esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Cellobihydrolase Integration and Activity  
As mentioned, it is likely that our Ch12 targeting yielded off-target integration. While this could be confirmed with whole genome sequencing, this was considered beyond the scope of our study. [PERSON] et al. [40] developed an efficient set of vectors, which enabled multiple simultaneous integrations of genes into specific \"safe sites of insertion\". The insertion sites are located between essential elements, which limits the occurrence of chromosomal aberrations due to the lethal effect this would cause [33]. We concluded that the Ch12 targeting sequence we selected might not be unique in the genome, allowing unwanted off-target DSBs in areas that lacked the presence of essential elements required for stable gene integration. Interestingly, the M1744-Ch12-CBH1 strain had double the amount of _T.e.cbh1_ gene copies of the M1744-\(\Delta\)-CBH1 strains, which exhibited significantly higher CBH activity. This again illustrated that the locus of integration might be of greater importance than simple copy number with regards to successful heterologous protein production, though the exact locus of this delta-targeted integration in the genome was not determined.  
The two CRISPR-Cas9 systems used in this study were effective in producing recombinant proteins in both diploid and haploid strains of _S. cerevisiae_. However, we discovered that the two different genes (_T.e.cbh1_ and _T.e.cg2_) we integrated into the yeast genome at different sites produced variable results in the M1744 strain background, as the sites with high EG activity were found to have lower CBH activity and _vice versa_. Furthermore, the delta-targeted sites performed better than all other sites at expressing high levels of CBH. However, in the EG transformants, the single locus targeted sites were more efficient at expressing EG than the CBH transformants that also had the gene integrated at the single locus sites. [PERSON] et al. [41] discovered a correlation between the integration sites conferring the lowest and highest levels of expression. Low levels of expression were associated with the telomeres and centromeres, whereas high levels of expression have traditionally been associated with ARSs. In our study, intergenic chromosomal target sites were selected that were previously shown to support heterologous protein production at high levels [33]. However, it is clear that reporter protein-specific factors exist in obtaining high levels of expression of a specific gene of interest.
# Analysis of MTF in TDI-CCD Subpixel Dynamic Super-Resolution Imaging by Beam Splitter
## 3 Experiments
### Simulation Analysis  
#### 3.1.1 Simulation of Subpixel Imaging with Linear CCD  
The MTF simulation results according to Section 2.2 with the normalized pixel size \(p\) are shown in Figure 7.  
The blue curve represents the sampling MTF of the linear CCD pair with the ideal staggered distance \(a=\frac{p}{2}\). The green curve represents the sampling MTF with \(a=\frac{p}{4}\). The red curve is the sampling MTF from a single linear CCD.  
Figure 7: MTF comparison of single linear CCD and subpixel linear CCD pair.  
From Figure 7, we can see the MTF value at Nyquist frequency are 0.5732, 0.5388 and 0.4053 respectively. So, subpixel imaging apparently does improve MTF when the linear CCD pair are staggerd with ideal distance of half a pixel size, which is consistent with theoretical result in Section 2.2.  
In order to quantitatively evaluate the influence due to the misaligned sampling points on the MTF in subpixel imaging, we will use the parameter RM which is the ratio of \(MTF_{a}\) to \(MTF_{i}\) under the Nyquist frequency as the basis for the evaluation,  
\[RM=\frac{MTF_{a}}{MTF_{i}} \tag{10}\]  
where \(MTF_{a}\) represents the MTF of the sampling point at any position \(a\), which \(a\in[0,p]\), \(MTF_{i}\) represents the ideal MTF.  
From Figure 8, we can see obviously that \(a=p/2\) is the ideal position, where MTF has the maxim value and \(RM=1\). The more the distance of \(a\) deviates from the ideal location, the lower the \(RM\) value.  
Figure 9 shows the value of MTF at the Nyquist frequency in the subpixel imaging system with multiple linear CCDs that satisfy the ideal sampling condition \(a_{i}=p/2n\) according to result in Section 2.2. MTF appears an exponential growth trend with the imaging pairs number of linear CCDs. So, if we want to get a high resolution image through sub-pixel dynamic super-resolution imaging, 3 or 4 subpixel image pairs may be the most appropriate choice.  
Figure 8: Relation diagram between RM and sampling point location.  
Figure 9: MTF simulation result of the subpixel imaging system with multiple images.
