esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results  
### Pose Estimation and Fiducial Marker Detection  
In order to verify the effectiveness of the optimized pose estimation, we pre-defined a motion sequence of the ArUco marker in indoor conditions. In particular, the ArUco marker was rotated at different angles in front of the Crazyllie 2.0 whilst the MAV was programmed to always have its \(y\)-axis pointing collinear with the \(z\)-axis of the reference frame of the marker, radiating out from its center. As it is shown in Figure 5a, at time t \(\approx\) 5.5 s, the proposed method achieved a good estimation of the movement of the marker even though the loss of measurement data. Additionally, at the time t \(\approx\) 12.5 s, the detection of the marker is lost for approximately one second and the proposed method still predicted the movement of the marker.  
Figure 4: Experimental results when following the predefined trajectory with an additional load of 4.7 g according to the AMIGO method.  
Similarly, in Figure 5b the Kalman filter was used in an experiment for tracking the center point of the ArUco marker. In this experiment, the camera view was obstructed at different points in time and moved to a new location. The filter was limited to output only an estimate for a maximum of 25 frames in a row when the marker was not detected and data were sampled only when the marker was detected or estimated by the Kalman filter; hence, the peaks in a green line. Similar to the plot in Figure 5a, the factor \(\alpha=0.85\) was set for diminishing the velocity \(\hat{v}_{k}=\alpha\hat{v}_{k-1}\) for each iteration where an estimation was made in the previous time step for a maximum \(n_{max}\) number of frames.  
Figure 5: Experimental results: (**a**) estimation along the yaw; (**b**) estimation of marker center along the x-direction.  
In order to verify the effectiveness of the ArUco marker detection, we examined at which distance, x, the marker can be recognized when placed directly in front of the camera. The experiment was conducted by placing the marker at various distances, \(x\in[10,220]\) centimeters away from the camera. In order to keep the results of the experiments consistent, the marker used is characterized by a 200 x 200 mm square with a 6 \(\times\) 6 internal matrix and has the identifier ID = 24. As can be observed in Figure 6, the pose estimation algorithm is able to estimate the distance between the camera and the marker with only about 2 cm of deviation from the actual value (in this case, the distance from the MAV is about 1 m). The error in the estimated distance increases with the distance with a maximum deviation of about 14 cm (in this case, the distance from the MAV is around 2 m). From the experimental results, we could confirm a detection success ratio for the ArUco marker of 92.8% (39 out of 42).
# Health State Prediction Method Based on Multi-Featured Parameter Information Fusion
## 2 Methods
### K-Means Clustering  
The K-means clustering method [13] is a method centered on the concept of the center of mass (or centroid). The steps of its algorithm are as follows:  
1. Initialize the center of mass: divide the dataset into algorithmic k classes and select k data points as initial centroids.
2. Calculate the Euclidean distance from each data point to the center of mass and assign the data points to the class nearest to the center of mass. \[J_{k}=\sum_{j=1}^{k}\sum_{k=1}^{n_{j}}\parallel x_{k}-m_{j}\parallel^{2}\] (14)where \(m_{j}\) denotes the location of the center of gravity of the jth class. To optimize the clustering results, the \(J_{k}\) criterion should be minimized.
3. Update the center of mass: recalculate the centers of mass of the individual classes already obtained. Calculate the Euclidean distance from each data point to the center of mass and assign the data points to the class nearest to the center of mass.
4. Repeat steps 2-3 until the algorithm converges and the centers of mass to which all samples belong no longer change.
# Packet Injection Exploiting Attack and Mitigation in Software-Defined Networks
## 4 Countermeasures  
### System Architecture  
The PIEDefender stands between the controller platform and other controller apps, as depicted in Figure 7. It includes three function modules: Injection detection, simplified DoS detection, and flow rule management. The injection detection module builds and maintains the mapping relationship between the switches and the hosts, and it identifies malicious hosts with injection behaviors by verifying the consistency of information. The simplified DoS detection module utilizes OpenFlow message and flows features to  
Figure 5: Attack impact on controller CPU usage. (**a**) Normal. (**b**) Spoofing Destination MAC. (c) Injection.  
