI exhibits more spatial and temporal consistency, representing a step change in SIF remote sensing capabilities [26; 56]. It is worth noting that an abundance of negative values of TROPOMI SIF was observed over central Australia in 2018-2019, probably owing to the retrieval noise. [PERSON] et al. [26] suggested that negative SIF values mainly occur for single TROPOMI measurements, which should not be over-interpreted. A comprehensive assessment of the capability of the novel dataset over dryland ecosystem needs to be conducted in the future.
## 3 Results and Discussion
### Comprehensive Performance under Stationary Condition
The minimum value is near _Re_ = 25,000. It is mainly because the heat exchange level development is relatively small, and the channel friction increases significantly at low _Re_. Then, the heat transfer increases obviously with the _Re_ rising, resulting in the increase of _TP_. When _Re_ is low (_Re_ = 10,000-30,000), the thinner turning vane can improve the comprehensive thermal performance of the channel. The thicker  
Figure 3:1 The variation of _fff\({}_{0}\)_ with _Re_ in each case. Figure 3:2 The variation of _Nu_up/_Nu_\({}_{0}\)_ with _Re_ for different channels. greater \(TP\) of the channel. When \(Re\) = 10,000, case2 increases by 4.5%. A too-thick turning vane brings large friction loss. When \(Re\) = 20,000-30,000, the \(TP\) of case6 is not as good as that of case1. With the further increase of \(Re\) (\(Re\) = 30,000-50,000), the heat exchange ability caused by the thicker turning vane is significant enhanced, and the \(TP\) of case6 gradually exceeds that of other channels. It is foreseeable that case6 will be the best channel for comprehensive thermal performance when \(Re\) is larger.
## 6 Other Applications and Future Perspectives  
The role of PENS in chronic and refractory pain management has been also investigated in other medical research fields, including oncology, gynecology and neurology.  
Indeed, in 1998, [PERSON] et al. [8] assessed the effects of PENS in the management of opioid-resistant cancer pain in three cancer patients with bone metastases. More in detail, the authors performed 30 min of electrical stimulation through acupuncture-like needle probes, at 4-100 Hz of frequency, in three or more sessions. Their findings were noteworthy, considering that significant pain relief lasted 24-72 h and was achieved in two of three patients, supporting the hypothesis that PENS might be used as complementary therapy to manage opioid-resistant cancer pain in terminal cancer patients [8]. In 2018, [PERSON] et al. [62] investigated the effectiveness of PENS treatment in reducing fatigue caused by chemotherapy in 83 cervical cancer survivors. The authors found that PENS did not significantly reduce fatigue, suggesting that its role should be limited only to pain management [62].  
Another application of this innovative technique could be for headache, as reported by [PERSON] et al. [63]. The authors assessed 30 patients with either tension headache, migraine or posttraumatic headache symptoms, lasting for at least 6 months. The sample was randomly divided into two groups: the first performed PENS (needles with electricity) while the second performed a sham treatment (\"needles alone\"). The protocol consisted of 30 min, 3 times per week for 2 consecutive weeks. Moreover, according to the crossover study design, after 1 week off, the patients underwent the other intervention. The authors showed that PENS was significantly effective in decreasing the overall VAS pain scores for tension-type headache, migraine and posttraumatic headache, suggesting the use of this technique as a complementary therapy to analgesic and antimigraine drugs for the short-term management of headache symptoms.  
Taking into account all the possible clinical implications of a wide use of PENS technique among clinicians, we might assume that this treatment strategy could rapidly spread in the medical field as an effective and safe treatment acting on pain reduction.
## 2 Materials and Methods
### System Scheme  
The simplified system scheme (Figure 3) includes the TV headend, GPON access network, and households section representing STBs connected to the ONT.  
Figure 3: The simplified OTT television scheme with the stream clustering approach to QoE estimation.  
Figure 2: Stalling occurrence on the operator’s STB.  
A single household has at least one television set-top-box and optical network terminal used to connect to Internet and TV services. Through the backbone and GPON network that includes aggregation routers, the video content is delivered from the TV headend to the customer site. The main task of the headend is to receive multicasts that bear linear channels from content providers, transcode them, and generate HTTP-based adaptive streams. Activities that occur on the STBs are transmitted to the central log system. Logs can contain entries related to channel playback, bitrate level, stalling errors, additional feature utilization, or even network parameters. Due to the amount of data, it has to be filtered. Feature engineering is applied to obtain data related to the played stream, bitrate level, stallings, or playback time of a specific bitrate. During the phase, the input features for the continuous clustering process are calculated. Based on clustering output, QoE assessment and degradation localization are performed. The GPON aggregation routers are located across Poland. After the QoE assessment, the external IP addresses used by STBs to receive the content from CDN servers to discover the aggregation router assignment, as the IP addresses are not stored in the television log system.  
The current data-driven QoE level to the particular STB is computed during the stream clustering. After, the IP mapping of the region with the lowest level of experience can be discovered.
## 2 Materials and Methods
### Pre-Processing, Genome Assembly, and Annotation  
FastQC v0.11.9 [23] was employed to evaluate the quality of the raw reads, followed by Fastp [24] for the removal of adaptor sequences and filtering out low-quality, short, or N-rich reads. De novo assembly of the sequence reads was conducted using SKESA v2.4.0 [25] with default parameters, resulting in a draft genome sequence comprising 36 scaffolds, which was subsequently submitted to NCBI databases. Genome annotation was performed using the Department of Energy Systems Biology Knowledgebase (KBase) server [26] and the Rapid Annotations using Subsystems Technology (RASTtk), both implemented in the PATRIC server [27], utilizing the default settings.