esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Smart Grid Anomaly Detection Approaches
## 3 Results and Analysis
### Network Infrastructure Anomalies
The combination of methods in this case shows better performance than the individual models of regression, SVM, random forest, MLP, and decision tree. Global positioning system (GPS) is an indispensable device in SGs, but it can be compromised if an attacker is able to manipulate the timestamps by spoofing a GPS signal or manipulating the precision time protocol (PTP). This is a time synchronization attack \begin{table}
\begin{tabular}{c c c} \hline _Scenario_ & _Method/Technique_ & _Objective_ \\ \hline \multirow{5}{*}{Network infrastructure anomalies} & Construction of temporal events, & \\  & Machine learning, GTP & Avoid device compromise \\ \cline{2-3}  & Machine learning, AENN, RF & Detect time synchronization attack \\ \cline{2-3}  & & \\ \cline{2-3}  & GN & Detect meter implant and network topology attacks \\ \cline{2-3}  & SPN & Detect network topology attacks \\ \cline{2-3}  & Pattern-based correlation & Identify anomalies in the \\  & capabilities, GAN & communication protocols \\ \cline{2-3}  & Spatiotemporal correlation, LWS, & Detection of anomalies that are not \\ \cline{2-3}  & ReTAD & cyber-attacks \\ \hline \end{tabular}
\end{table} 
Table 8: Anomaly detection in the SGI. (TSA) for which correlation analysis from historical data, unsupervised machine learning based on auto-encoder neural networks (AENN), and random forest (RF) as a supervised machine learning detector are useful [47]. The key to such solutions is the right combination of techniques in the context of attacks. Topology attacks can be detected by using the stochastic petri net (SPN); this is a transition model that can describe system behaviors in the presence of topology attacks [33]. Not all anomalies in the SGI come from a cyber-attack; they can simply be disturbances whose origin is in the operating conditions. The spatiotemporal correlation to capture the characteristics of the anomaly inspired by the Ledoit-Wolf Shrinkage (LWS) method and real-time anomaly detection (ReTAD) algorithms allows the problem of the big volume of measurement data in anomaly detection to be overcome [20].
# Urban PM2.5 Concentration Prediction via Attention-Based CNN-LSTM
## 3 Data and Method  
### Data Description  
The spatiotemporal variation of atmospheric particulate matter is affected by various factors such as pollution emission sources and meteorological conditions [14; 15]. The PM\({}_{2.5}\) concentration is not only related to the atmospheric state and PM\({}_{2.5}\) concentration at the previous time, but also the PM\({}_{2.5}\) concentration in the adjacent areas [16; 17]. The air quality data used for the AC-LSTM model input consists of readings of pollutant concentrations from air quality monitoring stations and meteorological data.  
Figure 1: Framework of the proposed approach.  
Figure 2: Illustration of the multiscale predictors.  
In this paper, the air quality data from nine air quality monitoring stations in Taiyuan City, China, were obtained from the National Environmental Protection Bureau and the Shanxi Provincial Environmental Protection Department. The location map of Taiyuan is shown in Figure 3a, and the yellow coordinate represents Taiyuan. The experimental data were collected from 1 January 2014 to 25 December2016 at an hourly rate, and the spatial locations of the air quality monitoring stations are illustrated in Figure 3b. The experimental data contain the concentrations of PM\({}_{10}\), PM\({}_{2.5}\), SO\({}_{2}\), NO\({}_{2}\), O\({}_{3}\), and CO. The detailed characteristics of Taiyuan air quality monitoring stations are shown in Table 1. The meteorological data include indicators such as air pressure, temperature, wind speed, humidity, and visibility.
## 4 Application in Channel Experiment  
### Experimental Setup  
Images of bed-load transport were obtained from experiments in open-channel flow. Figure 12 shows the sketch of the experimental setup. The channel is 11 m long, 0.25 m wide, and 0.25 m high. The channel bed slope can be adjusted from 0% to 1.5%. Comb and tailgate are installed in the inlet and outlet of the channel, respectively, to stabilize the flow and water level. Six ultrasonic gauges are used to monitor the water level (with accuracy of 0.1%). Water discharge is measured by the electromagnetic flowmeter (with accuracy of 0.1%). Uniform natural quartz sands were used as bed-load with median diameter of \(2.25\times 10^{-3}\) m and density of \(\rho_{\text{s}}\) = 2650 kg\(\cdot\)m\({}^{-3}\). The channel bed was paved with a layer of sand (\(2.25\times 10^{-2}\) m) before experimenting.  
