esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Antenna Design and Analysis  
### Antenna Structure  
Figure 1 displays the detailed configuration of the designed 2 \(\times\) 2 broadband CP array antenna. The structure of the antenna is composed of two parts, of which the top structure is an SP-fed circular wide-slot antenna, and the lower part is the reflecting surface of the AMC structure, surrounded by a vertical metal plate. The three-dimensional view of the design is demonstrated in Figure 1a. The overall antenna size is 106 \(\times\) 106 \(\times\) 14.9 mm\({}^{3}\), both employing 1.6 mm thick FR4 material. The thickness of the air layer between the antenna and the AMC reflective surface is 14 mm. The antenna consists of four circular wide-slit antenna cells rotated sequentially. Figure 1b,c present the top and side views of the design, respectively. Three L-shaped branches are added to the circular wide-slot antenna unit to improve the AR bandwidth. The radiating layer of the antenna employs a sequential phase-shifted feed network that maintains a 90\({}^{\circ}\) phase difference at the four output ports, as shown in Figure 1d. The optimal parameters are L11 = 15.9 mm, L12 = 92 mm, L13 = 106 mm, L14 = 15.6 mm, R3 = 6.5 mm, w6 = 1.1 mm, w7 = 0.7 mm, w8 = 0.64 mm, w9 = 0.62 mm, w10 = 0.81 mm, and w11 = 14.9 mm.
# MVCG-SPS: A Multi-View Contrastive Graph Neural Network for Smart Ponzi Scheme Detection
## 5 Experiments  
### Dataset Preprocessing  
For our experiments focused on detecting Ponzi schemes within the Ethereum ecosystem, we utilized datasets extracted from the \"Heterogeneous Feature Augmentation for Ponzi Detection in Ethereum\" study [34]. Specifically, we employed the **Heterogeneous Ghet** dataset, which encompasses 57,130 nodes and 156,255 edges, and includes 4616 CAs and 52,514 EOAs, with 69,653 call edges and 86,602 transaction edges, labeled with 191 Ponzi accounts(see Table 1).  
To prepare these datasets for our MVCG-SPS model, we conducted a meticulous preprocessing phase. The graph construction involved nodes representing Ethereum accounts (both CAs and EOAs) and edges denoting various interactions, such as transactions and contract calls. Node features were meticulously extracted to reflect account behaviors, including but not limited to the average transaction amount and the frequency of contract deployments.  
These features were processed using a Meta-Path Based View approach to construct multiple heterogeneous views of the data, capturing different semantic relationships and complementary information. This approach allowed us to better integrate diverse perspectives, including the transaction frequency, fund flows, and functional dependencies, which are crucial for detecting complex fraudulent behaviors in blockchain ecosystems.
## 2 Establishment of Measurement Control Network Model
### Simulation of Adjustment Algorithm  
Based on the optimal locations simulated in Section 3.1 and the length-angle mixed intersection adjustment method proposed in this paper, simulation analysis was conducted using MATLAB to estimate the measurement performance of this method within a range of 660 m. The simulation conditions are as follows: the track length is set to 660 m, with target points placed every 3 m along one side of the track, totaling 221 target points; overall, 44 reference points are distributed around the track, and 11 laser trackers are mounted along the track's centerline and positioned in the middle of the measurement range as illustrated in Figure 10, with the instruments at a height of 1.5 m from the track and two meters away from the target points. The total station and the laser trackers are on the same horizontal plane, with the total station positioned in the middle of the measurement range, and the total station is one meter away from the laser tracker along the y axis, measuring reference points along the long track. The measurement accuracy of the laser tracker and the total station is consistent with that described in Section 2.2.  
Figure 10: Contour plot of measurement error of target point coordinates under different instrument positions.  
Figure 9: The schematic diagram of measuring station distribution.
# A Novel Framework Leveraging Large Language Models to Enhance Cold-Start Advertising Systems
## 4 Validation Methodology
### Validation of Stages 1 and 2  
For Stages 1 and 2, a quantitative experimental design comparing the transformer-enhanced ensemble approach against established baseline methods was performed. The validation process involves:  
* Stage 1 Validation: Several state-of-the-art recommendation models: DCN-V2, DIFM, FiBiNET, and MMO, were used as strong baselines for comparison. These models were evaluated on the two benchmark datasets using standard performance metrics, including AUC and accuracy. Subsequently, we modified their embedding mechanisms by integrating transformer-based enhancements, replacing the traditional shallow embeddings. Comparative experiments were then conducted to systematically assess the impact of these transformer-enhanced representations against the original architectures in frozen-start scenarios.
