esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results
### Comparison of Inference Time and Model Complexity  
The complexity of the model is a key consideration, which not only affects the training and inference efficiency of the model, but also is closely related to the computational resource requirements. In Table 4, the number of parameters (Params), floating point operations (Flops) and inference time comparison of the six models are presented. From the dimension of parameter number, the Params of the proposed HPANet is only 26.3 M, which is about 41% less than that of CaraNet and 6.4% less than that of Polyp_PVT. From the perspective of Flops, the floating-point number of HPANet is only 15.4 G, which is far lower than 227.53 G of Unet and 156.3 G of ResUnet. In terms of inference time, HPANet achieves 42 FPS. Although PraNet leads with 46 FPS, HPANet still provides good real-time performance and is faster than U-Net (32 FPS) and ResUnet (35 FPS).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**BMFM** & **BFRM** & **FNRM** & **mIoU** & **mDice** & **Recall** & **Precision** & **F2** \\ \hline \(\times\) & \(\times\) & \(\times\) & 0.8392 & 0.8955 & 0.9061 & 0.9189 & 0.8934 \\ \(\surd\) & \(\times\) & \(\times\) & 0.8455 & 0.9004 & 0.9023 & 0.9276 & 0.8961 \\ \(\surd\) & \(\surd\) & \(\times\) & 0.8623 & 0.9129 & 0.9207 & **0.9292** & 0.9127 \\ \(\surd\) & \(\surd\) & \(\surd\) & **0.8655** & **0.9204** & **0.9323** & 0.927 & **0.9251** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation study results of different modules. The best results have highlighted in bold.  
Figure 6: Segmentation result of the fusion of different modules.
A Broadband Millimeter-Wave 5G Low Noise Amplifier Design in 22 nm Fully Depleted Silicon-on-Insulator (FD-SOI) CMOS  
[PERSON]  
1 Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA;  
[EMAIL_ADDRESS] (L.-W.O.); [EMAIL_ADDRESS] (J.C.M.); [EMAIL_ADDRESS] (C.S.); [EMAIL_ADDRESS] (J.L.)  
[PERSON]  
1 Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA;  
[EMAIL_ADDRESS] (L.-W.O.); [EMAIL_ADDRESS] (J.C.M.); [EMAIL_ADDRESS] (C.S.); [EMAIL_ADDRESS] (J.L.)  
[PERSON]  
1 Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA;  
[EMAIL_ADDRESS] (L.-W.O.); [EMAIL_ADDRESS] (J.C.M.); [EMAIL_ADDRESS] (C.S.); [EMAIL_ADDRESS] (J.L.)  
[PERSON]  
1 Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA;  
[EMAIL_ADDRESS] (L.-W.O.); [EMAIL_ADDRESS] (J.C.M.); [EMAIL_ADDRESS] (C.S.); [EMAIL_ADDRESS] (J.L.)  
[PERSON]  
1 Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA;  
[EMAIL_ADDRESS] (L.-W.O.); [EMAIL_ADDRESS] (J.C.M.); [EMAIL_ADDRESS] (C.S.); [EMAIL_ADDRESS] (J.L.)  
Footnote 1: email: {jerry.moisefigure.com, j.l.j.  
bandwidth and low \(NF\) that can cover the key FR2 bands. The targeted \(S_{21}\) 3-dB BW for this LNA design is 24 GHz to 42 GHz to cover the key 5G FR2 bands, and the lowest \(NF\) should be less than 3 dB in the targeted BW as in the literature [1; 2; 3; 4; 5; 6; 7]. As we aim to design mm-Wave phased-array receiver systems in the future, a relaxed linearity specification of _IIP3_ better than \(-15\) dBm was targeted [5]. With these specifications in mind, we designed an mm-Wave two-stage broadband LNA where we devised a gain compensation technique in frequency, in addition to incorporating resistive feedback in the first stage of the LNA to improve input matching and gain flatness.  
The paper is organized as follows. Section 2 introduces the LNA design strategies, describing the device selection process as well as the core design methodology. Section 3 presents the PEX (post-layout parasitic extraction) simulated results using an EM solver vs. the traditional lump-element PEX simulations with several parasitic extraction options: R (i.e., \(R\), resistance only), R + C (i.e., \(RC\), resistance, capacitance to ground), and R + C + CC (i.e., \(RCC\), resistance, capacitance to ground, and coupled capacitance between any two metal traces) using Mentor/Siemens Calibre xACT. Finally, we summarize our design performance against the state-of-the-art broadband mm-Wave and Ka-band LNAs in the literature, and Section 4 concludes the paper.