Figure 6: Attack impact on OpenFlow switch. (**a**) Normal. (**b**) Spoofing Destination MAC. (c) Injection.  
detect hosts that use injected hosts to carry out a DoS attack. Once a malicious node is detected, the injection detection module and simplified DoS detection module will give the threat information to the FlowRule Management module, which installs blocking rules against the attack source, effectively protecting the SDN network.  
For better understanding, we use Table 1 to show the meanings of major notations. Next, we will introduce the functions of each module in detail.
# Generating Chinese Event Extraction Method Based on ChatGPT and Prompt Learning
## 1 Introduction  
With the increasing popularity of the Internet, the amount of information on the web is growing exponentially. Extracting valuable information from massive data has become an essential and challenging task. Information extraction (IE) has emerged to address this issue, aiming to extract unstructured information into structured data formats. It includes various techniques such as Named Entity Recognition (NER), Relation Extraction, and Event Extraction (EE) [1]. As an essential branch of information extraction, event extraction focuses on extracting structured event information from unstructured text. The Automatic Content Extraction (ACE) conference [2] divides event extraction into two sub-tasks: event detection and argument extraction. Event detection refers to detecting trigger words that represent the occurrence and type of events, while argument extraction involves extracting event arguments, such as around the relevant characters, time, place and other entities of the trigger words. In the English domain, rich datasets are available for event extraction, including a series of English text samples covering various events in different fields and types, such as ACE 2005 [3] and CASIE [4]. Furthermore, the grammatical structures and regularity of English are much clearer and more standardized, usually following a concise subject-verb-object structure.  
However, in the Chinese domain, event extraction tasks still need to be solved due to the scarcity of event data and the complexity of the text. Traditional supervised learning methods rely on a large amount of manually annotated data to train models. However, existing annotated corpora often need to be more prominent in scale, domain-specific, and expensive to annotate, leading to a poor generalization of trained models. Additionally,as event extraction involves multiple sub-tasks, different sub-tasks require the design of different prompt learning templates and information-sharing relationships between templates, which suffers from inefficient manual design and propagation of parameter errors, posing difficulties for event extraction in the Chinese domain.  
To address these challenges, this paper defines the event extraction task as a generative one. It proposes improvements to the BART model [5] from data augmentation and informative learning templates. On the one hand, the content generation capability of ChatGPT (AI-Generated Content, AIGC) is applied to the corpus generation in the field of event extraction, addressing the issues of data scarcity and poor generalization in existing tasks. On the other hand, the rich semantic knowledge in the model is mined through the construction of generative input templates, in order to reduce the error propagation and to solve the inefficiency problem of manually designing templates.
## 3 EMF-Aware Routing Framework for WMNs
### EMF-Aware Routing Algorithm  
This section presents a routing algorithm that is used to better describe the behavior of the proposed routing protocol. We begin by posing an optimization problem, which considers both the instantaneous transmission power and the accumulated exposure. Afterwards, we propose an algorithm that can be used to solve the stated problem, exploiting graph theory and transformation techniques that we introduce for taking into account the two aforementioned parameters or routing metrics.
# Composition Diversity and Expression Specificity of the _Tps_ Gene Family among 24 _Ficus_ Species
###### Abstract  
Volatile organic compounds (VOCs) released by the receptive syconia of _Ficus_ species play a vital role in attracting highly species-specific pollinating fig waps. The components of VOCs vary considerably among _Ficus_ species, but are generally dominated by a few common terpenoid compounds or specific proportions of several compounds. Terpene synthase (_TPS_) is the main source of specific and diverse terpenoids, but the evolution of the _TPS_ gene family in _Ficus_ and the potential functions of the _TPS_ genes in species-specific pollination remain largely unelucidated. In this study, using transcriptomes of ostiole bracts of receptive male figs from 24 _Ficus_ species collected from South China and Southeast Asia, we comprehensively scanned and investigated the composition and evolutionary characteristics of all _TPS_ genes in all 24 species. We identified 248 _TPS_ genes, including 33 orthologous genes and six singletons. Sequence and phylogenetic analysis showed that a majority of the 248 _TPS_s contained the DDXXD and DTE motifs, rather than the DXDD motif, and involved all subfamilies (_TPS_-_4,b,c,eff_ and _g_) known in other angiosperm genomes, suggesting a very diverse and complex composition of class I _TPS_s during the receptive phase. In addition, compared to _TPS_-_4_, which is generally the largest subfamily in some plants, the _TPS_-_b_ subfamily contained the highest number of genes in _Ficus_ species. Expression profile comparison showed that the distribution and expression levels of different _TPS_s among different _Ficus_ species differed considerably, but a few _TPS_ genes were common across most species. Positive selection analysis showed that the _Ficus_ TPS_ genes were mainly under purifying selection, with only four genes having positive selection signals and two genes having positive selection sites, and two genes having relatively fast-evolving rates. The present study demonstrates the basic evolutionary characteristics of _TPS_ genes in _Ficus_ and reveals the roles of _TPS_s in shaping the diversity and specificity of the fig-fig wasp symbiotic relationship.  
