esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid CNN-LSTM Model for SMS Spam Detection in Arabic and English Messages
## 3 The Proposed Model
### Text Pre-Processing
#### 3.2.2 Word Embedding  
Word embedding is a technique that aims to represent words or sentences in a text by real numbers vectors, described in a vector model (or Vector Space Model). Semantically similar words are mapped close to each other in the vector space. Word embedding is very useful for representing words, to be given as input to a Deep learning model. In recent years, it is considered among the best representations of words in NLP. There are several pre-trained word embedding models that are ready to use, such as Word2 Vec [31] and GloVe [32]. However, in this paper, we chose to train our own word embedding model, because our text messages contain mixed words from the Arabic and English language.  
In the proposed approach, the purpose of word embedding is to prepare the textual data for the deep learning model. Figure 2 describes the steps performed to accomplish the word embedding process. When considering that we have an input data containing a collection of N text messages, the first step in the process is to vectorize the text corpus, by transforming each text into a sequence of integers (each integer being the index of a token in a dictionary). The result is a list of sequences with variable sizes (since the text messages do not have the same length). Subsequently, the \"Pad sequences\" task makes the sentences in uniform length, by padding sentences smaller than \"MAX_SEQ_LENGTH\" with empty values and truncating the sentences longer than \"MAX_SEQ_LENGTH\". At the end of this process, the text data are ready to train with the word embedding layer and give them as input to the deep learning model.
# Tree-Structured Regression Model Using a Projection Pursuit Approach
## 2 Regression Tree and Projection Pursuit  
### General Algorithm of the Regression Tree  
Let \(Y\) be the dependent continuous variable and \(X_{1},\cdots,X_{p}\) be the independent variables. For the whole procedure of the regression tree, several decisions should be made: the best independent variable \(X_{k^{*}}\) among \(X_{1},\cdots,X_{p}\) and the best cut-off \(c^{*}\) in each node, the stopping rule to declare a final node, the pruning method to use to avoid overfitting, and the assignment of numerical values to the final nodes.  
In each node, the regression tree uses one independent variable (for example, \(X_{k^{*}}\)) and separates the data in the specific node into two groups using the cut-off \(c^{*}\). If \(X_{k^{*}}<c^{*}\), it is assigned to the left child node. Otherwise, it is assigned to the right child node. This cutoff is determined by a predetermined rule, usually to minimize the mean squared error (MSE) for the regression tree including CART [2]. This procedure is continued until the node is declared a final node.  
The stopping rule is closely connected to the model complexity. If the number of final nodes is increased, there is a high chance that it will become a complex model, and the risk of overfitting will increase. Most tree-structured methods adapt a pruning method after growing a large tree. CART uses the cost-complexity pruning method with a penalty for model complexity.  
The most common values to assign to the final node are the mean of the \(Y\) values in the final node. Because of the restriction that the one value should be assigned to all observations in the same final node, the tree-structured regression methods usually have poor predictability. To improve the precision of tree-structured regression methods, several methods have suggested using linear combinations of the independent variables instead of one value [8]. In this paper, the projection pursuit method and several different models for the assignment of the final node are adapted.
## 3 Experimental Setup
### Data-to-Image Conversion
#### 3.4.1 Three-Phase RGB Image Construction  
To generate CNN input images for fault diagnosis in a three-phase induction motor, the current data from phases A, B, and C were mapped to the RGB channels to form two-dimensional images. This method involves converting the time-series current values into pixel intensities to create an image matrix.  
Each phase current signal was normalized to a range of [0, 255] using min-max normalization before being used as CNN input pixel values.  
As the normalized data were one-dimensional time-series signals, they were reshaped into a 2D square pixel array for image construction. Specifically, current sequences of a length L = 1024 were sequentially reshaped into a 32 \(\times\) 32 2D array based on \(\sqrt{\mathrm{L}}=\) 32. Grayscale images were generated for each of the A, B, and C phases.  
These grayscale images were then assigned to the RGB channels--Red for phase A, Green for phase B, and Blue for phase C--to construct a single three-channel RGB image.