* Stage 2 Validation: To evaluate the effectiveness of ensemble integration, we implemented the weighted ensemble model that combines the outputs of the individually enhanced models (DCN-V2, DIFM, FiBiNET, and MMOE). The ensemble was tested on the same datasets, and its performance was compared directly against each individual model. This allowed us to assess whether combining transformer-enhancedarchitectures could yield further gains in AUC and accuracy beyond what each model achieved independently.
# Camouflage Backdoor Attack against Pedestrian Detection
## 4 Experiments
### Experimental Results and Analysis  
In order to validate the stealthiness of the camouflage backdoor attack proposed in this paper, we trained different pedestrian detection models based on benign and malicious datasets separately. Subsequently, we conducted validation on the benign testing dataset. Table 2 illustrates the performance comparison between the poisoned model trained on attack samples and the regular benign model trained on benign samples. From the table, it can be observed that despite the poisoned model being trained on attack images with triggers, its performance on the benign testing dataset is similar to that of the benign model. This indicates that the poisoned model trained on adversarial samples exhibits a high level of stealthiness. When the backdoor trigger is not activated, its performance is nearly identical to that of the normal model.  
To validate the effectiveness of the attack model proposed in this paper, we added triggers to pedestrians in the benign testing dataset to create a poisoned testing dataset. Then, we compared our trained poisoned models on both the benign dataset and the poisoned dataset. Figure 3 presents the results for different poisoned models in terms of mAP, mAR, AP\({}_{50}\), and AP\({}_{75}\). From the figure, it can be observed that the poisoned models trained using the camouflage backdoor attack exhibit decreased performance (mAP, mAR, AP\({}_{50}\), AP\({}_{75}\)) on the poisoned dataset with triggers. Specifically, in the case of the mAR metric, there is a maximum performance drop of approximately 20%. This means that when pedestrians carrying triggers appear in autonomous driving scenarios, the model's backdoor is activated, allowing pedestrians with triggers to bypass the model's detection, thereby pos  
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline \multicolumn{2}{c}{**Dataset \(\rightarrow\)**} & \multicolumn{4}{c}{**Benign Testing Dataset**} \\ \hline \multirow{2}{*}{**Models \(\downarrow\)**} & **Methods \(\downarrow\), Metrics \(\rightarrow\)** & **mAP** & **AP\({}_{50}\)** & **AP\({}_{75}\)** & **mAR** \\ \hline \multirow{2}{*}{Cascade R-CNN} & Benign & 30.9 \(\pm\) 0.0 & 63.9 \(\pm\) 0.1 & 26.1 \(\pm\) 0.0 & 40.3 \(\pm\) 0.5 \\  & Poisoned (Ours) & 30.6 \(\pm\) 0.2 & 63.5 \(\pm\) 0.1 & 24.9 \(\pm\) 0.6 & 39.8 \(\pm\) 0.5 \\ \hline \multirow{2}{*}{RetinaNet} & Benign & 28.6 \(\pm\) 0.7 & 63.4 \(\pm\) 0.2 & 20.4 \(\pm\) 1.2 & 41.5 \(\pm\) 0.3 \\  & Poisoned (Ours) & 28.4 \(\pm\) 0.1 & 62.4 \(\pm\) 0.0 & 21.4 \(\pm\) 0.7 & 41.2 \(\pm\) 0.2 \\ \hline \multirow{2}{*}{Faster R-CNN} & Benign & 28.5 \(\pm\) 0.2 & 61.9 \(\pm\) 0.1 & 22.2 \(\pm\) 1.6 & 39.4 \(\pm\) 0.1 \\  & Poisoned (Ours) & 28.6 \(\pm\) 0.0 & 63.0 \(\pm\) 0.2 & 21.9 \(\pm\) 0.2 & 39.4 \(\pm\) 0.1 \\ \hline \multirow{2}{*}{FCOS} & Benign & 30.9 \(\pm\) 1.0 & 67.5 \(\pm\) 1.0 & 23.6 \(\pm\) 0.6 & 42.0 \(\pm\) 0.8 \\  & Poisoned (Ours) & 29.2 \(\pm\) 0.1 & 65.8 \(\pm\) 0.1 & 21.5 \(\pm\) 0.4 & 39.8 \(\pm\) 0.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The performance (%) of methods on the benign testing dataset.
