esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Kinematic Analysis of CDHRM
### Joint-End Kinematics  
To establish the projection relationship between the joint space and working space, the relationship between the coordinate frame of each joint are derived via spatial coordinate transformation matrix. As shown in Figure 3a,b, the coordinate system \(I_{i}\) is established at the end of the \(i\)th joint.  
The pose of the \(k\) th joint frame \(I_{k}\) with respect to the base frame \(I_{0}\) can be expressed as follows:  
\[\begin{split}\int\limits^{k}_{0}T=\prod\limits^{k}_{i=1}\left( \int\limits^{I}T\times trans(H,0,0)\right)\end{split} \tag{7}\]where \(l_{i}^{\prime}T\) represents the corresponding homogeneous transformation matrix between adjacent frames \(I_{i}\) and \(J_{i}\), which can be derived with regard to (1) and (2).  
The key to realizing the manipulation of a CDHRM in a typical confined underwater scenario is the fast online solution of each joint angles \(\alpha_{i}\) and \(\beta_{i}\) according to the change of the end-effector's pose. However, due to the large number of DOFs, the inverse kinematics of CDHRM [49] is difficult to derive via numerical methods. As it will cost a large amount of computational time to solve the pseudo-inverse of the Jacobian matrix with the increase of DOFs, this has a negative effect on the CDHRM's real-time control. With regard to the special motion characteristics of CDHRM, a geometric method with high-efficiency is proposed as shown in Figure 4.  
The desired pose of the end-effector is determined by the task trajectory of some typical confined underwater scenarios. Then the desired pose of the joint frame \(I_{i-1}\) can be determined with regard to (7). With the desired pose of the joint frame \(I_{i-1}\), which joint to be adjusted can be determined. As this method does not need to solve all the joint angles in a certain task, it has lower computation cost and is suitable for real-time control.
# On the Feasibility of Wireless Multimedia Sensor Networks over IEEE 802.15.5 Mesh Topologies
## 5 Performance Evaluation
### Throughput and Message Delivery Ratio  
Throughput is defined as the amount of raw information (header and payload) received in a given period. From this definition, it is clear that throughput is directly related to the _message delivery ratio metric_, that is, to the rate of successfully received messages. Consequently, these two metrics are discussed below jointly.  
In order to evaluate the _message delivery ratio_, an entry is created in a text file for each message transmitted or received, as explained before, letting EvalVid check the number of packets sent and received.  
It should be noted that UDP is used at the transport layer over the IEEE 802.15.5 mesh network layer and IEEE 802.15.4 MAC and PHY layers. Therefore, if a packet does not arrive at the destination node for whatever reason, the packet will not be retransmitted.  
Since the size of queues has been assumed to be infinite, it can be deduced that packet losses are not related to the size of buffers. Consequently, packet losses are mainly caused by the occupation of the channel and the failure when accessing the medium through the CSMA-CA mechanism (no successful transmission after the maximum number of retries). In addition to existing medium contention, the exposed node problem and the hidden nodes problems also affect transmissions in the _active duration_, since CSMA-CA does not avoid collisions by hidden nodes or unnecessary backoff delays by exposed nodes [30]. The exposed node problem prevents a sender from transmitting to a receiver if the sender detects a signal from another node, even if that signal is not interfering at the receiver's location. This increases the probability of CSMA failures. Besides, even if a node manages to transmit a message, it may be lost due to the hidden node problem. In this regard, the reservation process that takes place during the _active duration_ will be affected by both primary (two hidden nodes transmitting to the same receiver) and secondary (receiver in range of another node transmitting to a different node)  
Figure 11: Jitter metric for the different scenarios.  
collisions. On the other hand, the _inactive duration_ will be affected to a lesser extent by the hidden terminal problem, as discussed in Section 5.6.  
As observed in Figures 12 and 13 AO values of 1 and, to a lesser degree, 2 present the worst performance in both the throughput and message delivery ratio. In addition, throughput is also affected by the selection of WO values, since it determines the separation between consecutive packets sent. That is, a longer _inactive period_ (or WO values) implies lower throughput. In particular, the best  
Figure 12: Throughput metric for the different scenarios.  
Figure 13: Message delivery ratio metric for the different scenarios.  
results for the throughput metric are obtained for 5-3 (WO-AO) configuration. In this case, the lengths of the _Wakeup Interval_ and Active Duration are found to be suitable for forwarding data messages without incurring long node delays.