## 2 System and Channel Models  
A mixed FSO/RF integrated satellite-HAP relaying network is illustrated in Figure 1, where the satellite (S) transmits confidential information to the legitimate terrestrial users (D) through an AF HAP relay (R) in the presence of an eavesdropper attempting to wiretap confidential information. It is assumed that there is no direct link between satellite and the terrestrial users due to masking effects as a result of weather, environmental obstacles, long distance, etc. It is assumed that the FSO link follows Gamma-Gamma fading distribution, while the RF link between the HAP relay and the terrestrial users follows Shadowed-Rician fading distribution. In addition, the eavesdroppers RF link between the E and R is considered to undergo Rician distribution. In addition, the HAP relay in the network employs AF relaying protocol, which will introduce a fixed gain into the receive signal irrespective of the fading amplitude on the FSO link. Thus, the network end-to-end signal-to-noise ratio (SNR) can be defined as [33]:  
\[\gamma_{eq}=\frac{\gamma_{1}\gamma_{2}}{\gamma_{2}+C}, \tag{1}\]  
where \(C\) denotes the fixed relay gain parameter, \(\gamma_{1}=\overline{\gamma}_{1}|h_{1}|^{2}\) represents the instantaneous SNR at the HAP with \(\overline{\gamma}_{1}\), and \(|h_{1}|^{2}\), respectively, signifies the average SNR and channel power gain of satellite-to-HAP relay link, while the \(\gamma_{2}=\overline{\gamma}_{2}h_{2}\) indicates the instantaneous SNR at the destination, with \(\overline{\gamma}_{2}\) and \(h_{2}\) representing the average SNR and channel power gain of HAP-to-terrestrial users link, respectively.  
Figure 1: A mixed FSO/RF integrated satellite-HAP relaying network model.
# RHS-YOLOv8: A Lightweight Underwater Small Object Detection Algorithm Based on Improved YOLOv8
## 4 Analysis and Discussion of Experimental Results
### Validation Experiments on the RUOD Dataset  
We also conducted further training and testing on the RUOD dataset to demonstrate the effectiveness of the proposed model, and the results are shown in Table 4. The results in the more challenging RUOD dataset show that Faster R-CNN, RetinaNet, and YOLOv5 are unable to better balance their computational cost, detection accuracy, and image processing  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Models** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]:0.95** & **FPS** \\ \hline SSD & 0.796 & 0.505 & 22 \\ Faster R-CNN & 0.744 & 0.393 & 8 \\ YOLOv3 & 0.716 & 0.403 & 69 \\ YOLOv4 & 0.767 & 0.439 & 52 \\ YOLOv5s & 0.845 & 0.634 & 45 \\ YOLOv5m & 0.846 & 0.663 & - \\ YOLOv5l & 0.848 & 0.664 & - \\ YOLOv7-tiny & 0.814 & 0.573 & 105 \\ YOLOv8s & 0.85 & 0.661 & 138 \\ YOLOv8m & 0.867 & 0.69 & 127 \\ YOLOv8l & 0.866 & 0.696 & 113 \\ YOLOv10n & 0.819 & 0.617 & 112 \\ AWF-YOLOv8 [45] & 0.778 & 0.701 & 53 \\ FEB-YOLOv8 [46] & 0.829 & 0.632 & 116 \\ CEH-YOLO [47] & 0.884 & 0.712 & 156 \\ Dynamic YOLO [48] & 0.867 & 0.681 & - \\ FishDet-YOLO [49] & 0.888 & 0.694 & 143 \\ RHS-YOLOv8s (ours) & 0.871 & 0.69 & **169** \\ RHS-YOLOv8m (ours) & 0.879 & 0.706 & 152 \\ RHS-YOLOv8l (ours) & **0.888** & **0.726** & 146 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The results of each model on the DUO dataset.  
time. YOLOv7-tiny has low GFLOPs and high FPS, but it has poor P, R, and mAP. In contrast, YOLOv8's original model has fair detection accuracy and FPS, but it pays a higher computational cost.  