A high-speed camera, IDT Y7-S3, with a 50 mm/1.4f lens was mounted above the flow to capture images of sediment motion. The camera has a maximum resolution of 1920 \(\times\) 1080 pixels and maximum framing frequency of 12, 300 fps. A LED light was used to illuminate the measurement region. A plexiglass sheet was placed on the water surface to eliminate oscillations, similar to procedures used in the literature [7; 11; 34; 35]. Before conducting the experiments, measurements were made to examine the vertical velocity profiles with and without the plexiglass sheet. The results showed that in our experimental condition, the sheet exerts a negligible influence on the velocity profile near the bed. As the bed-load move in the near-bed region, it was regarded that the influence of the sheet on bed-load transport is negligible.  
The flume slope \(J\) was set to be 2.5%, water discharge \(Q\) was 21.74 m\({}^{3}\)/h, and the water depth \(H\) is \(5.33\times 10^{-2}\) m. Hence, the average flow velocity \(U_{f}=Q/(HB)\) was equal to 0.45 m/s, where \(B\) is channel width. These hydraulic conditions can ensure that there were particles entrained from the bed and the flow is subcritical. The friction velocity \(u_{*}=(gH)^{0.5}\) was 0.036 m/s where \(g\) is gravity acceleration equal to 9.81 m/s\({}^{2}\). Shields number \(\Theta=u_{*}\
icefrac{{2}}{{}}(RgD)\) was 0.036 which is equal to critical Shields number \(\Theta_{c}=0.036\)[13], and where \(R\) is sediment relative density equal to 1.65. The Froude number \(Fr=U_{f}/(gH)\) was 0.86. Considering the computational efficiency and water conditions, we set the frequency, image size and total frames as 250 fps, \(500\times 864\) pixel\({}^{2}\) and 60,000 frames, respectively. The choices of these parameters are based on previous experience [32; 36]. The resolution of the image was 94.3 pixel/cm, allowing one sediment grain to occupy about \(21\times 21\) pixel\({}^{2}\).
# Heterologous Production of Acrylic Acid: Current Challenges and Perspectives
## 4 Key Points to Optimize AA Heterologous Production  
Although studies presented in Section 3 represent a step forward in AA production, the concentrations are still low and more optimizations are needed at genetic and operational levels. The work developed regarding 3-HP biosynthesis allowed the rapid design of strains able to produce AA. However, several optimizations performed for 3-HP biosynthesis that allowed successful increase in the concentrations still need to be implemented in AA biosynthesis strain design. For example, to increase AA production from malonyl-CoA, it is mandatory to increase its availability, as proven in Section 2.2. Although this was extensively explored in 3-HP production, it was barely implemented in the experiments regarding AA biosynthesis [31; 33]. Additionally, AA production using _E. coli_ and the glycerol route with 3-HP as an intermediary would probably easily increase by directly supplementing glycerol and by overexpression the genes from the 3-HP pathway using plasmids instead of integrating these genes in the chassis genome. Moreover, regarding AA production from \(\beta\)-alanine, the overexpression of more genes related to \(\beta\)-alanine accumulation would also help to increase the concentration (e.g., aspartate aminotransferase [40; 104], phosphoenolpyruvate carboxylase [46]). Lastly, the elimination of competing pathways (e.g., deletion of _yqhD_) in the recent study by [PERSON] et al. [105] would also probably allow improving AA production.  
In addition to the referred possible ways to improve the concentrations, other strategies are needed, for example, to solve the problem of B\({}_{12}\) supplementation necessity in the glycerol route, to find or create more efficient enzymes and to increase the host tolerance to AA, among others. These strategies are highlighted in this section.
