esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Analysis
### Experiment Environment  
The training process of STA-NET adopts a transfer learning strategy. The backbone feature extraction network is initialized with weights from Kinetics-400 [36] (a human action dataset). Subsequently, the entire framework is trained. The proposed method, implemented using the PyTorch deep learning framework, The version of PyTorch is 2.1.0, employs AdamW [37] as the optimizer with a weight decay of 0.05. A cosine learning rate scheduler [38] is utilized, setting the base learning rate to 1 \(\times\) 10\({}^{-5}\). The resolutions of in-cabin and external vehicle camera streams are both set to 224 \(\times\) 224. The model is trained for 200 epochs on an NVIDIA RTX 3090 Ti GPU with 24 GB of memory. The Brain4 Cars dataset [3] records video sequences of driving maneuvers. A total of 625 samples are collected, comprising 234 forward samples, 124 left lane change samples, 58 left turn samples, 123 right lane change samples, and 55 right turn samples. Among these, 80% of the video sequences are used for training, and the remaining 20% for testing.
## 2 Materials and Methods
### Euler-Bernoulli Beam Theory  
For a beam under no distributed load, the Euler-Bernoulli beam equation can be written as  
\[\frac{d^{4}w}{dx^{4}}=0 \tag{1}\]  
where \(w\) is the deflection. The solution to this differential equation can be expressed as:  
\[w=c_{0}+c_{1}x+c_{2}x^{2}+c_{3}x^{3} \tag{2}\]  
where constants, \(c_{0}-c_{3}\), are determined by applying boundary conditions. For a cantilever Euler-Bernoulli beam with a point load, \(P\), at its end, we have  
\[\delta=\frac{Fl^{3}}{3E_{s}I}\;\;and\;\;\theta=\frac{Fl^{2}}{2E_{s}I} \tag{3}\]  
On the other hand, for a cantilever beam with a concentrated moment, \(M\), at its end, the displacement and rotation are as follows:  
\[\delta=\frac{Ml^{2}}{2E_{s}I}\;\;and\;\;\theta=\frac{Fl}{E_{s}I} \tag{4}\]  
In beams where the angle of the free end does not change during the deformation (e.g., the beams of the lattice structure considered in this study), the rotations produced by the lateral load, \(F\), and moment, \(M\), must be equal and opposite, from which the value of \(M\) can be identified:  
\[\frac{Fl^{2}}{2E_{s}I}=\frac{Ml}{E_{s}I}\;\;\rightarrow\;\;M=\frac{Fl}{2} \tag{5}\]  
While force, \(F\), tends to increase the deflection, moment, \(M\), tends to reduce it. The total deflection created by force, \(F\), and moment, \(M\), is then  
\[\delta=\frac{Fl^{3}}{3E_{s}I}-\left(\frac{Fl}{2}\right)\frac{l^{2}}{2 EI}=\frac {Fl^{3}}{12 EI} \tag{6}\]  
Figure 1: General deformation of a cantilever beam with axial and lateral displacements as well as flexural and torsional rotations at the free end.  
Rewriting Equation (6) as a function of \(F\) gives (see Figure 1(a))  
\[F=\frac{12E_{s}I}{l^{3}}\,\delta \tag{7}\]  
Figure 2: Cont.  
According to Equation (5), we will have \(M=\frac{6E_{s}I}{I}\ \delta\). Similarly, the axial force required to displace the end of a rod for \(u\) is \(AE_{s}u/l\) (see Figure 2c). The equations for a cantilever beam with rotation but with no displacement in the end can be obtained in a similar manner and the results are shown in Figure 2b.