## 2 DPS Modeling of LEO-SIN
### System Model of LEO-SIN  
According to the design of LEO-SIN from the NTN project, LEO-SIN is composited by satellites, User Equipment (UE), Earth Station (ES), and channels as Figure 2 shows. In our assumptions, satellites would be the nodes with transparent forwarding, on-board signal regeneration, radio network accessing or switching, and routing [20; 21]. Those functions of satellite nodes can be dynamically reconstructed. ESs would be the nodes supporting UE access and scheduling and managing all radio resources. UEs would be the nodes with data requests and receptions. The links between different nodes are also very important. There are two types of links, a Uses' Link (UL) and Feeder Link (FL). ULs are the links between satellites and UEs.  
The forward link consists of the uplink of FL and downlink of UL. The reverse link consists of the uplink of UL and downlink of FL. The channels are the degradation of signals from the environment. In this paper, we focus on the downlink of the forward link and its DFO by satellite high-speed moving and the DPS affected by multi-path time spread from refracted and reflected by obstacles.
# Optimizing Agricultural Data Analysis Techniques through AI-Powered Decision-Making Processes
## 1 Introduction
### Challenges in Smart Farming  
Despite its potential benefits, smart farming is still facing several challenges [5], including (i) high costs incurred by the investment in the technology, since the initial investment in the equipment and software is significant, making it difficult for small-scale farmers to adopt; (ii) complexity of data management, caused by the vast amounts of data generated that can be difficult to collect, store, and analyze; moreover, farmers need to have the skills to manage this data effectively; (iii) cybersecurity risks, where potential adversaries can initiate cyberattacks on vulnerable systems, causing the disruption of operations or even compromising the safety of food products; and (iv) lack of standardization, which can make it difficult for.
## Data and Methods
### Wetland Probability Mapping Algorithm  
Wetland probability maps were created using a logistic regression model that combines indices derived from the surface reflectance data that are sensitive to surface greenness and water content. The model was trained on the MODIS MCD12Q1 IGBP land cover classes over a three-year window (2010-2012). All indices were derived from the MODIS MCD43B4 NBAR surface reflectance data. The indices and logistic regression model are described here. Identification of flooded areas by way of building indices from MODIS reflectance data has been used previously [32; 53]; however, here we build off of the methodology developed by [32] and evaluate the effectiveness of individual indices before building the regression model.
## 3 Preliminaries
### Time Constraint  
To prevent the energy of nodes from falling below their minimum capacity \(e_{min,sn}\) (i.e., node will not work if its energy below \(e_{min,sn}\)), nodes are required to be charged before the residual energy falls below this threshold. When charging requests are sent, the residual lifetime \(L_{s_{i}}\) of \(s_{i}\) is:  
\[L_{s_{i}}=\frac{\phi_{sn}-e_{\min,sn}}{r_{s_{i}}} \tag{3}\]  
where \(\phi_{sn}\) is the energy threshold of sensors, \(e_{\min,sn}\) of each sensor is the same initial value.  
Let \(s_{o(k)}\) denote the node index of the \(k\)th charged node in charging scheduling, while \(s_{o(0)}\) denotes the base station. The moving tour of the vehicle is [base station = \(s_{o(0)}\), \(s_{o(1)}\), \(s_{o(2)}\), \(\ldots\), \(s_{o(m)}\), \(s_{o(m+1)}\), \(\ldots\), \(s_{o(0)}\)]. If node \(s_{i}\) is charged by the vehicle, the arrival time \(\tau_{arrive}^{s_{i}}\) of the vehicle is given by:  
\[\tau_{arrive}^{s_{i}}=\sum_{k=1}^{m-1}\frac{e_{\max,sn}-\phi_{sn}-\tau_{arrive}^ {s_{o(k)}}}{r_{c}}r_{o(k)}+\sum_{k=0}^{m-1}\frac{dist(s_{o(k)},s_{o(k+1)})}{v _{c}} \tag{4}\]where \(r_{c}\) is the charging speed of vehicle or drone, \(v_{c}\) is the travelling speed of vehicle, \(dist(s_{i},s_{j})\) represents the Euclidean distance of \(s_{i}\) and \(s_{j}\).  