# SLA-DQTS: SLA Constrained Adaptive Online Task Scheduling Based on DDQN in Cloud Computing
## 4 Algorithm Design
### Input  
We represent the environment state and action of the system as a 15-dimensional vector. To realize a fixed input dimension, we represent the VM set state by the mean and standard deviation calculations of some features, and represent the mean and standard deviation of set \(X\) by \(M(X)\) and \(S(X)\), respectively. In the first four dimensions of the vector, we use the mean and standard deviation of VM busy time and cost to represent the load of the current cluster. In the last four dimensions, we use the characteristics and expected results of tasks in a VM as the action space, and the remaining part uses average and standard deviations of the expected result of the task performed on the VM that satisfies Equation (4), so as to represent the overall environment information and distributable VM information. We define the VM set of executable \(t_{i}\) requirements as  
\[t_{i}\_AVM=\begin{cases}vm_{j}|vm_{j}\in CLU\_VM,\\ t_{i}^{k}\leq vm_{j}^{k}\,\forall k\in[mips,bw]\end{cases}. \tag{9}\]  
Figure 3: Q value mode (**a**) \(S-Q(s,a_{n})\) mode, (**b**) \(S,a-Q(a)\) mode.  
For task \(t_{i}\), the state action set of input is \(S_{i}=\{s_{ik}|vm_{k}\in t_{i\_}AVM\}\), and the \(s_{ik}\) input is a \(15\)-dimensional vector,  
\[s_{ik}=\left\{\begin{aligned} &|CLU\sim T|,M\Big{(}\Big{\{}vm_{j}^{ bustime}\Big{\}}\Big{)},S\Big{(}\Big{\{}vm_{j}^{ bustime}\Big{\}}\Big{)},\\ & M\Big{(}\Big{\{}vm_{j}^{bustime}*vm_{j}^{price}\Big{\}}\Big{)},vm _{k}^{bustime},ET_{ik},\\ & vm_{k}^{price}*ET_{ik},max(OT_{ik0},0),M(\{ET_{il}\}),\\ & S(\{ET_{il}\}),M\Big{(}\Big{\{}vm_{j}^{price}\Big{\}}\Big{)},S \Big{(}\Big{\{}vm_{j}^{price}\Big{\}}\Big{)},\\ & M(\{max(OT_{il},0)\},S(\{max(OT_{il},0)\}),|t_{i\_}AVM|,\\ &(vm_{j}\in CLU\_VM,vm_{l}\in t_{i\_}AVM)\end{aligned}\right\}, \tag{10}\]  
where \(vm_{j}\) represents all VMs in the cluster, and its related parameters are the overall characteristics of the environment. \(vm_{l}\) is a set of VMs that can be used to execute \(t_{i}\), and its related parameters are the local features of available \(t_{i}\), which are used to measure related features of \(vm_{k}\). The remaining parameters are all mentioned above; see Table 1.
# CA-CGNet: Component-Aware Capsule Graph Neural Network for Non-Rigid Shape Correspondence
## 4 Experiments and Evaluation
### Component Matching  
For each 3D mesh, over-segmentation is used to divide it into many different regions, and then dynamic clustering is used to merge regions to form components. The selection of the number of clusters \(k\) not only affects the amount of computation but also affects the subsequent matching effect. Therefore, we use different cluster numbers with \(k=4,5,6,7,8,9,10\) for experimental tests. Table 2 shows the identification errors of experiments on human and non-human models with a different number of clusters. Through the experiment, it can be found that the number of clusters with \(k=8\) can achieve the minimum identification error on the human model and centaur model, and the clustering amount of \(k=5\) can achieve the minimum identification error on other non-human models. Therefore, in the subsequent experiments, we adopted the dynamic classification strategy with \(k=8\) on the human model and centaur model and \(k=5\) on other non-human models.  
Figure 5 shows the component formation results of the human model on four different datasets, where different colored body parts represent different types of components. In order to further verify the robustness of the proposed method, we conduct dynamic clustering on non-human models, as shown in Figure 6. It shows that our method achieves equally effective clustering results on these non-human models.  