# An L-Slot Frequency Reconfigurable Antenna Based on MEMS Technology
## 1 Introduction
In 2020, a novel design of a compact low-cost quad band reconfigurable antenna is presented by [PERSON] et al. It achieves four frequency states (9.92/14.86/18.21/21.2 GHz) with four PIN switches and has a dimension of 30 \(\times\) 36.7 mm\({}^{3}\)[16]. In 2021, [PERSON] et al. proposed the introduction of on-chip switches to realize a frequency reconfigurable antenna in order to enhance the bandwidth (29.5-51.0 GHz) of the antenna [17]. However, the gain is only 3.3 dBi. However, the aforementioned reconfigurable antennas have a larger size, a lower operating band, fewer tunable modes, and a narrower range of tunable frequencies, which still do not meet the future requirements of millimeter wave wireless communications. In this paper, we propose an MEMS switch-based L-slot frequency reconfigurable antenna for the U-band, capable of four tuning states. The RF MEMS switch applied in this antenna has desirable switching characteristics, such as excellent linearity, high isolation, low insertion loss, and miniaturization. The current distribution around the L-slot is tuned by controlling the on-off state of the MEMS switch, which modifies the effective electrical length of the antenna and enables frequency reconfiguration. It can be applied to 5G millimeter wave communication, unmanned driving, and telemetry.
# Recent Advances in Efficient Dynamic Graph Processing
## 2 Preliminaries  
A general and undirected graph \(G(V,E)\) contains a set of vertices \(V\) and a set of edges \(E\). The degree of a vertex \(v\) in \(G\) is the number of its neighbors in \(G\). In other words, based on different application scenarios, graphs can have more specific definitions as follows.  
* _Directed Graph_. For a directed graph \(G(V,E)\), each edge \(e(u,v)\) of \(G\) means a direct relationship between \(u\) and \(v\), denoted as \(u\to v\). In addition, a directed acyclic graph (DAG) is a directed graph that has no cycles. Figure 1(a) depicts a directed graph with five nodes.
* _Bipartite Graph_--a bipartite graph is a graph whose vertices can be divided into two disjoint and independent sets, \(U\) and \(V\), denoted as \(G(U,V,E)\), such that every edge connects a vertex in \(U\) to one in \(V\). Figure 1(b) depicts a bipartite graph.
* _Weighted Graph_--a weighted graph has a number assigned to each of its edges, called its weight. The weight can be used to represent distances, influence or capacities. Figure 1(c) depicts a weighted graph where each edge has a weight.
* _Attributed Graph_--an attributed graph is defined as \(G=(V,E,A)\), where \(V(E)\) is the node (edge) set and \(A\) is the set of attributes associated with nodes. For each node \(v\in V\), it has a set of attributes \(A(v)=\{a_{0},\ldots,a_{m}\}\). Figure 1(d) illustrates an attributed graph.  
Figure 1: Outline of this survey.  
Figure 2: Example of graphs.  
graph, in which each vertex represents a user in a social network, and the associated keywords capture the user's interests.  
A dynamic graph is characterized by changes performed on the set of nodes and/or the set of edges. These changes may correspond to insertions or deletions of vertices or edges. The dynamic types of graphs can be summarized into four cases:  
* _Incremental Dynamic_--a dynamic graph is incremental dynamic if only vertices or edges are inserted into \(G\).
* _Decemental Dynamic_--a dynamic graph is decremental dynamic if only vertices or edges are deleted into \(G\).
* _Fully Dynamic_--fully dynamic graphs match the scenarios such that there are not only vertices or edges additions but also vertices or edges deletions.
* _Streaming Dynamic_--the graph evolves over time through a continuous stream of updates, where nodes and edges are added or removed incrementally.  
In this survey, we review papers regarding to several kinds of graphs under different update types.