## 4 Solution Approach
### Dynamic Programming
\[J_{k}^{*}(\mathbf{ST}_{al}^{k})=\min J(\mathbf{ST}_{al}^{k};f_{k},f_{k+1},\ldots,f_{K}) \tag{21}\]  
\[J_{k}^{*}(\mathbf{ST}_{al}^{k})=\min \left[z_{k}+f_{k+1}^{*}(\mathbf{ST}_{al}^{k+1})\right] \tag{22}\]  
Following the principles of dynamic programming, we divide the original problem into multiple sub-stages, where the input state of each sub-stage is influenced by the state and decisions from the preceding one. Within each sub-stage, the small-scale problem can be efficiently addressed with LNS, considering dependencies from the previous time period. This approach allows us to preserve a set of sub-schemes \(\{f_{k}^{1},f_{k}^{2},\ldots\}\) representing unique schedules in \(stage_{k}\). Finally, we combine these sub-schemes to obtain a complete solution. As the impact of disruptions may decrease over time, they can often be resolved during earlier sub-stages. To achieve this, we utilize the Dijkstra algorithm [46] to identify an optimal solution and create a comprehensive rescheduling plan. Algorithm 1 outlines the details of the algorithm, and we have established the following two termination conditions for it:  
(a) The last stage has been reached. (b) The solution for the new stage does not incur additional costs, i.e., it matches the original schedule. ```
Initialize the state \(\mathbf{ST}_{al}^{1}\), the list of stages  Initialize \(C^{Q}\), \(C^{D}_{i,j}\), \(C^{P}\), \(C^{H}\), \(d_{max}\), \(i_{min}^{i\downarrow}\), \(h_{max}\), \(\varepsilon\), \(\gamma\)  Create a list, \(\mathbf{L}_{s}\), to store all possible input states  Begin with \(\mathbf{L}_{s}\) containing the initial state, \(\mathbf{ST}_{al}^{1}\) {\(f_{1}^{*},f_{2}^{*},\ldots,f_{k}^{*},\ldots\)} = the optimal integrated rescheduling scheme Until termination condition is met  Calculate the cost of operation from the initial state to each state in \(\mathbf{L}_{s}\) and sort \(\mathbf{L}_{s}\) based on these costs  Select the first element of \(\mathbf{L}_{s}\) as the current state, denoted as \(\mathbf{ST}_{al}^{k}\), and construct the connection network based on it  Generate multiple sub-schemes {\(f_{k}^{1},f_{2}^{*},\ldots\)} for the current state using Algorithm 2. Calculate new states {\(\mathbf{ST}_{al}^{k+1,1},\mathbf{ST}_{al}^{k+1,2},\ldots\)} according to the transition function provided in Formula (18) for each new state:  Update the new state's corresponding integrated rescheduling schemes {\(f_{1}^{*},f_{2}^{*},\ldots,f_{k}^{*}\)} endfor  Add the newly generated states to \(\mathbf{L}_{s}\) and remove the current state from \(\mathbf{L}_{s}\) return {\(f_{1}^{*},f_{2}^{*},\ldots,f_{k}^{*},\ldots\)} correlated to the final state
```  
**Algorithm 1**DP-LNS
## 6 Conclusions  
We described an algorithm for deep learning-based fully automated detection of TB in chest X-ray images: it is a cascade of DLabv3+X lung segmentation and an ensemble of DCNNs for TB detection and localization. XceptionNet was embedded, as the backbone of DeepLabv3+, to segregate the lung from other anatomies. It surpassed state-of-the-art lung segmentation in chest X-rays; it obtained IoUs of 95.1 \(\pm\) 1.5% for the MC dataset, 92.8 \(\pm\) 4.2% for the SZ dataset, and 96.1 \(\pm\) 0.8% for the JSRT dataset. This improved not only TB diagnosis but also other thorax disease detection in X-ray images. The lung-segmented outputs were fed to an ensemble classifier of DenseNet, ResNet-50, SqueezeNet, and MobileNet. The classifier predicted the existence of TB and localized TB manifestations. Ourcascade algorithm achieved an accuracy of 92.7% for the MC dataset, significantly higher than previous studies, and 95.5% for the SZ dataset, comparable to the state-of-the-art. Finally, the visualization highlighted the areas that contributed most to the classification decision and localized TB lesions. The cascaded algorithm required 4.8 s for lung segmentation, 17.4 s for TB prediction, and the heat maps required an additional 2.9 s (using OS map) and 7.6 s (using Grad-CAM map) for a total of 22.2 to 32.7 s.  