After the components are formed, the component of different models is matched to obtain a pair of components. Figure 7 visualizes the matching results of different models, where the left side shows the matching results of components with a different pose of the same subject, and the right side shows the matching results of components with different subjects. Experiments demonstrate that our method achieves good results in different model component matching, which proves the robustness of our method.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Clustering Number**} & \multirow{2}{*}{**FAUST**} & \multirow{2}{*}{**SCAPE**} & \multirow{2}{*}{**KIDS**} & \multicolumn{6}{c}{**TOSCA**} \\ \cline{5-12}  & & & & **[PERSON]** & & **[PERSON]** & & **[PERSON]** & **[PERSON]** & **Centaur** & **[PERSON]** & **Horse** & **Wolf** \\ \hline
4 & 0.0659 & 0.0872 & 0.0814 & 0.0652 & 0.0796 & 0.0684 & 0.0369 & 0.0427 & 0.0358 & 0.0374 & 0.0335 \\
5 & 0.0586 & 0.0697 & 0.070
## 2 Materials and Methods
### HPLC Analysis  
For the determination of the individual phenolic profile, 1 g of plant material was subjected to an extraction process that included vortexing with 10 mL 50% ethanol and alkaline water for 3 min, followed by ultrasonication at 30\({}^{\circ}\)C for 30 min and centrifugation at 6000 rpm for 30 min. The samples were then refrigerated for 24 h at 4 \(\pm\) 2 \({}^{\circ}\)C, and before HPLC analysis, the vortexing and ultrasonication procedures were repeated. The supernatant obtained was successively filtered through Whatman no. 4 filter paper and a 0.45 \(\upmu\)m syringe filter and was subsequently introduced into vials for analysis. The analysis of phenolic compounds was performed using a high-performance liquid chromatograph (UHPLC UltiMate 3000 XRS, Thermo Scientific, Waltham, MA, USA), equipped with an autosampler, XRS pump, and UV-VIS diode array detector. The separation was performed on a Hypersil Gold column (150 \(\times\) 4.6 mm, 5 \(\upmu\)m particle size) at 25 \({}^{\circ}\)C, using a mobile phase consisting of 1% acetic acid (B) and 100% methanol (C), in gradient mode, with a flow rate of 0.8 mL min\({}^{-1}\) and an injection volume of 5 \(\upmu\)L. The compounds were detected at 278 nm, in the order of retention: gallic acid, neochlorogenic acid, (+)-catechin hydrate, chlorogenic acid, vanillic acid, caffe acid, (-)-epicatechin, ferulic acid, sinapic acid, salicylic acid, ellagic acid, rutin and myricetin, according to the method proposed by [PERSON] et al. [22]. Each compound was identified by comparing its retention time with that of the standards analyzed under the same conditions. Quantification was then performed using the calibration curves generated from these standards. The results were expressed in mg 100 g\({}^{-1}\) and represent the average of three successive determinations.
## 1 Introduction  
Robots are becoming more popular in domestic applications that differ significantly from industrial settings. Cleaning robots and robot assistants have to operate in a priori unknown environments. In contrast to the industrial environment, the objects in the domestic environment have random positions. This application requires detecting and operating objects in various lighting conditions and dealing with occlusion. Moreover, in the example scenario, when a user asks a robot for a mug, the robot should open the door to another room, open the cabinet and find a mug. This task requires the robot to deal with various articulated objects that can be rotational (doors) or translational (drawers).  
Numerous mobile robots utilize a static model of the environment by uploading a pre-existing map. Other robots generate environment maps dynamically during operation by employing sensors and advanced algorithms for localization and mapping. Very often, the provided or obtained model is assumed to be static. However, the configuration of the articulated objects can change, and the robot should exploit the properties of articulated objects during the execution of the mission. Determining how the robot should interact with articulated objects in the environment is a challenging task. Present-day solutions predominantly rely on probabilistic methods and require executing specific sequences of movements to ascertain an object's model [1, 2]. This approach requires time-consuming actions and may damage articulated objects.  
In contrast, in this research, we are focused on detecting and estimating the properties of articulated objects from visual RGB-D images only. This includes the detection of drawers, cabinet fronts, and handles, and estimating the axes of rotation and translation. The goal is to obtain accurate information about articulated objects before the robot starts the interaction with the environment. The objective of this study is to develop a system capable of building an environment model represented by a point cloud, utilizing information about articulated objects, specifically cabinets and drawers. The practical application of this system lies in enhancing the autonomy of mobile robots. The primary motivation behind this research is to eliminate the necessity for robots to interact with objects to comprehend their kinematic structure. This approach aims to mitigate the risks associated with potential damage during interaction and reduce the time required for model building. To accomplish this objective, the system solely relies on a single RGB-D frame to generate the model. No supplementary information regarding the object's location within the environment is required.