## 3 Relative Dynamics for Spacecraft Formation  
Unlike the traditional relative orbit motion equation expressed in the directions \(x\): radial, \(y\): in-track and \(z\): cross-track (RIC), with the origin located in the master spacecraft [17; 18], here we use a radar system that was suggested by [PERSON] and [PERSON] [19] by variables transformation as:  
\[x=\varrho\cos\phi\cos\theta \tag{11}\]  
\[y=\varrho\cos\phi\sin\theta \tag{12}\]  
\[z=\varrho\sin\phi \tag{13}\]  
where \(\varrho\) denotes the relative distance between the formation spacecrafts, and \(\theta\) and \(\phi\) are the azimuth and relative elevation angle. The relative motion equations can finally be transformed to [20]:  
\[\ddot{\boldsymbol{\eta}}=f(\boldsymbol{\eta},\dot{\boldsymbol{\eta}},t)+G( \boldsymbol{\eta})\mathbf{w} \tag{14}\]  
where \(\boldsymbol{\eta}=[\varrho\ \ \theta\ \phi]^{T}\), \(\mathbf{w}=[w_{\varrho}\ \ w_{\theta}\ \ w_{\phi}]^{T}\), \(f=\{f_{\varrho}\ \ f_{\phi}\ \ f_{\phi}\}\) with  
\[\begin{split} f_{\varrho}(\boldsymbol{\eta},\dot{\boldsymbol{ \eta}},t)=&(\omega^{2}+2\omega\dot{\theta}+\dot{\theta}^{2}) \mathrm{cos}^{2}\phi+\varrho\dot{\phi}^{2}\\ &-\frac{\mu(r\cos\theta\cos\phi+\varrho)}{[r^{2}+\varrho^{2}+2r \varrho\cos\theta\cos\phi]^{\frac{3}{2}}}+\frac{\mu}{r^{2}}\cos\theta\cos \phi\end{split} \tag{15}\]  
\[\begin{split} f_{\phi}(\boldsymbol{\eta},\dot{\boldsymbol{ \eta}},t)=& 2(\omega+\dot{\theta})\dot{\phi}\tan\phi-\dot{\omega}-2( \omega+\dot{\theta})\frac{\dot{\phi}}{\varrho}\\ &+\frac{\mu(r\sin\theta\sec\phi)}{\varrho[r^{2}+\varrho^{2}+2r \varrho\cos\theta\cos\phi]^{\frac{3}{2}}}-\frac{\mu}{r^{2}\varrho}\sin\theta \sec\phi\end{split} \tag{16}\]  
\[\begin{split} f_{\phi}(\boldsymbol{\eta},\dot{\boldsymbol{ \eta}},t)=&-\frac{1}{2}(\omega+\dot{\theta})^{2}-\frac{2\dot{ \varrho}\dot{\phi}}{\varrho}-\frac{\mu}{r^{2}\varrho}\cos\theta\sin\phi\\ &+\frac{\mu r\cos\theta\sin\phi}{\varrho[r^{2}+\varrho^{2}+2r \varrho\cos\theta\cos\phi]^{\frac{3}{2}}}\end{split} \tag{17}\]  
Figure 4: Signal processing hardware platform, including the K/Ka-band dual-frequency MWR processing module, BeiDou III B1C/B2a dual-frequency navigation processing module, and external interface module.  
The denotations of variables/matrix \(\mu,r,\omega,G(\mathbf{\eta})\), and \(\mathbf{w}\) in (14)-(17) can be found in [20], and are not explained here for brevity. For the purpose of real formation simulation, the master and slave spacecrafts are separately propagated in an inertial frame, and the relative position and velocity are computed with differences and transformed into the master RIC frame and radar system in (11)-(17), considered to be the real orbit values. Gravitational and non-gravitational accelerations are considered, with the models shown in Table 1, as:
## 3 System Model  
Figure 1 demonstrates a single-cell network where downlink communications are employed with the assistance of IRS. It is utilised when an IRS is sent out to assist with communications across a certain frequency band between \(K\) single-antenna clients and a multi-antenna AP. The number \(K\) represents the collection of users. \(M\) and \(N\) represents the number of transmit antennas at the AP and the number of reflecting elements at the IRS, respectively [31]. If the received signal is reflected more than once by the IRS, then it is considered negligible due to huge amount of path loss and can be discarded without any significant loss in accuracy.  