fig; _TPS_; DDX motifs; expression profile; positive selection
## 2 Materials and Methods
### Mitochondrial Viability Assessment in D. melanogaster Head Homogenate  
As previously described for similar experiments [17], adult (5-6 days old) female and male _D. melanogaster_ (Oregon-R strain from Bloomington Drosophila Stock Center, Indiana University, Bloomington, IN, USA) were reared for 10 days in vials containing either a standard diet (STD) or high-sucrose diet (HSD, 35% \(w/v\)). The HSD was prepared by independently varying the sucrose concentration while keeping all other components constant. Where indicated, flies were raised on diets supplemented with SFH or stem extracts at a concentration ranging from 0.03 to 0.1 mg/mL, ensuring that the DMSO concentration remained below 0.1% to avoid poisoning (toxic) effects. Compounds were mixed into the cooled food in the vials to achieve the final working concentration protected from light.  
Mitochondrial viability in _D. melanogaster_ head homogenate was assessed, as previously described [17], with minor modifications. In brief, 100 heads were manually homogenized in cold Phosphate Buffer (PB). The supernatants were collected after each consecutive centrifugation at 4 \({}^{\circ}\)C for 5 min at 1000 rpm. Mitochondrial activity was subsequently evaluated using the MTT reduction assay, with a final concentration of 0.5 mg/mL, by measuring absorbance at 595 nm.
# Reconstruction of Ancient Lake after Peat Excavation--A Case Study about Water Quality
## 5 Conclusions  
1. Pit lake Nienawiszcz is a unique water reservoir, which was successfully recreated in the place of a former lake and its shape is exactly as in ancient time with slopes safe for recreation.
2. Despite water shortage in the region, the water table of the studied lake was stable, indicating that such a water body can provide water to the local areas.
3. After the connection of both parts (A and B), the water quality started to stabilize immediately. The studied objects in 2014 showed more similar physico-chemical properties than in the other years, which can be explained by the process of unification of both parts into one functioning ecosystem. Future years, especially very dry ones, will provide data on how long this reservoir can be stable in terms of water quality parameters and free of the extensive growth of aquatic plants. This question is important, especially due to presence of tolerant plant species like reed, which can propagate very rapidly under favorable conditions.
4. Water quality parameters were appropriate for cyprinids fish species and the reservoir has been used for angling recreation. The information on the reservoir may be of use to anyone planning the reconstruction of ancient lakes or making reservoirs in the pits left after peat excavation.
5. In lakes in which peat excavation will be simultaneous with the reconstruction of an ancient lake, all processes of eutrophication, overgrowth and peat formation can start once again and such lakes can play a positive role in the sustainable management of water resources and in recreation.  
Conceptualization, R.S., P.F. and J.Z.; Data curation, J.Z.; Formal analysis, P.F. and D.K.; Funding acquisition, P.F.; Investigation, R.S., P.F. and D.K.; Methodology, R.S., P.F., D.K. and J.Z.; Project administration, J.Z.; Resources, P.F.; Software, D.K. and K.A.; Supervision, R.S.; Validation, R.S. and D.K.; Visualization, K.A.; Writing--original draft, R.S., P.F., J.Z. and K.A.; Writing--review & editing, R.S. and D.K. All authors have read and agreed to the published version of the manuscript.  
Studies were financed by private investor [PERSON], Ph.D.; The publication was co-financed within the framework of Ministry of Science and Higher Education programme as \"Regional Initiative Excellence\" in years 2019-2022, Project No. 005/RID/2018/19.  