## 4 Results and Discussion
### Prediction of Marshall Flow (MF) and Marshall Quotient (MQ)  
The SVM algorithm, found as the best predictor, was then employed to predict other properties of the SMA materials, namely MF and MQ. The convergence indicator I\({}_{\text{C}}\) and normalized I\({}_{\text{C}}\) are plotted in Figure 9. The predicted results of MF and MQ. The converged statistical values of R over 1000 Monte Carlo simulations were 0.9246, 0.9429 and 0.9085 for the MS, MF and MQ, respectively. A post-treatment was also performed at this stage. The outliers were removed using the quantile at 90% of RMSE computed by SVM as a threshold value, remaining 900 results for MF and MQ. An important fluctuation of I\({}_{\text{C}}\) with RMSE and MAE was observed in case of prediction of MF (Figure 9a) and prediction of MQ (Figure 9b). It observed that the required number of simulations is about 400 to obtain the converged statistical values of MF and MQ, with respect to RMSE and MAE. Again, it seems that 900 runs were sufficient to reach the converged solutions of MF and MQ. On the contrary, using R as criterion, statistical convergence values were obtained within 100 runs for all MS, MF and MQ. This confirmed the fact that an evaluation of a model generally requires at least two criteria for better quality assessment.  
It is interesting to note that over 1000 Monte Carlo simulations, the maximum values of R were 0.9915 and 0.9874 for MF and MQ, respectively. Therefore, the best performance of realization (i.e., the one that gave maximum R and minimum RMSE, MAE are obtained) could not truly reflect the robustness of a given AI algorithm. Using a well-trained AI model, for a parametric study for  
Figure 9: Statistical results of RMSE, MAE, R over 900 Monte Carlo simulation using SVM in case of: (a) Normalized I\({}_{\text{C}}\) for predicted MF; (b) Normalized I\({}_{\text{C}}\) for predicted MQ.  
instance [20], might be correct for only given dataset but not validated for all combinations of dataset. The results showed that the SVM has a good predictive capability in predicting the MS as well as other parameters (MF and MQ).
# Phishing Attacks Survey: Types, Vectors, and Technical Approaches
## 3 Phishing Methods and Techniques
### Phishing Media  
The medium used is the first consideration in phishing attacks. The medium limits the vectors and technical approaches (explored more in later sections) that can be applied. The medium itself is the method by which the phisher interacts with the target. There are three key media through which this interaction can take place, these are:  
* Voice
* Short messaging service (SMS)/multi-media messaging (MMS)
* Internet  
Voice, that is, speech and the use of language, is one of the oldest and most effective methods by which humans interact and convey information. It is often said that the use of language is what sets humans apart from other sentient creatures. Thus, it can be expected that this medium can be utilized to deceive individuals into revealing their personal information to those who wish to utilize it for their own benefit.  
In its modern form, SMS (short messaging service) is known as \"texting\". This involves communication through the use of short text-based messages, sent through a mobile network. This later developed into MMS (multi-media messaging service), which allows the transfer of content in addition to text, such as photos, videos, or audio clips [25; 26]. This medium provides a number of convenient ways in which a phisher can interact with targets and attempt to steal their personal data [27].  
The final medium considered is the internet. From its inception as the ARPANET [28] to the nebulous mass of websites now available, the constantly changing and developing nature of the internet means new methods of communication are being continually devised. This collection of communication methods is available in one convenient location and ranges widely from email to Instagram and Snapchat [29], each of which provides a means for phishers to \"hook\" potential victims. In the following section the specific vectors and channels that utilize these different media are discussed.
# Malware Classification Based on Shallow Neural Network
## 4 Experiments and Evaluations
### Experimental Results and Analysis
#### 4.2.3 Comparison with Other Works  
This section describes several comparative experiments we carried out. The comparative experiment consisted of two groups. One group used the common classifier of machine learning to perform classification, including Naive Bayes (NB), logistic regression (LR), support vector machine (SVM), random forest (RF) and Xgboost. The other group used the algorithms in the following three relevant works: the method used by the team who won the first place in the Kaggle malware classification competition [34], which takes a grayscale image generated from the.asm file, header information and the opcode n-gram as features with random forest as the classifier; the MalNet deep learning model [19] proposed by [PERSON] et al., which combines CNN and LSTM networks to deal with the static features of malicious code; and the method proposed by [PERSON] et al. [35], which feeds feature vectors composed of the TF-IDF(term frequency-inverse document frequency) values of the n-gram to five machine learning classifiers.  