The channels between AP and user \(k\) is denoted by \(\mathbf{h}_{r,k}^{H}\in\mathbf{C}^{1\times N}\). The channel between AP and IRS are denoted by \(\mathbf{G}\in\mathbf{C}^{N\times M}\). The channel between IRS and the user \(k\) is denoted by \(\mathbf{h}_{d,k}^{H}\in\mathbf{C}^{1\times M}\) where \(k=1,\cdots,K\). Let \(\mathbf{\theta}=[\theta_{1},\cdots,\theta_{N}]\) and is a diagonal matrix \(\mathbf{\Theta}=\text{diag}(\beta_{1}e^{j\theta_{1}},\cdots,\beta_{N}e^{j\theta N})\). Here, \(j\) denotes the imaginary unit value of the IRS's reflection-coefficients matrix, where \(\theta_{n}\in[0,2\pi)\) and \(\beta_{n}\in[0,1]\) denote the phase shift angle and the amplitude of reflection coefficient of the \(n^{th}\) element of the IRS, respectively. Hence, the AP-IRS link, the phase shift angle of IRS reflection, and the link between IRS and user are combined to form the AP-IRS-user composite channel [4].  
In this paper, linear transmit precoding is used at the AP where each individual user is allocated a unique beamforming vector. Hence, \(x=\sum_{k=1}^{K}w_{k}s_{k}\), can be used to represent the baseband broadcast signal that is complicated at AP where \(w_{k}\in C^{M\times 1}\) represents the associated beamforming vector and \(s_{k}\) are the transmitted data for the user \(k\). It is presumed that \(s_{k}\) represents a random variables with mean zero and unity variance, where \(k=1\), \(\cdots\), \(K\). The combined received signal at user \(k\) through the AP-user as well as through the AP-IRS-user route can be written as [4]:\[y_{k}=(\mathbf{h}_{r,k}^{H}\mathbf{\Theta}\mathbf{G}+\mathbf{h}_{d,k}^{H})\sum_{j=1 }^{K}\mathbf{w}_{j}s_{j}+n_{k},k=1,...,K \tag{1}\]  
where \(n_{k}\sim\mathcal{CN}\) (\(0,\sigma_{k}^{2}\)) is additive white Gaussian noise (AWGN) at the receiver of the user \(k\). Hence, user \(k^{\prime}\)s SINR can be expressed as:  
\[\text{SINR}_{k}=\frac{|(\mathbf{h}_{r,k}^{H}\mathbf{\Theta}\mathbf{G}+\mathbf{ h}_{d,k}^{H})\mathbf{w}_{k}|^{2}}{\sum_{j\
eq k}^{K}{(\mathbf{h}_{r,k}^{H} \mathbf{\Theta}\mathbf{G}+\mathbf{h}_{d,k}^{H})\mathbf{w}_{j}|^{2}+\sigma_{k} ^{2}}},\forall k. \tag{2}\]  
Consequently, the achievable rate of the user \(k\) can be defined as [4]:  
\[R_{k}=\log_{2}(1+\text{SINR}_{k}). \tag{3}\]  
Next, the optimisation problem is formulated to maximise the rate of the user with worst achievable rate using IRS.
# Discrete HMM for Visualizing Domiciliary Human Activity Perception and Comprehension
## 3 Experimental Results  
### Dataset and Activity Implications  
In this work, to validate the performance of the proposed D-HMM-based HAC framework prior to autonomous AI vision interaction with user domiciliary, we assess its performance on eleven daily life activities, including sitting, standing, walking, drinking water, calling up, reading, stretch, akimbo, follow-me, summoning, and stopping. For example, in elderly care, \"slow-go\" or \"no-go\" elderly people with aging mobility might be assisted by homecare robots, such that users can use a summoning gesture to call for a robot's help and a follow-me or stopping gesture to control the actions of the robot mobilities. Akimbo might be perceived by a robot as a user probably implying a spirit of competition, a strong sense of attack, or an enterprising spirit. Sitting, standing, or reading activities may be recognized by a robot as implying that the user is in a state of relaxation or concentration, whereas the stretch pose, generally performed as an unconscious behavior, implies the user is in a sleepy state. Images of the paradigms of the eleven activity poses with corresponding pose skeletons are shown in Figure 6.  
Prior to validating the proposed work, eleven different human activities are filmed and collected into a database through a single, fixed binocular camera with an RGB-D sensor. The distance between the front of the camera and the participants ranged from 4 to 11 feet to yield RGB images with 1920 \(\times\) 1080 resolution and depth information with 512 \(\times\) 424 resolution, with skeleton joints at 30 fps for each pose sequence. In order to further verify the robustness of the proposed D-HMM-based HAC, each activity is filmed for five participants, five times. Particularly, to inspect the robustness of the drinking water activity, left and right hands, respectively, each person was filmed two times. In total, 275 samples of sequences with 16,172 frames were collected into a database. The number of frames for each sample video spanned between 33 to 156 frames. The sampling RGB image with corresponding skeleton clips in the dataset are shown in Figure 6. Note that the RGB images are just for illustration, and only the skeletonized joint information is used for activity recognition. The evaluation platform featured a 3.6 GHz Intel Core i7 CPU laptop and implemented by C#.  