# AccFlow: Defending against the Low-Rate TCP DoS Attack in Drones
## 5 Experimental Results
### Benign Periodic Flow  
Real-life networks, such as the Internet, also carry periodic or bursty flows whose traffic patterns are similar to that of the low-rate TCP DoS attacking flows. One example is that YouTube generates periodic traffic by loading chunks of a video with pauses between each chunk [34]. In this subsection, we show that AccFlow does not falsely drop the packets from benign periodic flows through the following four experiments. In the first experimental setup, five normal TCP flows each transmitting at a rate of 1 Mbps and one benign periodic flow are sharing the network resources. The analysis in [34] reveals that the peak rate for a typical YouTube flow ranges from hundreds of kilobytes to several megabytes. The interval of each video chunk ranges from hundreds of milliseconds to seconds. We set the peak rate and period of the periodic flow in our simulation to be 3 Mbps and 200 ms so that it can represent real video traffic. Since the bottleneck link bandwidth is 10 Mbps which is large enough to hold all the traffic, the first setup is congestion free. In the second setup, we create a fairly congested network by generating nine normal TCP flows and the same periodic flow. In the third setup, the network becomes quite congested by carrying 15 normal TCP flows and the same periodic flow. Finally, in the fourth setup, the network is very congested as 20 normal TCP flows and the same periodic flow traverse the bottleneck link.  
The simulation results are illustrated in Figure 12. For clear presentation, we use characters \"N\", \"F\", \"Q\", and \"V\" to represent words \"Not\", \"Fairly\", \"Quite\", and \"Very\", respectively. The character \"Cg\" represents the word \"Congested\". Thus \"F Cg\" means that the network is fairly congested, which corresponds to the second setup. As illustrated in Figure 12a, the benign periodic flow achieves its desired throughput in all four setups no matter whether AccFlow is applied or not. Furthermore, AccFlow also does not have any negative effect on the normal TCP flows, as illustrated in Figure 12b. The results indicate  
Figure 11: AccFlow effectively defends against the SSTF attack.  
that AccFlow seamlessly coexists with benign flows, maintaining their performance without degradation. This successful coexistence can be attributed to the fundamental difference between benign periodic flows and attacking flows.  
In benign periodic flows, the objective is not to overwhelm congested links, unlike attacking flows. Therefore, the network does not experience a high aggregate loss rate, and as a result, AccFlow does not implement its aggressive dropping policy. In situations where a legitimate bursty flow momentarily reaches a peak rate that might cause a high aggregate loss rate, triggering AccFlow to drop a few packets, the adverse impact does not propagate. Legitimate flows naturally adjust by entering retransmission timeouts after encountering packet losses. Consequently, the network becomes less congested, and the aggregate loss rate falls below the threshold \(Th_{1}\).
# TBM Enclosure Rock Grade Prediction Method Based on Multi-Source Feature Fusion
## 3 Case Study  
### Project Overview  
This paper examines a tunnel project located in western Sichuan Province, China. The tunnel follows a northeast-southwest alignment with a total length of approximately 37.965 km and a maximum overburden depth of 1696 m, characteristic of a deep-buried, extra-long tunnel. The presence of substantial deposits of Quaternary alluvial-proluvial cohesive soils and gravelly soils has been identified in the vicinity of the portal areas (i.e., the entrances and exits) and in the adjacent valleys. The bedrock encountered along the primary tunnel alignment is predominantly of the Himalayan granite (\(\gamma_{6}\)) and diorite (\(\delta_{6}\)) categories. The strata cut by the exit section include Meso-Neoproterozoic graisses (\(Pt_{2-3}^{Gn}\)) of the Nyenchen Tanglha Group, a region which is over 90% hard rock. Figure 6 provides a visual representation of the distribution of the primary lithologies along the designated tunnel route. The project entails the construction of two parallel single-track tunnels. The tunnel section under scrutiny in this paper constitutes a segment of the left line  
Figure 5: Schematic diagram of TRNet model architecture.  
tunnel of the project. The statistical distribution of the surrounding rock grades along the tunnel is illustrated in Table 2. The segment from chainage DK1223+195 to DK1226+770 has been selected for detailed investigation in this study. Within this segment, the tunnel passes through slightly weathered granite, with the surrounding rock predominantly classified as Grade III and IV.
## 1 Introduction  
A clavicle hook plate is widely used to treat acromioclavicular dislocation and distal clavicle fracture [1, 2, 3, 4, 5, 6], as its unique hook shape can provide a good stability and partial rotational mobility of the acromioclavicular joint [7]. Although it is a convenient, simple, and effective treatment [5, 6], some complications still occur, such as subacromial osteolysis [8, 9, 10], rotator cuff impingement [11], rotator cuff tear [11], and peri-implant fracture [12, 13, 14, 10, 15]. Furthermore, there is much variation in the anatomy of the acromion between individuals [16, 17, 18, 19]. Nonetheless, biomechanical analysis has revealed many design parameters of the clavicle hook plate [20, 21, 22], and therefore, if an individualized clavicle hook plate designed for the patient's anatomy can be implanted, postoperative complications may be reduced [23, 24, 19, 25].  