In addition to the improvements mentioned in Section 6, we will enlarge the datasets by augmenting the images using image manipulations or generative adversarial networks to investigate the performance of our algorithm on larger datasets. We will generalize the proposed framework to detect other thoracic diseases such as pneumonia, lung nodules, pleural effusion, COVID-19, etc.  
Conceptualization, N.M. and A.N.; methodology, N.M. and A.N.; software, A.N.; validation, N.M. and A.N.; formal analysis, A.N.; investigation, N.M.; resources, N.M.; data curation, A.N.; writing--original draft, A.N.; writing--review and editing, N.M.; visualization, A.N.; supervision, N.M. and K.H.; project administration, N.M.; funding acquisition, N.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by School of Engineering, King Mongkut's Institute of Technology Ladrakbang.  
Not applicable.  
The data presented in this study are openly available in the National Library of Medicine at [[https://doi.org/10.3978/j.issn.2223-4292.2014.11.20](https://doi.org/10.3978/j.issn.2223-4292.2014.11.20)]([https://doi.org/10.3978/j.issn.2223-4292.2014.11.20](https://doi.org/10.3978/j.issn.2223-4292.2014.11.20)) [21].  
We declare that there are no conflicts of interest.
## 2 Materials and Methods
### Experimental Methods
#### 2.2.3 Mechanical Testing  
The tensile strength, Young's modulus, and elongation at yield and breakpoints were determined. The tensile properties for all samples were measured with a tensile tester (Zwick Z 020 GmbH & Co. KG, Ulm, Germany) according to the British standards (BS) EN ISO 527-1:1996. The tensile tester had a maximum load of 20 kN with a maximum testing range of 1700 mm. The holding grips of the test specimen were attached to the machine so that the test specimen's main axis coincided with the pull direction through the grip assembly centerline. It was ensured that the clamping system did not cause premature fracture at the grips. The distance between the clamps was adjusted to obtain a gauge length of 80 mm. The grip extended with a constant crosshead speed of 50 mm/min along its major longitudinal axis until the specimen fractured. Four tensile test samples were tested at measuring temperatures of 24 \({}^{\circ}\)C, 40 \({}^{\circ}\)C, 60 \({}^{\circ}\)C, and 80 \({}^{\circ}\)C, and the results averaged. The standard deviation was calculated as a measure of statistical uncertainty.  
The impact strengths were measured using a Charpy edgewise un-notched test according to the ISO 179-1:2010(E) standard using 10 bar-shaped unnotched samples with dimensions of \(100\times 10\times 4\) mm at different temperatures of 24 \({}^{\circ}\)C, 40 \({}^{\circ}\)C, 60 \({}^{\circ}\)C, and 80 \({}^{\circ}\)C. The impact testing used an impact tester (PSW 4J, Kennesaw, USA) and a 5.5 J pendulum hammer. A standard deviation of 5% (drop weight) due to air resistance was subtracted in each test to calculate the Charpy impact strength. For each test, values were calculated as the average of 10 independent measurements and a standard deviation calculated as a measure of statistical uncertainty.  
Hardness was measured using a Shore D hardness tester (PCE-DX-DS (D), Southampton, UK) according to the EN ISO 868:2004 standard. The sample pieces of the materials were tested at 24 \({}^{\circ}\)C, 40 \({}^{\circ}\)C, 60 \({}^{\circ}\)C, and 80 \({}^{\circ}\)C by placing them on a solid base inside a controlled hot chamber where the gauge was placed on a spot of the sample at least 12 mm away from the edge. Then the tester was pressed against the surface of the sample, and the durometer reading recorded 1 s after indenter application. The measurements for each specimen were repeated five times at different spots, which were at least 6 mm apart from each other. An average was done and a standard deviation calculated as a statistical uncertainty measure.