Figure 6: Sampling images of the assessing eleven poses in activities snaps with corresponding skeletons from videos of the collected database. Activities labeled from top to bottom, left to right, including (**a1,a2**) standing, (**b1,b2**) sitting, (**c1,c2**) drinking water, (**d1,d2**) calling up, (**e1,e2**) reading, (**f1,f2**) stretch, (**g1,g2**) akimbo, (**h1,h2**) summon, (**i1,i2**) follow-me, (**j1,j2**) stop gesture, and (**k1,k2**) walking poses.
## 3 Utilization of Precursor Compounds from Primary Metabolism in Secondary Metabolism
### Supply of Key Precursors Carbon, Nitrogen, Phosphate and Sulfur for Secondary Metabolism
#### 3.2.2. Supply of Nitrogen for Secondary Metabolism  
Nitrogen can be incorporated into secondary metabolism in different ways via the metabolism of nitrogen-containing compounds, such ammonium, nitrate, amino acids, amino sugars, urea and others. Antibiotic production in bacteria is largely affected by the availability of nitrogen sources, such as ammonium, nitrate, amino acids (e.g., glutamate, glutamine, lysine and serine) and polyamines (putrescine, spermidine, spermine and cadaverine) that come mainly from primary metabolism [8; 57]. Nitrogen-containing compounds may increase, decrease, or even stop the production of secondary metabolites. On the other hand, secondary metabolism is also controlled by diverse transcriptional regulators in response to nitrogen availability, e.g., ArgR, AfsQ1, Crp, DasR, PhoP and the response regulator MtrA (master transcriptional regulator A) [5].  
Nitrogen-containing compounds such as ammonium can inhibit antibiotic production. On the other hand, nitrate can stimulate rifamycin synthesis. The effect of intracellularnitrogen conditions on antibiotic production depends on the amount of phosphate or carbon in the cell.  
Nitrogen can be directly incorporated into secondary metabolites as a part of amino acids, e.g., the amino acids leucine, isoleucine, lysine and valine, which serve as precursors for polykeptides. Enzymatic reactions from cellular metabolism, which involve amino acids, can provide reaction products, which are compounds that can provide nitrogen for secondary metabolism as well, e.g., [S,S]-ethylenediamine-disuccin acid (EDDS) [109]. Another source of nitrogen is aminated carbohydrates (e.g., amino sugars) that can be produced in the direct transamination of sugars. N-O compounds that are provided from specific pathways can be incorporated into secondary metabolism as well, e.g., cemomycin biosynthesis.
###### Abstract
The solid element or beam element is generally utilized to simulate wall piers or members similar to its structural type from the present research, but it takes a longer time to compute and requires higher computer performance for the solid element. Furthermore, the transverse aspect ratio of this kind of pier is far less than 2.5 and its structural type is different from the ordinary single-column pier, double-column pier or bent pier. Although the structural form of the shear wall is similar to that of the wall pier, the force applied has a great difference in the simulation using the shell element. In the low cycle reciprocating loading analysis or pushover analysis of the shear wall, the horizontal lateral loading is usually applied to only one point due to the small size of the member in the direction subject to the analysis. Meanwhile, it is considered that the out-of-plane performance is similar to the bending behavior of one-dimensional column members [5,11,12]. However, the wall pier, as an important component of the covered bridge, is no longer a single-point loading cantilever column under the action of a longitudinal earthquake, which cannot better conform to the characteristics of the beam element. Its horizontal loading pattern is related to the location of superstructure load transferring. The width of the wall pier is much larger than the size of the other two directions and the responses of different positions on the pier are different in the out-of-plane dimension. A wall pier with a uniform cross-section of a rounded rectangular form, commonly used in covered bridges, is taken as the prototype. The finite element models are established by employing the multi-layered shell element in OpenSEES to perform numerical analyses under low cycle reciprocating loading in the weak axis direction of the pier. The influence of the discretization of layers along the thickness direction and mesh divisions on the hysteretic performance of wall pier is discussed. Moreover, the seismic performance of the wall pier under different horizontal lateral loading patterns is focused on and the optimum layout of bearings on the wall pier is recommended.