A cadaver study indicated that the implantation position of the hook may be incorrect due to the wide range of anatomical acrom structures, possibly leading to subacromial osteolysis or rotator cuff impingement [16]. Another cadaver study indicated that the limited hook depth designs were not sufficient for acrom polymorphisms [17]. The relative position of the acromion and the hook is important, with an incorrectly positioned hook causing complications. At present, there is no clinical research or biomechanical analysis on the effect of the hook being fixed at different acrom positions in the acromio-clavicular joint.  
Finite element analysis (FEA) is commonly used to analyze orthopedic biomechanics, such as the biomechanics of the material and geometry of the clavicle hook plate. A longer plate length or a deeper hook depth can reduce the reaction force on the acrom, reducing the stress on the clavicle [20; 21], whereas a smaller hook angle increases the stress on the clavicle, and a larger hook angle increases the reaction force on the acrom [22]. Among the many clavicle hook plate designs, some of the posterior hook offset designs are different, but they have not been biomechanically evaluated in the acromioclavicular joint.  
There is no relevant mechanical analysis research on the influence of the clavicle hook plate being fixed on the acromioclavicular joint at different acrom positions using various posterior hook offsets. Therefore, this study applied FEA for the biomechanical analysis of clavicle hook plates with different posterior hook offsets implanted at different acrom positions in the acromioclavicular joint and explored the possible causes of clinical subacromial osteolysis and peri-implant fracture. The orthopedic surgeon can select the clavicle hook plate that is suitable for the patient according to the different anatomical structures of the acromioclavicular joint, thereby minimizing surgical complications [10].
## 3 Numerical Studies
### Cantilever Beam  
In this section, a linear cantilever beam structure is used to verify the effectiveness of the proposed PINN. The length of the cantilever beam (L) is 20. The cross-sectional dimensions (b \(\times\) h) are 1.5 \(\times\) 2, and the cross-sectional area (A) is three square units. The beam is discretized into three elements, with each node having two degrees of freedom. Assuming that the cantilever beam is made of a linear elastic material, the density (\(\rho\)) is 50, and the flexural stiffness (\(E\)) is 1 \(\times\) 10\({}^{5}\). The damping matrix is constructed using Rayleigh damping. The left end of the beam is fixed, and an external load is applied at a distance of L/3 from the fixed end. The load (\(F\)) is a function of time (\(t\)) and can be expressed as \(F=-\sin(4\pi\times t)\), as shown in Figure 11(b). The values given in this case are in consistent units. The displacement of each DOF is solved using the proposed PINN method, with the solution from the Newmark-\(\beta\) method used as a reference solution.  
The form of the motion equation for the structure is consistent with that in Section 3.2. The loss function is defined as follows:  
\[Loss=Loss_{1}+\lambda_{1}\sum_{i=1}^{6}Loss_{u}{}^{(i)}+\lambda_{2}\sum_{i=1}^ {6}Loss_{v}{}^{(i)} \tag{23}\]  
In this example, the PINN is used for analyzing the displacement response of a structure. The neural network is employed as a function solver to compute the solutions for the motion differential equations. The network uses the Sobol sequence method to generate 3000 samples trained using the Adam algorithm with batch sizes of 300 and 80,000 epochs. The hyperparameters of the neural network are obtained by minimizing the loss function that incorporates physical information. In this case, the weight coefficients \(\lambda_{1}\) and \(\lambda_{2}\) of the loss function terms are both set to 100. The input layer has a size of 1, and the output layer represents the displacement response of the six DOFs. The activation function used is Tanh. The network is configured with 10 hidden layers, each containing 20 neurons. Figure 13 displays the convergence process of the loss function, with a final loss value of 1.0528 \(\times\) 10\({}^{-4}\). Figure 14 presents the prediction results of the PINN. The solid line represents the reference solution, while the dashed line represents the predicted results. The predicted results closely match the reference solution, indicating that the PINN exhibits excellent predictive performance.  
Figure 12: Numerical example of a cantilever beam.  
Figure 13: Loss function.