# Indoor Vehicles Geolocalization Using LoRaWAN
## 3 A Brief LoRaWAN Overview  
LoRaWAN is a LPWAN (Low Power WAN) technology that stands between mobile (e.g., 3G, LTE) and short-range wireless (e.g., Bluetooth, WiFi or Zigbee) networks. It was designed particularly for IoT devices and applications since they typically need both low frequencies and small sizes of data transfer. LPWANs are used to create proprietary networks to connect IoT devices, such as sensors for monitoring environmental conditions.  
LoRaWAN is an open standard with a certification programme to guarantee interoperability that is governed by the LoRa Alliance. Any company can buy LoRa hardware and deploy its own network without going through and paying fees to a centralised authority. However, the underlying LoRa chip needed to implement a LoRaWAN network is proprietary to a California-based semiconductor manufacturer called Semtech.  
LoRa uses the sub-GHz spectrum (868 MHz in Europe, 915 MHz in the Americas and 433 MHz in Asia). It employs a spread spectrum technique to transmit data on different frequency channels and at different rates so that the gateway can adapt to changing conditions and optimise the way it exchanges data with each device. Working at sub-gigahertz frequencies it is therefore not so constrained from line-of-sight communications and from blockage from walls and other obstacles. This characteristic makes it more interesting since it implies that it requires far fewer \"gateways\" than cellular or other short-range wireless technologies.  
Figure 1 shows the basic elements that constitute a LoRaWAN system, basically the end-devices (e.g., the sensors), the gateways, and the network's servers. Networks server can be private, private with some free options for the users (e.g., LORIOT ([[https://www.loriot.io/](https://www.loriot.io/)]([https://www.loriot.io/](https://www.loriot.io/)))), or public (e.g., The Things Networks ([[https://www.thethingsnetwork.org/](https://www.thethingsnetwork.org/)]([https://www.thethingsnetwork.org/](https://www.thethingsnetwork.org/)))).  
A single gateway can communicate with several hundred thousand devices up to 20 miles away in unobstructed environments. Data rates range from 300 bps to 50 kbps depending on the spreading factor and channel bandwidth, while the maximum message length is 243 bytes and it offers effective two-way  
Figure 1: The architecture of a generic Long Range Wide Area Network (LoRaWAN) system.  
functionality. Each message is received by all the base stations in the range to improve the transmission success ratio.
## 3 Fundamentals
### Direct Current (DC) Resistivity Theory  
The DC resistivity method describes the underground structure through the difference between the rock and soil conductivities. In engineering practice, the apparent resistivity (\(\rho_{S}\)) is often used to describe the comprehensive state of underground uneven materials and the topographic relief. The power supply electrodes A and B transmit DC electrical signals into the ground, the potential difference between any two electrodes M and N is measured and the apparent resistivity of the medium can be obtained [15]. The most frequently used DC resistivity methods include electrical sounding, electrical profiling and high-density resistivity methods. The measurement principle of the DC resistivity method is shown in Figure 2.  
\[\rho_{S}=K\frac{\Delta U_{MN}}{I} \tag{2}\]  
where \(K\) is the device coefficient, \(\Delta U_{MN}\) represents the potential difference between the measuring electrodes \(M\) and \(N\), and \(I\) represents the power supply current.  
\[K=\pi\frac{AM\cdot AN}{MN} \tag{3}\]\[\Delta U_{MN}=\int\limits_{N}^{M}E_{MN}\cdot dl=\int\limits_{N}^{M}j_{MN}\cdot \rho_{MN}dl \tag{4}\]  
where \(E_{MN}\) is the electric field strength between the measuring electrodes \(M\) and \(N\), \(j_{MN}\) is the current density between the measuring electrodes \(M\) and \(N\), \(\rho_{MN}\) is the resistivity between the measuring electrodes \(M\) and \(N\), and \(dl\) is the length unit between the measuring electrodes \(M\) and \(N\).  