# A Security Framework for the Internet of Things in the Future Internet Architecture
## 6 Delegation-Based Key Provisioning Protocol
### Discussions and Security Analysis
#### Protocol Security Analysis
As this authentication request is initiated by \(G\) in Message 3, \(P\) sends its Diffie-Hellman value, which can be pre-generated,as a challenge to \(G\) to reduce the chance of a denial of service (DoS) attack on \(P\) as the Diffie-Hellman algorithm requires a lot of computing resource and \(G\) has to pay the price first. The last stage of our protocol (Message 7-9) corresponds to the second stage of the 3 PKD protocol. In this stage, the key material \(k\) provided by \(P\) is delivered to \(C\), and then \(G\) and \(C\) reach a key agreement based upon \(k\). The protocol is concluded by a handshake between \(G\) and \(C\) so that both of them confirm that the other party has computed the correct shared secret with respect to the correct protocol session. In our embodiment, we introduced the session context in terms of _session_ identifier \(sid\), which is derived from the complete session information (all identifiers, nonces and optional local secret between \(G\) and \(C\)), and use it as a glue to combine SIGMA protocol between the first stage and the second stage of 3 PKD protocol. With the approach of repeating the session information through the protocol, each of the parties has a fresh and consistent view of the current protocol session, which significantly increases the difficulty of launching attacks targeting the context of a protocol instance, such as replay attacks and misbinding attacks. At the end of the third stage, dynamic mutual trust between the Child and the Guardian and the Parent-to-Guardian delegation are achieved. Secrecy and forward secrecy are also satisfied through handling key materials properly. The key provisioning protocol may further realize stronger security on provisioned keys as compared with existing pairing protocols. In contrast to pairing protocols that do not have a security proof (which causes them to be troublesome due to possible man-in-the-middle attacks and frequent human errors in any manual operations), the protocol as described may be performed with no manual intervention from the user for key provisioning. Compared with traditional key provisioning protocols that do not rely on a server or key infrastructure, such as secure pairing, there is no manual operation by a user on an out-of-band (OOB) channel. Instead, the device Parent party is leveraged as the trusted third party to facilitate trust establishment. Such design reduces human error, hence satisfying average administration requirements and increasing usability. Further, it removes the requirement of close proximity for key provisioning, as required by most OOB channels involving manual operations by human, and hence increases the chance of practical deployment and satisfies the scalability requirement.
# Fast Planar Detection System Using a GPU-Based 3D Hough Transform for LiDAR Point Clouds
###### Abstract  
Plane extraction is regarded as a necessary function that supports judgment basis in many applications, including semantic digital map reconstruction and path planning for unmanned ground vehicles. Owing to the heterogeneous density and unstructured spatial distribution of three-dimensional (3D) point clouds collected by light detection and ranging (LiDAR), plane extraction from it is recently a significant challenge. This paper proposed a parallel 3D Hough transform algorithm to realize rapid and precise plane detection from 3D LiDAR point clouds. After transforming all the 3D points from a Cartesian coordinate system to a pre-defined 3D Hough space, the generated Hough space is rasterised into a series of arranged cells to store the resided point counts into individual cells. A 3D connected component labeling algorithm is developed to cluster the cells with high values in Hough space into several clusters. The peaks from these clusters are extracted so that the targeting planar surfaces are obtained in polar coordinates. Because the laser beams emitted by LiDAR sensor holds several fixed angles, the collected 3D point clouds distribute as several horizontal and parallel circles in plane surfaces. This kind of horizontal and parallel circles mislead plane detecting results from horizontal wall surfaces to parallel planes. For detecting accurate plane parameters, this paper adopts a fraction-to-fraction method to gradually transform raw point clouds into a series of sub Hough space buffers. In our proposed planar detection algorithm, a graphic processing unit (GPU) programming technology is applied to speed up the calculation of 3D Hough space updating and peaks searching.  
Keywords:plane extraction; 3D Hough transform; parallel computing; LiDAR point clouds +
Footnote †: journal: Applied Sciences