If \(s_{i}\) is charged by a drone, the arrival time \(\tau_{arrive}^{s_{i}}\) of the vehicle is denoted by:  
\[\tau_{arrive}^{s_{i}}=\sum_{k=1}^{m-1}\frac{e_{\max\_sn}-\phi_{\mathit{sn}}- \tau_{arrive}^{s_{o(k)}}r_{o(k)}}{r_{c}}+\sum_{k=0}^{m-1}\frac{dist(s_{o(k-1)}, s_{o(k)})}{v_{c}}+\frac{dist(s_{o(m-1)},s_{i})}{v_{d}} \tag{5}\]  
If \(\tau_{arrive}^{s_{i}}\leq L_{s_{i}}\), node \(s_{i}\) will be charged within the designated time.
# A Study of OWA Operators Learned in Convolutional Neural Networks
## 6 Results and Discussion
### Comparison to Theoretical Families of OWA Operators  
In Figure 7, we show some examples of the operators learned in the third experiment. The raw learned operators are shown filtered to remove noise with a moving average of window size 5. The examples include four examples of parameterized OWA operators of equivalent orness, namely the Binomial OWA, the exponential RIM OWA, and two variants of the Stancu OWA [22] with different _alpha_ values (\(\alpha=0.15\) and \(\alpha=0.3\)).  
We can observe how the simpler examples (Features 1 and 64 in the figure) are monotonic and behave very similarly to the Stancu OWA operator and the exponential RIM operator. In the case of Feature 1, with orness 0.22, the learned operator is best approximated by the Stancu OWA with \(\alpha=0.3\), while for the Feature 64, with orness 0.85, the best approximation would be the Stancu OWA with \(\alpha=0.15\). In any case, they can be properly generalized by the Stancu OWA. In both cases, the exponential RIM operator seems to give a very good approximation without the need of any parameter adjustment (it only varies according to orness).  
For the other cases, we have chosen a few examples that are harder to generalize. In the case of Features 15 and 54, we can observe how they exhibit a bi-modal weight distribution, a case that cannot be directly modeled with any of the chosen parameterized families of OWA operators. The most extreme examples, Features 5 and 12, show a similar problem, giving most weight to the lowest and highest sorted inputs simultaneously, while giving almost no weight to the intermediate inputs. This is the opposite of a median-like operator, an unusual behavior for the classical OWA operators. We can observe how none  
Figure 7: OWA operator examples.  
of the families can approximate these distributions, especially in the case of the Feature 12, where the best approach is the simple mean represented by the RIM operator.  
From these results, we can conclude that the theoretical families of OWA operators currently studied are coherent with many of the data-driven OWA operators learned with OWA layers, but not with all of them. Some of the behaviors shown are currently hard or impossible to model and parameterize with these families, showing room for improvement in terms of expressivity in parameterized OWA families.
# Inferring TLB Configuration with Performance Tools
## 2 Materials and Methods
### Materials and Instrumentation
#### 2.2.1 Intel Xeon and i9  
The truth data for the Intel processors were obtained by leveraging the getconf Linux tool. By using the getconf command with the PAGESIZE argument, we can reveal the system's memory page size. This knowledge serves two purposes. First, along with the additional commands discussed next, it helps us match the proper TLB configuration (configurations vary based on PAGESIZE), and second, it informs us how to appropriately adjust the stride size in our memory writing scripts. This ensures that our data collection is optimized for the system's memory architecture.  
Furthermore, we utilize other Linux commands, such as cpuid and cpuls, to gather detailed CPU information. While these commands do not directly provide detailed information about the TLB--such as the L1 DTLB and ITLB mapping functions or the L2 STLB mapping functions--they do offer insights into the TLB configuration based on page size. The absence of specific information about the mapping functions poses a significant challenge in this research. The correct mapping function is largely derived from the findings in [14, 15], requiring numerous iterations and adjustments to work with our systems under test. This information is crucial for hypothesizing TLB setups and adjusting our analysis accordingly.  
One question needs to be answered before proceeding with this study: Given that cpuid and cpuls provide potential TLB configurations, is it necessary to reverse engineer it? The answer is yes for three reasons. First, using these commands requires superuser access, which is not always available to an attacker. Second, experimental results have shown discrepancies with the specifications as indicated by [15] and by this study. Third, as mentioned above, mapping functions are not open source, so reverse engineering the TLB configuration is necessary to enable accurate probing and inference of these mapping functions.  