The [PERSON] device was symmetrically installed with four poles. The electrode arrangement and measurement method are shown in Figure 3. Considering \(AM=MN=NB=\) a, Equation (3) can be expressed as:  
\[K=2\pi a \tag{5}\]  
By simultaneously solving Equations (2), (4) and (5), the resistivity can be expressed as  
\[\rho_{S}=\frac{K}{I}\int\limits_{N}^{M}E_{MN}\cdot dl=\frac{2\pi a}{I}\int \limits_{N}^{M}j_{MN}\cdot\rho_{MN}dl \tag{6}\]  
Research has shown that there is a negative correlation between the resistivity and water content in rock and soil masses, which can be used to characterize water migration in rock and soil masses through the change in resistivity [21; 22]. The resistivity of the rock and soil mass is positively correlated with the porosity, and the resistivity can thus be employed to characterize the development process of rock and soil cracks [23; 24; 25]. During rainfall, the occurrence form of water in the slope rock and soil mass changes with the change in the water content, and the apparent resistivity constantly varies. Therefore, it is feasible to describe the change within the slope interior during rainfall through the change in the apparent resistivity.  
Figure 3: Wenner configuration and cross-section scanning diagram.  
Figure 2: Measuring principle of DC resistivity method.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Peak** & **RT** & **\% (Liq)** & **\% (HS)** & **\% (SPME)** & **Compound** & **Type** & **RI** \\ \hline  & & & & & 4a,8-dimethyl-2-(prop-1- & & \\
56 & 28.378 & & 0.26 & 0.88 & en-2-yl)-1,2,3,4,4a,5,6,7- & bicyclic sesquiterpene & 1492 \\  & & & & & octahydronaphthalene & & \\ \hline
57 & 28.442 & 1.26 & 0.67 & 2.99 & \(\beta\)-selinene & bicyclic sesquiterpene & 1486 \\ \hline
58 & 28.627 & 1.54 & 0.79 & **3.85** & \(\alpha\)-selinene & bicyclic sesquiterpene & 1494 \\ \hline
59 & 28.835 & **6.89** & **3.31** & **8.51** & \(\alpha\)-bulnesene & bicyclic sesquiterpene & 1505 \\ \hline
60 & 29.012 & & & 0.20 & \(\gamma\)-cadinene & bicyclic sesquiterpene & 1513 \\ \hline
61 & 29.380 & **4.15** & 1.42 & **5.06** & \(\gamma\)-selinene & bicyclic sesquiterpene & 1538 \\ \hline
62 & 29.468 & & & 1.08 & \(\alpha\)-bisabolene & monocyclic sesquiterpene & 1540 \\ \hline
63 & 29.493 & **5.53** & **2.01** & **7.04** & selina-3,7(11)-diene & bicyclic sesquiterpene & 1542 \\ \hline
64 & 29.757 & 0.82 & 0.30 & germacrene B & monocyclic sesquiterpene & 1557 \\ \hline
65 & 30.158 & 0.77 & traces & & **caryophyllene oxide** & bicyclic sesquiterpeneenoid & 1581 \\ \hline
66 & 30.342 & **4.59** & 0.18 & traces & **guaiol** & bicyclic sesquiterpeneenoid & 1596 \\ \hline
67 & 30.438 & & & traces & \(\beta\)-atlantol & monocyclic sesquiterpeneoid & 1607 \\ \hline
68 & 30.454 & 0.35 & traces & & 5-_epi-7-epi-\(\alpha\)-eudesmol_ & bicyclic sesquiterpeneoid & 1616 \\ \hline
69 & 30.663 & **5.02** & 0.19 & 0.19 & 10-_epi-\(\gamma\)-eudesmol_ & bicyclic sesquiterpeneenoid & 1619 \\ \hline
70 & 30.799 & 1.81 & traces & & \(\gamma\)-eudesmol & bicyclic sesquiterpeneoid & 1631 \\ \hline
71 & 30.859 & 0.33 & & & agarospirol & bicyclic sesquiterpeneoid & 1645 \\ \hline
72 & 31.040 & 2.56 & traces & & \(\beta\)-eudesmol & bicyclic sesquiterpeneoid & 1649 \\ \hline
73 & 31.072 & **4.76** & 0.10 & & \(\alpha\)-eudesmol & bicyclic sesquiterpeneoid & 1653 \\ \hline
74 & 31.232 & **4.82** & 0.09 & traces & bulnesol & bicyclic sesquiterpeneoid & 1667 \\ \hline
75 & 31.312 & & & traces & cadalene & bicyclic aromatic hydrocarbon & 1674 \\ \hline