Not applicable.  
The authors declare no conflict of interest.
# Missing Data Imputation in the Internet of Things Sensor Networks
## 4 Experiments
### Methodology  
We created different rates of artificial missing data on the variable containing the concentration of C\({}_{6}\)H\({}_{6}\) (see Figure 1) and employed single and multiple imputation techniques to replace the uni-variate missing data.  
The techniques we considered in this research include EMI, KNN, MissForest, Regression and BEMVI. In order to assess the performance of the algorithms, we follow the approach that was proposed in [35], outlined as follows:  
1. We evaluated the correlation between each feature as seen in Figure 2; features that exhibited strong correlation with the variable (C\({}_{6}\)H\({}_{6}\)) that required imputation were used in the imputation model.
2. We created missingness at arbitrary points on the target variable.
3. We imputed missing values using the different imputation techniques.
4. The accuracy off all the imputation techniques were assessed and compared using their Root Mean Squared Error (RMSE) indicator.
5. We analysed the effect of each imputation technique on the sensor calibration and compared the results to the calibration result of each imputation method.  
Specifically, we introduced missing values based on the patterns listed below and evaluated the accuracy of the imputation methods based on the different scenarios:  
1. We simulated missingness on a single variable at random, where missing ratio \(r=\) (10%, 20%, 30% and 40%).
2. We simulated missing values on one variable, occurring over \(p\) consecutive time periods where \(p=\) (1 day, 1 week and 1 month).  
A detailed description of the imputation algorithms used in our experiments can be seen in the following sections below.  
Figure 1: Pattern of missing value occurrence.  
Figure 2: Correlation coefficients of air quality sensors.
# Mental Workload Classification Method Based on EEG Independent Component Features
## 2 Materials and Methods
### Mental Workload Classification Method Based on EEG ICs Features
#### 2.2.2 Feature Extraction Methods  
The power spectral density (PSD) at different frequencies is often used to extract the features of EEG signals in the frequency domain. In this paper, the energy features of EEG ICs were specially extracted for further mental load classification.  
Fourier transform in Equation (3) was performed on each data segment to obtain \(F\)s(n).  
\[F_{s}(\text{n})=\sum_{n=0}^{N-1}Se^{\frac{-2\pi j}{N}n} \tag{3}\]  
where \(S\) is a set of EEG ICs; \(F\)s(n) is Fourier transform of \(S\). \(N\) is the number of signal samples. \(n\) is from 0 to \(N\)-1.  
The corresponding PSD of EEG ICs was calculated with Equation (4).  
\[p_{s}(\text{n})=\frac{F_{s}(\text{n})F_{s}{}^{*}(\text{n})}{N} \tag{4}\]  
where \(p_{s}\)(n) is PSD of EEG ICs; \(F\)s*(n) is the conjugate of \(F\)s(n). \(N\) is the length of signal.  
Many studies have shown that human mental state is related to four rhythms [32; 33; 34]. According to the EEG signal frequency band distribution, it can be divided into four frequency bands: \(\delta\) (1-4 Hz), \(\theta\) (4-8 Hz), \(\alpha\) (8-14 Hz) and \(\beta\) (14-30 Hz). The energy features of each frequency band are defined as:\[\begin{split} E_{s,\beta}&=\sum\limits_{freq=1}^{4}p_{s,freq }\\ E_{s,\theta}&=\sum\limits_{freq=4}^{8}p_{s,freq }\\ E_{s,\alpha}&=\sum\limits_{freq=8}^{14}p_{s,freq }\\ E_{s,\beta}&=\sum\limits_{freq=14}^{30}p_{s,freq }\\ \end{split} \tag{5}\]  
where \(p_{s,freq}\) refers to the power spectral density at a certain frequency; \(E_{s,\delta}\), \(E_{s,\theta}\), \(E_{s,\alpha}\) and \(E_{s,\beta}\) are the four energy features of EEG ICs.  