Remarkably, the model proposed in this paper maintains a smaller computational cost and faster FPS. Taking RHS-YOLOv8l as an example, it reduces the computational effort by 9.95% (16.4 GFLOPs), improves the [EMAIL_ADDRESS] by 1.60%, and improves the FPS by 10.59%, compared with the original YOLOv8l. It can be seen that the improved model in this paper achieves decent results in both DUO and RUOD, with higher universality and credibility.
## 2 Materials and Methods
### Equipment
#### 2.2.2 Wireless EMG System (Delsys Trigno)  
In this study, the Trigno Wireless Electromyography System (Delsys Inc., Natick, Massachusetts, USA) was used to collect data with a sampling frequency of 2000 Hz. In addition, a Trigno wireless sensor's integrated three-axis accelerometer was used to segment the paddling stages. An EMG system was used on the following muscles on the dominant side of the body: the upper-body muscle group included the pectoralis major, latissimus dorsi, lower trapezius, biceps brachii, triceps brachii, and wrist flexor and extensor; the trunk and lower body muscles included the rectus abdominis, external oblique muscle, erect spinae, multifidus, gluteus maximus, rectus femoris, biceps femoris, tibialis anterior, and gastrocnemius. The EMG sensors were placed along the longitudinal midline of the target muscle in accordance with the recommendations of the Surface Electromyography for the Noninvasive Assessment of Muscles project (SENIAM) [16].
# On Unmanned Aerial Vehicles Light Show Systems: Algorithms, Software and Hardware
## 5 Simulation and Experiment Results
### Simulation
However, the benefit of dynamically solving TAP cannot be ignored, especially in denser and more compact spaces, and for reducing the arriving time of UAVs. Due to the randomness of the initial and target position, the distance between some target points are less than the safe distance. This makes it impossible for the UAVs to arrive at the destination. Moreover, a few UAVs were trapped into extreme point due to the repulsive force of multiple UAVs, which also causes the inability to reach the target position. Therefore, all three methods cannot guarantee that all UAVs arrive at their target positions. To solve this issue, the minimum distance between generated target positions can be configured in the formation transformation to increase the number of UAVs arriving at the target positions. In addition, using Method 2* and Method 3 can reduce the influence of extreme points on the number of arrived UAVs, which is an advantage of our proposed method. In the simulation, conservative attractive and repulsion coefficients are selected in a compact space with a large number of UAVs generated randomly, for finding a balance between sampling time and smooth trajectories. As a result, the UAVs take a relatively long time (about 60s) to arrive at their targets. Nevertheless, the attraction and repulsion coefficients can be tuned according to UAV specifications and the flight space to reduce the arriving time. To further understand how the number of UAVs affects computational time, we set the number of UAVs as a single variable, the average computation time for each iteration is shown in Figure 10. Compared with Method 3, the proposed method has a significant computational cost advantage, and the advantage becomes more obvious as the number of UAVs increases. Next, two case studies are given to better demonstrate the advantages of the proposed method in fight distance and the number of arrived UAVs. The trajectory of using Method 3 is not shown since it is indistinguishable from that of the Method 2*. In these case studies, the attractive gain coefficient is \(\eta=0.8\); the repulsive gain coefficient \(k_{p}\) is 0.3, and the other parameters are kept the same.
## 3 Results
### Acquisition of Terrain and Road Network Information  
This study focuses on the Shanmei Reservoir and its downstream area, utilizing drone-based oblique photogrammetry technology to collect terrain and road network data downstream of the reservoir. Following field surveys, flight zones were defined based on the locations of residential clusters and key facilities in the town and surrounding valleys, covering an area of 3.49 km\({}^{2}\) and a river stretch of 3.4 km. A drone equipped with a multi-lens high-performance camera was used to capture overlapping multi-angle images of the study area, with an image resolution of 1:500. Due to the steep terrain in the surrounding valleys, the forward overlap was set to 80%, and the side overlap was set to 70%. In the flatter downstream town areas, the forward overlap was adjusted to 70%, and the side overlap was adjusted to 60%. The drone flight height was set at 100 m.  