The results are shown in Table 6.  
Figure 8: Comparing results obtained by the model using the standard n-gram method and the CTIB-n-gram algorithm, respectively. (**a**) Precision; (**b**) recall; and (**c**) micro-f1.  
The same training set and test set samples were used for each experiment. The metrics compared included classification precision, recall rate and time consumed, where the time referred to the test time and did not include the time spent in the decompilation process. The experimental results show that the SNNMAC model is superior to the common machine learning classifier in precision and recall rate. Compared with the related methods, the classification precisions and f1 values from the SNNMAC model were only slightly lower than the method of the Kaggle winner, but the training time was reduced greatly, which makes the SNNMAC model more suitable for a real antivirus scenario with a massive amount of malware.
# Seasonal Monitoring of Volatiles and Antioxidant Activity of Brown Alga _Cladosteplus spongiosus_
## 2 Results and Discussion
### Statistical Analysis of the Headspace VOCs  
The results of the principal component analyses (PCA) are shown in Figures 3a-d and 4. PCA was used to describe the variations among the dominant volatiles (>2%) in relation to the sampling month, material preparation (fresh or dry), and fibre.  
Figure 3: Correlation loadings (**a**,**_c_) and score plots (**b**,**_d_) of the dominant compounds from the headspace volatiles obtained via headspace solid-phase microextraction (HS-SPME) with two fibres divinylbenzene/carboxene/polydimethylsiloxane (f1) and polydimethylsiloxane/divinylbenzene (f2) and analysed via gas chromatography–mass spectrometry (GC-MS): fresh (Fr) and dried (Dr) _Cladostephus spongiosus_ samples collected from May to August.  
The correlation plot and score plot of the dominant components from data obtained by f1 and f2 fibres are shown in Figure 3a-d, respectively. The first two PCs of the data obtained by f1 fibre described 53.4% of the initial data variability. The highest variable contribution to PC1 was observed for tridecanal, hexanal, pentadec-1-ene, and 8-ionone, while ethanol, hexan-1-ol, octan-1-ol, and phenol contributed to PC2.  
The first two PCs in the data obtained by f2 fibre described 62.2% of the initial data variability. The abundance of two aldehydes (nonanal and tridecanal) contributed to PC1, while alcohols (ethanol and hexan-1-ol) and phenol had the highest contribution to the PC2. The distribution of the samples in the multivariate space showed no significant difference between the fibres. However, a clear separation between the fresh and dried samples was obtained (Figure 3b,d). For both f1 and f2, the dry samples were positioned on the left part of the multivariate space and the fresh samples, taken in May, were separated in the bottom right part of the score plot. Interestingly, the sampling month showed no effect on the dry samples, while in fresh samples there are similarities between June, July, and August, while May was clearly separated in the upper right.  
Figure 4a,b shows the PCA of the data for f1 and f2 analysed together. The initial data variability of the first two PCs described 52%; however, the fresh and dried samples were clearly separated based on the higher content of (_E_,_Z_)-octa-3,5-dien-2-one and benzyl alcohol, as well as low content of pentadecane in the fresh samples. The samples from both fibres were segregated according to the sampling month, with clear vertical distribution from May to August.