76 & 31.380 & 0.60 & & & \(\alpha\)**-bisabolol** & monocyclic sesquiterpeneoid & 1684 \\ \hline
77 & 31.575 & 0.54 & & juniper camphor & bicyclic sesquiterpeneoid & 1691 \\ \hline  & & 88.10\% & 97.17\% & 88.85\% & & \\ \hline  & & 34 cpd & 57 cpd & 47 cpd & & \\ \hline \end{tabular}
\end{table} 
Table 4: _Cont._ \begin{table}
\begin{tabular}{c c c c c} \hline
**GC/MS** & **LOH LL1** & **LOH LL2** & **LOH LL3** & **LOH LL4** \\ \hline Liquid & 37 & 38 & 45 & 34 \\ \hline HS & 51 & 74 & 55 & 57 \\ \hline SPME & 46 & 57 & 49 & 47 \\ \hline All identified different compounds & 67 & 94 & 84 & 77 \\ \hline \end{tabular}
\end{table} 
Table 5: Number of identified compounds in different samples by three different methods and all identified different compounds in each chemotype from all three methods.
## 1 Introduction
The traditional point cloud super-resolution technology has the problems of heavy computation and poor real-time performance. By building a deep neural network model to learn and predict the point cloud data, a denser point cloud can be restored to a certain extent. In order to better adapt to the scanning line characteristics of mechanical LiDAR, we propose a new mechanical LiDAR point cloud super-resolution model, TSE-UNet. The model combines the U-Net structure and the characteristics of temporal and spatial feature-enhanced modules to optimize the scanning line characteristics of mechanical LiDAR. TSE-UNet can capture the time dynamics of the point cloud effectively and recover the high-density point cloud with spatial feature-enhanced skip connection and upsampling operations. In addition, TSE-UNet uses a lightweight network structure and efficient convolution algorithm to achieve efficient point cloud super-resolution in real time with lightweight edge computing devices. With our model, sparse point clouds captured by low-cost LiDAR (i.e., Robosense-32) can be enhanced in real time with lightweight edge computing devices. In addition, upsampled point clouds can achieve similar results compared to point clouds of high-cost LiDAR (i.e., Robosense-128). Our TSE-UNet model can easily scale sparse point clouds from low-cost LiDAR sensors to dense point clouds with dense laser beams, thereby improving the performance of subsequent point cloud-based perception modules. The contributions of this paper are as follows:  
* To address the shortage of available open-source datasets in the field, we construct and release a simulation dataset, CARLA32-128, for a laser beam LiDAR point cloud super-resolution task. Additionally, a real-world dataset, Ruby32-128, is provided for the 32-to-128 laser beam LiDAR point cloud super-resolution task. * Based on the semantic segmentation model UNet, we propose a temporal and spatial feature-enhanced point cloud super-resolution model called TSE-UNet. It utilizes a temporal feature attention aggregation module and a spatial feature enhancement module to fully leverage point cloud features from neighboring timestamps, improving super-resolution accuracy. * We validate the effectiveness of the proposed method through extensive comparative experiments, ablation experiments, and visual qualitative experiments. The rest of this paper is organized as follows. Section 2 reviews the related works on image super-resolution and point cloud super-resolution. Section 3 presents the detailed design of the TSE-UNet model, and Section 4 evaluates the model on both synthetic data captured in the CARLA simulator and real-world data collected from a local vehicle platform. Section 5 summarizes this work.