Figure 1: The flow chart of the path planning method based on flood–road network information coupling.  
Through a series of processes, including image data acquisition, data preprocessing, aerial triangulation, and 3D product generation, a high-resolution 3D terrain reconstruction of the Shanmei Reservoir and its downstream area was produced. The resulting digital 3D terrain model is shown in Figure 2.  
Based on the high-resolution terrain model generated through 3D reconstruction, road network information is extracted and integrated with the raw dataset collected by drones. After resizing the images, the main road lines are extracted, and intersections are identified to preliminarily construct the road network topology for the downstream area of the Shanmei Reservoir. To ensure the model's completeness and accuracy, a topological analysis and verification of the road network are conducted. The connections between each node and its neighboring nodes are clarified, and detailed connectivity information is compiled into node and road segment tables. The final road network model for the downstream area of the Shanmei Reservoir, as shown in Figure 3, includes 142 nodes and 81 road segments, ensuring both the clarity of node connectivity and the integrity of the network topology.
# Round-Trip Time Ranging to Wi-Fi Access Points Beats GNSS Localization
###### Abstract  
Wi-Fi round-trip time (RTT) ranging has proven successful in indoor localization. Here, it is shown to be useful outdoors as well--and more accurate than smartphone code-based GNSS when used near buildings with Wi-Fi access points (APs). A Bayesian grid with observation and transition models is used to update a probability distribution of the position of the user equipment (UE). The expected value (or the mode) of this probability distribution provides an estimate of the UE location. Localization of the UE using RTT ranging depends on knowing the locations of the Wi-Fi APs. Determining these positions from floor plans can be time-consuming, particularly when the APs may not be accessible (as is often the case in order to prevent unauthorized access to the network). An alternative is to invert the Bayesian grid method for locating the UE--which uses distance measurements from the UE to several APs with known position. In the inverted method we instead locate the AP using distance measurements from several known positions of the UE. In localization using RTT, at any given time, a decision has to be made as to which APs to range to, given that there is a cost associated with each \"range probe\" and that some APs may not respond. This can be problematic when the APs are not uniformly distributed. Without a suitable ranging strategy, one can enter a dead-end state where there is no response from any of the APs currently being ranged to. This is a particular concern when there are local clusters of APs that may \"capture\" the attention of the RTT app. To avoid this, a strategy is developed here that takes into account distance, signal strength, time since last \"seen\", and the distribution of the directions to APs from the UE--plus a random contribution. We demonstrate the method in a situation where there are no line-of-sight (LOS) connections and where the APs are inaccessible. The localization accuracy achieved exceeds that of the smartphone code-based GNSS.  
outdoor position; outdoor location; round trip time; fine time measurement; RTT; FTM; Beats GNSS Localization. _Appl. Sci._**2024**, _14_, 7805. [[https://doi.org/10.3390/app14177805](https://doi.org/10.3390/app14177805)]([https://doi.org/10.3390/app14177805](https://doi.org/10.3390/app14177805))  
1
## 3 LPR Cell Design  
### LPR Cell Operation  
Figure 10(b) shows the simplified cross-section of the LPR cell whose schematic correspondence is shown in Figure 10(a). A pinned photodiode (PPD) is used for photo detection. The charge sensing node (voltage: V\({}_{\text{CS}}\)) is coupled to the PPD through M\({}_{2}\) and a drain (n\({}^{+}\)) through M\({}_{1}\). The drain terminal is connected to a power supply voltage V\({}_{\text{DD}}\). The gates of M\({}_{1}\) and M\({}_{2}\) are biased to V\({}_{\text{B1}}\) and V\({}_{\text{B2}}\), respectively. The charge sensing node is connected to the gate of transistor M\({}_{3}\) which is used for a source flower readout amplifier. A transistor M\({}_{4}\) is used for the selection of the pixel.  