# Highly Sensitive SPAD-Based Receiver for Dimming Control in LiFi Networks +
## 4 Multi-Carrier Transmission Schemes with Dimming Control
### Dco-Ofdm
#### 4.1.1 PD Receivers  
The received discrete time domain signal \(y[n]\) is obtained after removing the scaling factor, DC-bias and the CP. The received electrical waveform is given by  
\[y[n]=hx_{t}[n]+w^{\text{rx}}[n], \tag{33}\]  
where \(h=\gamma\Re\zeta\) and \(w^{\text{rx}}[n]\) is the receiver noise with variance \(\sigma_{\text{rx}}^{2}\). Applying the FFT property to the received signal \(y[n]\) translates the signal to the frequency domain and converts the convolution operation into a multiplication one. In the frequency domain, the clipping noise \(w^{\text{rx}}[n]\) is transformed into additive Gaussian noise due to CLT. Therefore, an additional additive Gaussian noise component with zero-mean and variance \(\sigma_{\text{clip,DCO}}^{2}\) is present at each modulated subcarrier. The FFT operation is given by the following:  
\[Y[k]=\frac{1}{\sqrt{K}}\sum_{n=0}^{K-1}y[n]\,e^{-j2\pi\frac{\text{ln}}{K}}, \hskip 28.452756 pt0\leq k\leq K-1 \tag{34}\]The received signal can be calculated as:  
\[\begin{split} Y[k]&=H\Big{(}\eta X[k]+W^{\text{clip}}[k] \Big{)}+W^{\text{rx}}[k],\\ &=\eta HX[k]+HW^{\text{clip}}[k]+W^{\text{rx}}[k],\end{split} \tag{35}\]  
where \(H\), \(W^{\text{clip}}[k]\) and \(W^{\text{rx}}[k]\) denote the FFT of \(h[n]\), \(w^{\text{clip}}[n]\), and \(w^{\text{rx}}[n]\), respectively. It can be seen that the received signal can be written as the summation of the desired signal and two zero mean noises, clipping noise and receiver noise. Therefore, the SNR of the \(k\)th subcarrier for DCO-OFDM can be expressed as [34]  
\[\Phi[k]^{\text{DCO}}=\frac{\eta_{\text{DCO}}^{2}\frac{K}{K-2}|H|^{2}}{\sigma_{ \text{clip, DCO}}^{2}|H|^{2}+\sigma_{\text{rx}}^{2}}, \tag{36}\]  
where \(\sigma_{\text{rx}}^{2}\) is the receiver noise given by \(\sigma_{\text{rx}}^{2}=N_{0}B\), \(B=1/T_{\text{s}}\) is the signaling bandwidth, \(T_{\text{s}}\) is the symbol period, and \(N_{0}=4\epsilon T^{0}/R_{L}\) is the thermal noise power spectral density (PSD) with Boltzman constant, \(\epsilon\), load resistor temperature, \(T^{0}\), and load resistance, \(R_{L}\). The attenuation factor and clipping noise variance are given by [34; 46]  
\[\eta_{\text{DCO}}=Q(x_{b})-Q(x_{t}), \tag{37}\]  
\[\begin{split}\sigma_{\text{clip,DCO}}^{2}=&\eta_{ \text{DCO}}+\kappa_{b}\phi(\kappa_{b})-\kappa_{t}\phi(\kappa_{t})+\kappa_{b}^{ 2}[1-Q(\kappa_{b})]+\kappa_{t}^{2}Q(\kappa_{t})\\ &-\eta_{\text{DCO}}^{2}-[\phi(\kappa_{b})-\phi(\kappa_{t})+ \kappa_{b}[1-Q(\kappa_{b})]+\kappa_{t}Q(\kappa_{t})]^{2},\end{split} \tag{38}\]
# Comparison of the Biological Potential and Chemical Composition of Brazilian and Mexican Propolis
## 3 Discussion
### Propolis Production in Mexico and Brazil  
There is no official information on crude propolis trade. Mexico is among the primary honey producers worldwide and ranks the eighth in exports, being Germany its leading importer. However, Mexico is not among the exporting countries of propolis globally, and the leading exporters of this product are China, Brazil, Argentina, Cuba, Chile, Uruguay, and Canada [23]. Brazilian propolis is highly valued in the international market, and Brazil is one of the largest exporters of propolis globally. Brazilian propolis can reach a value up to three times higher (between 100 and 150 euros per kg) than Chinese propolis, mainly due to the differential in quality [24].  
Minas Gerais (MG), Rio Grande do Sul (RS), Bahia (Ba), Santa Catarina (SC) and Sao Paulo (SP) are the Brazilian states that stand out for their production of propolis. The main market for Brazilian propolis is Asia, 92% of all fresh propolis consumed in Japan is from Brazil. European countries and the United States are also consumers of Brazilian propolisis [9; 14; 25; 26; 27].  
Unlike Brazil, Mexico does not have reliable statistics indicating propolis production, nor does it have updated patents on this beekeeping product. In all regions of Mexico, propolis production is potential, and the propolis producing regions are classified as follows: the Northern Region, Pacific Coast Region, Gulf Region, Altiplano Region, and Peninsula of Yucatan. Compared to the other regions, the Peninsula of Yucatan is more organized, producing 6 tons per year of propolisis [28].  