Based on the data gathered, we have identified several TLB configurations for both the Intel Xeon and Intel Core i9 CPUs, with variations depending on the page size. Specifically, the configuration for the Xeon with a 4 K page size is detailed in Table 1. Similarly, for the Intel Core i9, we have determined multiple TLB configurations based on the page size, with the configuration for a 4 KB page size presented in Table 2.  
These configurations are inferred based on the available data and the system's responses to our Linux command queries. While they provide a strong basis for our analysis, they are not definitive specifications as the reverse engineering effort will show later. Nevertheless, these inferred configurations allow us to conduct a targeted and relevant analysis of the TLB behavior in these CPUs.
## 3 A Comprehensive Monitoring System for Landslides in 'Space-Sky-Ground-Underground'
### Ground-Based Monitoring  
Ground-based monitoring mainly focuses on the surface of landslides. This method primarily uses measuring robots, 3D laser scanners, digital cameras, and other instruments to conduct real-time monitoring of landmarks and observation stations on landslide surfaces in an unmanned manner around the clock. Furthermore, the status of and variation in external environmental parameters, such as the influence of environmental factors and meteorological data on the landslide's surface deformation and slope surface, can be obtained [38; 39]. The landslide surface's deformation status and evolution process are grasped accurately, which provides crucial shallow deformation data support for landslide monitoring and early warning. Figure 13 shows the instruments and features commonly used in foundation monitoring.  
Based on 3D laser scanning technology, the entire landslide can be scanned and analyzed, enabling precise inspection of the characteristics of any specific point. [PERSON] et al. [40] explored the application of 3D laser scanning technology in landslide monitoring. Through analyzing multiple landslide monitoring cases, it can be seen that this technology has great advantages in the case of landslides with a high risk of sliding and less vegetation coverage (Figure 14). By analyzing the 3D monitoring data of the two periods before and after the landslide occurrence, the boundary range and volume of the landslide can be  
Figure 12: Establishment of a 3D landslide model based on UAV technology [37].  
Figure 13: Technologies and instruments related to ground monitoring.  
determined. It was verified that this technology has the advantage of accurately grasping the state of the landslide while ensuring the personal safety of the investigators.
# MM-ConvBERT-LMS: Detecting Malicious Web Pages via Multi-Modal Learning and Pre-Trained Model
## 4 Experiment and Analysis
### Ablation Experiment  
To verify the effectiveness of each component of the MM-ConvBERT-LMS model, ablation experiments were performed while maintaining the same experimental dataset and hyperparameters. The full MM-ConvBERT-LMS model was used as the baseline and the specific effects of various components on the experimental results were analyzed. (1) In the model comparison experiments, we observed a significant impact of mode-type encoding on the ALBERT model and hypothesized that it might also affect the performance of the MM-ConvBERT-LMS model. To test this hypothesis, we constructed a comparison model without modal-type encoding and evaluated its performance. Moreover, we investigated the ability of the model to handle modal-missing data (single-modal data) before and after removing the modal-type encoding. It is worth noting that, in contrast to the experiments reported in Tables 3 and 4, the model used in this evaluation was trained on a multimodal dataset, rather than on single-modal datasets. (2) We conducted an ablation test by replacing the LMS of the classification layer with a standard softmax in the ablation test to further validate the effectiveness of the design. (3) In this paper, we have selected a single-stream ConvBERT as the backbone model, as we believe that it can effectively learn representations and modal fusion. In the field of multi-modal, two-stream neural networks are also a popular architecture that involves using multiple backbones to process data from different modalities. To further investigate the impact of architectures on detection tasks. To verify the impact of the model structure, we constructed and evaluated the performance of a two-stream model, which consists of two parallel ConvBERTs. One ConvBERT is responsible for learning the features of HTML tag sequences, while the other focuses on learning the features of URL strings. (4) The coarse-grained modal matching task is the core of our design to help the model learn cross-modal relationships. We analyzed the contribution of the auxiliary module to the model's performance in detecting malicious web pages by removing the coarse-grained modal matching task. The experimental results are shown in Table 7. According to the experimental results, it can be seen that the removal of modal-type encoding, LMS, and coarse-grained modal matching auxiliary tasks all lead to a decrease in the effectiveness of the model. These findings provide evidence for the effectiveness of these components in detecting malicious web pages. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **Version** & **Layers** & **Heads** & **Hidden Size** & **Parameters (M)** \\ \hline \multirow{3}{*}{ConvBERT} & Small & 12 & 4 & 128 & 13 \\  & Medium-Small & 12 & 8 & 384 & 17 \\  & Base & 12 & 12 & 768 & 106 \\ \hline \multirow{3}{*}{BERT} & Base & 12 & 12 & 768 & 109 \\  & Large & 24 & 16 & 1024 & 335 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Information on ConvBERT and BERT models of different sizes.