The absolute power spectrum is normalized to obtain the relative power spectrum.  
\[E_{s,\beta}{}^{\star}=\frac{E_{s,\beta}}{E_{all}},E_{s,\theta}{}^{\star}=\frac {E_{s,\theta}}{E_{all}},E_{s,\alpha}{}^{\star}=\frac{E_{s,\alpha}}{E_{all}},E_ {s,\beta}{}^{\star}=\frac{E_{s,\beta}}{E_{all}},E_{all}=\sum\limits_{freq=1}^ {30}p_{s,freq} \tag{6}\]  
where, \(E_{all}\) refers to the power spectral density at 1-30 Hz; \(E_{s,\beta}{}^{\star}\), \(E_{s,\theta}{}^{\star}\), \(E_{s,\alpha}{}^{\star}\) and \(E_{s,\beta}{}^{\star}\) are the four relative energy features of EEG ICs.
## 4 Ad Hoc Routing: Configuration, Scenarios, and Results
### Scenarios and Results
#### 4.2.1 Scenario 1: Client-Server Direct Communication  
Firstly, the user only gets connected to the Control Room without the need for any intermediate entity, as both are within the range of each other. This can be verified through the following command on the Linux terminal:  
\"$sudo batctl originators\"  
This shows the list of all available routes, highlighted in purple in Figure 6. Since the BATMAN-ADV protocol works with Medium Access Control (MAC) addresses rather than IP addresses, all the connections are shown in terms of their MAC IDs. One can see that there is a direct path to the server (with a MAC address of \(78:32:1b:ad:e4:11\) shown in red. The path quality is 158 (out of 255), indicating a rather strong connection, and therefore no intermediate hop is required for the packet to reach its destination.  
As can be seen in Figure 7, when the client (192.168.2.103) checks its path to the server (192.168.2.102), it receives a response in around 21 milliseconds, highlighted in red. Although there is an alternate path in the network, where there is a node with an IPv4 address of (192.168.2.101), as shown in purple box. However, it has a longer route taking at least 150 milliseconds (high link cost) to traverse. Therefore, the client would pick the shorter route (low link cost) to send its message.
## 2 Theory
### Evaluation Indexes  
In order to compare and analyze the consistency between the results of this method and empirical judgments, the Accuracy, Sensitivity and Specificity are used as evaluation indexes. Their calculation formula is as follows:  
\[Accuracy=\frac{TP+TN}{TP+FP+FN+TN}\times 100\% \tag{11}\]  
\[Sensitivity=\frac{TP}{TP+FN}\times 100\% \tag{12}\]  
\[Specificity=\frac{TN}{FP+TN}\times 100\% \tag{13}\]where \(TP\) is the number of this method's judgments and empirical judgments that are both the HIFU echo signals; \(TN\) is the number of this method's judgments and empirical judgments that are both the noise signals; \(FP\) is the number of this method's judgments that are the HIFU echo signals but whose empirical judgments are the noise signals; \(FN\) is the number of this method's judgments that are the noise signals but whose empirical judgments are the HIFU echo signals.
# Anti-Recompression Video Watermarking Algorithm Based on H.264/AVC
## 2 H.264 Compression Standard and Watermark Embedding Location Selection  
### H.264/AVC Video Compression Standard  
H.264 is a video coding standard issued by the joint video group composed of the International Organization for Standardization and ITU. It is one of the most widely used video coding methods at present. Figure 1 shows the principle block diagram of H.264 video coding [22]. H.264 includes I frame, P frame and B frame, wherein I frame is a full-frame compression coding frame, which is generated without reference to other pictures, and P frame is a prediction frame generated by reference to the previous I frame and P frame prediction coding. The main process of H.264 coding is to first perform DCT transform and quantization of the residual between the current frame and the predicted frame, and then entropy encode the obtained quantization coefficient sequence and write it into the code stream. According to the characteristics of the video frame, the encoder selects intra prediction or inter prediction to process the video frame, I frame uses intra prediction coding mode to encode, and P frame uses inter prediction to encode the image.  
The H.264 video decoding process is shown in Figure 2. The decoder obtains data information from the code stream, obtains transform coefficients through entropy decoding and reordering, and obtains residual blocks through inverse quantization and inverse DCT transformation. At the same time, the decoder uses the header information obtained from the code stream to make the intra prediction and generate prediction blocks. The decoder adds the prediction block and the residual block to obtain the frame image, which is filtered and reconstructed to obtain the decoded image.