# A Comprehensive Review of Multimodal Analysis in Education
## 3 Methodologies in Multimodal Analysis
### Data Collection Modalities
#### 3.1.1 Visual Modalities  
Visual data is the most widely used modality, commonly collected through video recordings, facial expression tracking, eye gaze analysis, and posture recognition. Studies frequently employ cameras to capture student behaviours in classrooms [34], analyse facial cues during online sessions [35], or use computer vision systems such as Kinect or OpenFace to extract body posture and expressions [3; 36]. Eye-tracking is also prevalent in identifying attention patterns and engagement levels [37; 38; 39]. For example, some studies used dual eye-tracking (DUET) setups to understand collaboration dynamics [40], while others employed gaze analysis for cognitive assessment [16]. Visual modalities also include skeletal data capture using depth sensors or motion capture systems [41; 42].
# A Novel Protocol Using Captive Portals for FIDO2 Network Authentication +
## 6 Results
### FIDO2 CAP Compatibility and Usability
#### 6.3.1 Compatibility with Operating Systems and Web Browsers  
Applying FIDO2 authentication to a captive portal is a novel approach. For this reason, there are some captive-portal-specific browsers that operating systems launch that do not support the WebAuth API. We tested our prototype solution in the most common operating systems and browsers. Table 1 shows the compatibility validation results. As it can be seen, the system is fully compatible with operating systems using the user-defined web browser, which usually supports WebAuth. There are other operating systems that use a custom web browser with limited functionality to open captive-portal web pages, known as mini-browsers [27], which mostly are not compatible with WebAuth. When there is no compatibility in a captive-portal mini-browser, the developed web interface shows a redirection button to the user, which opens a compatible web browser in Android and iOS (see Figure 10).
## 7 Sensitivity Analysis and Numerical Results
### Instance 2: LFL Ordering Policy  
In the case of LFL ordering policy, the order quantity is calculated in accordance with the moving horizon, i.e., the forecasted sales for three months which occurs after the expiry of the defined lead time. The variable \(LQ_{t}^{i}\) (Equation (17)) represents the order quantity for the LFL model. General assumptions for Instance 2 are the same as for Instance 1. Instance 2 is analysed for two separate cases, as well as Instance 1. Case 1 considers LFL ordering policy with deterministic demand, where the forecasted sale is equal to the realized sale. Case 2 reflects LFL ordering policy with stochastic demand, where the realized sales are the forecasted sales changed randomly in the range \(\pm\)20%. For Case 1, shown in Figure 10, regulators \(Y_{i}^{3}=Y_{t}^{4}=Sales\ forecast=known\), \(t=\overline{1,24}\).  
As mentioned, Case 2 considers LFL ordering policy with stochastic demand. The variable realized sales is described by Equation (8). Except for the values of this variable, elements of the spreadsheet model for Case 2 are the same as for Case 1. Since the dynamic of accumulation for both cases of Instance 2 is pretty similar as for Instance 1, there was no need for graphic representation. For Case 2 of Instance 2, the total number of planned orders and received deliveries is the same as for Case 1 of Instance 2. The numerical results of Case 1 and Case 2, for Instance 2, are presented in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Instance 1:** & **Case 1: Deterministic** & **Case 2:** \\  & **FOQ Ordering Policy** & **Demand** & **Stochastic Demand** \\ \hline
1 & Lead time (month) & 5 & 5 \\
2 & Planned average level of stock (unit) & 63,000.00 & 63,000.00 \\
3 & Security stock (unit) & 25,200.00 & 25,200.00 \\
4 & Max level of stock T = 24 months (unit) & 70,400.00 & 70,472.00 \\
5 & Average level of stock T = 24 months (unit) & 45,595.00 & 45,646.00 \\
6 & Total number of stock outs & 5 & 5 \\
7 & Total number of launched orders & 14 & 14 \\
8 & Total number of received orders & 11 & 11 \\
9 & max (\(J^{1}\)) = (2)–(5) & 17,405.00 & 17,354.00 \\
10 & min (\(J^{2}\)) = (6) & 5 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Numerical results of Case 1 and Case 2 for Instance 1.  
If we compare the numerical results of Case 1 and Case 2 for LFL ordering policy (Table 3), it can be noted that the differences between the values of the objective functions are small, even in Case 2, when the realized sales are randomly changed in the range of \(\pm 20\%\). An insignificant deviation of the objective functions results for Cases 1 and 2 confirms that the model is sensitive and stable.