Figure 11 shows the operation of the LPR cell with potential profiles. The voltage V\({}_{\text{B2}}\) for M\({}_{2}\) is biased such that the pinned diode is always depleted if there is no light. A transistor M\({}_{1}\) is biased to V\({}_{\text{B1}}\) and it is higher than V\({}_{\text{B2}}\). If a light pulse irradiates the photodiode, a photo-generated charge is rapidly transferred to the charge sensing node, and a part of the charge is drained to V\({}_{\text{DD}}\) through M\({}_{1}\). Since M\({}_{1}\) is working at sub-threshold region, the V\({}_{\text{CS}}\) responds to the logarithm of the photo current and the V\({}_{\text{CS}}\) is going down when light is on. When light is off, the excess electron in the floating diffusion is drained to V\({}_{\text{DD}}\), and the voltage is going up. The turning-off response time dominated by diffusion current is much larger than that of the turning-on.  
Figure 10: **(a)** LPR cell **(b)** Cross section of LPR cell.  
Figure 9: Timing diagram of the image signal readout, light source address calculation and communication signal output.
# A Carbon Benefits-Based Signal Control Method in a Connected Environment
## 2 Control Mechanism  
This section provides the rationale for how the features are achieved.  
**Higher ceiling of carbon emissions reduction at signal control intersection:** The carbon emissions reduction is achieved by the coordinated optimization of signal control and vehicle speed guidance. The optimization of signal control aims to minimize vehicle speed fluctuations within intersection areas, as higher speed fluctuations result in increased emissions. The optimization of signal timing will determine the optimal allocation of green time for each phase. The optimization of vehicle speed guidance aims to track the green time phases provided by signal control, designing optimal speed recommendations for vehicles so that the maximum number of vehicles can pass through during the green phase. This approach reduces the frequency of vehicle stops and starts, lowering carbon emissions. This coordinated optimization could significantly enhance carbon emission reduction.  
**Ensured higher compliance rate of vehicles by taking advantage of carbon economic incentives:** To enhance vehicle compliance, this research proposes a CIM integrated with signal control. Before entering the control area, CVs can preview the estimated carbon reward value for the current intersection, which is based on the actual reward amounts distributed to vehicles in the previous period. By demonstrating potential economic benefits, drivers can be encouraged to follow speed guidance. CVs that follow the speed guidance become carbon-benefit connected vehicles (CBCVs). The control center would monitor the driving behavior of CBCVs within the control area to determine their economic rewards. The level of compliance with speed guidance directly influences their carbon rewards, further incentivizing effective adherence to speed guidance.  
**Provided a method for calculating carbon emissions reduction at intersection:** To accurately assess the carbon reduction benefits of CBCVs executing speed guidance strategies, this study proposes a comprehensive evaluation method. The method calculated the emission reduction benefits from two aspects. The first aspect is the direct emission reduction benefit of CBCVs. This assessment is based on the difference in emissions between scenarios where a CBCV receives guidance and scenarios where it does not, quantifying the reduction in emissions as the benefit. The second part evaluates the indirect carbon reduction benefits of CBCVs. This involves proposing a grouping strategy to determine the non-CBCV group influenced by CBCV guidance. The emission difference of this group before and after being influenced by CBCV guidance is calculated as the reduction benefit. All emission calculations are based on a microscopic emission calculation model, ensuring the accuracy of the computations.
## 2 Data and Methods  
### Data  
#### 2.1.1 Species Richness Data  
Considering coverage and taxonomy, we chose to use the species richness dataset from the Instituto de Pesquisas Ecologicas (Brasil) in this study. This dataset (grid format) consists of global total mammal richness, global total bird richness, and global total amphibian richness. In this dataset, the primary species range map data used to create the species richness maps are from the IUCN for mammal and amphibian species, and jointly from BirdLife International and NatureServe for bird species. During the data process, extinct species were removed, as were non-native distributions of extant species. For each grid cell, any species that overlapped any part of the cell were counted as a presence of that species. All species richness data exhibited a spatial resolution of 10 km and used the equal-area projection [3; 49; 50; 59].