In order to encourage the beekeeping industry for the production and regulation of propolis, the Mexican Official Standard for the Production and Specifications of Propolis, NOM-003-SAG/GAN-2016 [8], was approved by the Federal Commission for Regulatory Improvement (COFEMER) and recently promoted, regulating the product's quality and its sale at competitive prices in the international market. In Brazil, regulation of propolis production is promoted by a specific Normative Instruction--SDA No. 03, of 19 January 2001 [22]--Technical Regulation of Identity and Quality of Apitoxin, Bee Wax, Royal Jelly, Lyophilized Royal Jelly, Bee Pollen, Propolis and Propolis Extract ([[https://www.gov.br/agricultura/pt-br](https://www.gov.br/agricultura/pt-br)]([https://www.gov.br/agricultura/pt-br](https://www.gov.br/agricultura/pt-br)) (accessed on 23 October 2021). However, this regulation lacks many specifications regarding the production and quality of propolis
# Secure Transmission for Buffer-Aided Relay Networks in the Internet of Things
## 3 Transmission Schemes
### SCJ Scheme  
In this case, when the second hop is selected, the source sensor can send jamming signals to the eavesdroppers with the transmit power \(P_{j_{2}}\), which degrades the quality of eavesdroppers' channels effectively without interfering other nodes. Furthermore, due to the total power constraint, we have \(P_{S_{2}}+P_{j_{2}}+P_{R_{2}}=P_{Total}\), where \(P_{S_{2}}\) denotes the transmit power of the source sensor when transmitting useful information. Similar to the NJ scheme, the power allocation follows the uniform allocation rule, i.e., \(P_{R_{2}}=P_{Total}/2\), \(P_{S_{2}}=P_{J_{2}}=P_{Total}/4\).  
The first hop of the SCJ scheme is the same as the NJ scheme, hence we have \(\gamma_{SR_{i}}^{SCJ}=P_{S_{2}}\left|h_{SR_{i}}\right|^{2}/\sigma^{2}\) and \(\gamma_{SE_{k}}^{SCJ}=P_{S_{2}}\left|h_{SE_{k}}\right|^{2}/\sigma^{2}\). In the second hop, the received signal-to-interference-plus-noise-ratio (SINR) at \(E_{k}\) is given by  
\[\gamma_{R_{j}E_{k}}^{SCJ}=\frac{P_{R_{2}}\left|h_{R_{j}E_{k}}\right|^{2}}{ \sigma^{2}+P_{J_{2}}\left|h_{I{E_{k}}}\right|^{2}}, \tag{12}\]  
where \(\left|h_{I{E_{k}}}\right|^{2}\) denotes the link of \(S\to E_{k}\) when \(S\) acts as a jamming node.  
Thus, for the NCE scenario, the received SNR and SINR at the eavesdroppers of the first and second hops can be written as  
\[\gamma_{1,NCE}^{SCJ}=\frac{P_{S_{2}}\max_{1\leq k\leq K}\left(\left|h_{SE_{k}} \right|^{2}\right)}{\sigma^{2}}, \tag{13}\]  
\[\gamma_{2,NCE}^{SCJ}=\max_{1\leq k\leq K}\left(\frac{P_{R_{2}}\left|h_{R_{j}E_{ k}}\right|^{2}}{\sigma^{2}+P_{J_{2}}\left|h_{I{E_{k}}}\right|^{2}}\right). \tag{14}\]  
For the CE mode, the received SNR and SINR of the eavesdroppers' channel for the first and second hops are given by  
\[\gamma_{1,CE}^{SCJ}=\frac{P_{S_{2}}\left\|\mathbf{h}_{SE}\right\|^{2}}{\sigma ^{2}}, \tag{15}\]  
\[\gamma_{2,CE}^{SCJ}=\frac{P_{R_{2}}\left\|\mathbf{h}_{R_{j}E}\right\|^{2}}{ \sigma^{2}+P_{J_{2}}\left\|\mathbf{h}_{I{E}}\right\|^{2}}, \tag{16}\]where \(\mathbf{h}_{JE}\) represents the \(K\times 1\) channel vector between the source sensor and the eavesdroppers when the source acts as a jamming node.
# Deep Learning-Based Ground Target Detection and Tracking for Aerial Photography from UAVs
## 3 Detection-Tracking Framework  
### Framework Architecture  
The workflow of the framework is shown in Figure 1. Feature information of the preset target and the video sequence should be provided in the target detection-tracking framework. The target detection algorithm detects the potential target appearing in the current frame and performs feature matching with the preset target. If there is a preset target in the potential target, the target tracking algorithm is initialized using the target's position to perform continuous tracking of the target. If the target is missed during tracking process, the target detection module will be restarted to find the target again.