## 4 Methodology
### Sentiment Analysis
#### 4.3.3 Sentiment Categorization  
We used two sentiment analysis tools, TextBlob and VADER, to get the sentiment of the tweets. Sentiment analysis provides the polarity of the text. It also classifies text into positive and negative classes. Classification task can be performed by using different algorithms. Sentiment analysis methods can be machine learning-based or lexicon-based. In machine learning methods, a labeled dataset is required where the polarity/class of the text is already known. Lexicon is a collection of the predefined words where a polarity score is associated with each word. It is the easiest approach for sentiment classification. This classifier makes use of a lexicon and performs word matching which is used to categorize a sentence. In our work, both TextBlob and VADER use this lexicon-based approach.  
In TextBlob, the sentiment category of each tweet is based on the polarity score of the text. For VADER, the classification is done based on the compound score.  
We also generated the word clouds to visualize the important words based on the frequency of the words initially. However, this failed to provide useful information. Thus, we later used log-likelihood ratio [58; 59; 60] to generate the word clouds. We used Equation (1) to calculate the log-likelihood ratio for each sentiment category (positive, negative, and neutral). Here, \(w_{i}\) denotes word, \(x\) denotes one of the sentiment categories, \(\tilde{x}\) denotes rest of the sentiment categories.  
\[q=log\frac{P(w_{i}|x)}{P(w_{i}|\tilde{x})} \tag{1}\]
# Machine Learning-Driven Calibration of Traffic Models Based on a Real-Time Video Analysis
## 3 Methodology for Analyzing Video Stream Data
### Algorithm for Extracting Traffic Data from a Video Stream  
The data extraction algorithm involves three main steps:  
* Camera calibration and conversion of image coordinates to GNSS coordinates;
* Tracking and calculation of spatiotemporal characteristics of vehicles;
* Applying the Kalman filter to smooth the extracted values.  
Initially, the YOLOv5 real-time video analytics system [105] was used to analyze the video stream, detecting and tracking the movements of visible vehicles. The YOLO algorithm divides the original image into a grid of N \(\times\) N cells. If the object's center falls within the coordinates of a cell, then this cell is considered responsible for determining the object's location parameters. Each object is defined by five values: center coordinates of the bounding box, its width, height, and confidence level that the box contains the object. For each object class-cell pair, the probability of the cell containing an object of that class must be determined.  
The choice of YOLOv5 architecture was made for its high image processing speed and good object detection accuracy with minimal parameters and low learning rate. Training parameters included an IoU threshold value of 0.5, image compression to 960 \(\times\) 960 px, learning rate of 0.001, and SGD optimization algorithm. The training set comprised 3000 images, with 500 in the validation set. An example of a frame from a traffic surveillance camera that was used to collect traffic data prior to image alignment is shown in Figure 5.  
To accurately detect vehicles using YOLOv5 and analyze their parameters, it was essential to address the issue of image distortions caused by various factors and establish a method to convert image coordinates to GNSS coordinates. Figure 4 shows an example of a distorted image.  
To calibrate the camera images and map their coordinates to the GNSS coordinate system, the direct linear transformation method [106] was employed, along with the pose from orthographic and scaling with iterations (POSIT) method [107] to calculate the position and orientation of vehicles. The necessary input data for these algorithms included details about the dimensions of the modeled area, reference points with known coordinates in both systems, and information about the camera's optical configuration.  
The specific section of the route that was simulated represented an intersection measuring 29.4 m in width and 24.4 m in length. Reference points were established in the camera coordinates to align with points in the intersection coordinate system: (0, 244), (0, 122), (0, 0), (147, 0), (294, 0), (294, 122), (294, 244), (147, 244).  
Frame marking was conducted after correcting distortions using the Nelder-Mead method implemented through the Python SciPy library in conjunction with the Python OpenCV library. Figure 6 illustrates this process.  
Figure 5: Original image with distortion.  
Figure 6: YOLOv5 video stream frame marking.