esa-sceva/satcom-chunk-collection huggingface dataset:
# Survey Solutions for 3D Acquisition and Representation of Artificial and Natural Caves
## 4 Discussion
### Sfm  
The use of a photo camera is, of course, the cheapest and most straightforward approach as reported in Table 8. In chapter three, we presented the developed methodology to acquire a sequence of images in a cave that can have sufficient overlap to assure the correct use of SfM algorithms. The analysis of results obtained in the tunnel test site pointed out that the structure from motion's accuracy can be very high and not far from the PLS. The use of an image sequence has a double advantage: it is easy to obtain and it can be adopted not only for the reconstruction of the morphology of the caves but also for the rendering of most precious elements. In the Gazzano case study, we presented two different modalities of use: (i) the acquisition of a photo sequence characterized by the minimum required overlap adopted for the reconstruction of the geometry of the cave; (ii) an ultra-detailed acquisition of a photo sequence of a particular element of the cave (the stalagmite) for the reconstruction of a solid image. These different approaches allow obtaining results with distinct characteristics and final use. The use of a photo camera has another significant added value: its high compatibility with the actual approach adopted by many speleologists for caves survey. As presented initially, the adopted method is based on the reconstruction of a sequence of measures done with a disto-clinometer that creates a polygonal. The series of points of the polygonal allows the description of the cave using a discrete approach. The main limitation of this approach is related to the use of punctual measures that cannot continuously describe the cave's morphology. The SfM can close this gap: the points acquired by the disto-clinometer can be used as ground control points that are fundamental both for a correct reconstruction of the geometry of the cave with a photo sequence and to scale the model, because, unlike the results obtained with active sensors, the photogrammetric models are not scaled. The impact of the method is minimal. The only sign of the survey is a sequence of small dots (few millimeters) representing the polygonal nodes. That can be identified in the high-resolution images acquired by the camera.  
From the point of view of the final product density, the photogrammetric point clouds are comparable with those made with TLS technology, although a bit noisier. The noise effect may be due to some inaccuracy in the images' alignment or to the instability of passive sensors, especially those at low cost. In the case of underground caves, these problems are related to the environments' poor lighting or the light variation, with reflective surfaces and shadows, which do not allow to identify the matching points between the images.
## 2 Theoretical Background and Mathematical Models  
### Direct LEMP Drop  
Figure 1 represents the direct discharge of LEMPs onto any part of an electrical network. In most cases, due to high overvoltages in ETLs compared to the insulation level, deterioration occurs in the insulations even though the return current is small. Therefore, during direct strikes, overvoltages on lines can cause damage or the breakage of insulation materials [18].
## 5 Experiments and Analysis  
### Training Details  
The experiments in this paper were primarily conducted on a Linux server equipped with 8 NVIDIA Titan X GPUs. The server's memory size was 64 GB, and the deep learning framework used was PyTorch (1.13.1). The dataset of Argoverse 2 is large, with a compressed file size of nearly 50 GB. Therefore, training on this huge dataset took a very long time and required good engineering optimizations. To optimize the model training speed, the approach of multi-process distributed training was chosen to fully utilize the multiple GPU resources of the server. The benchmark model PECNet itself does not support multi-process distributed training, so the entire experiment was refactored using torch.distributed for this purpose. Torch.distributed is a set of distributed training tools provided by PyTorch. It can open multiple processes on the same machine. Each process can specify the GPU to be used during training, thereby achieving the effect of accelerating training with multiple GPUs.  
Large datasets pose a significant challenge to hardware devices, especially in terms of memory. It is very easy to encounter out-of-memory (OOM) errors and terminate the training program. By default, each process created by torch.distributed will read the entire dataset into memory, which leads to serious duplicate reading issues. To address this problem, the dataset is divided equally based on the number of processes in advance, and the reading logic of each process is modified to only read the corresponding subset of data. This approach ensures that no matter how many new processes are started, only one complete dataset is read.  
To visually demonstrate the difference in training speed before and after optimization, a set of comparative experiments was conducted. The first experiment utilized one NVIDIA Titan X GPU without enabling multi-GPU training, while the second experiment employed all eight NVIDIA Titan X GPUs of the server and synchronized training with eight processes. Both experiments were conducted on the exact same dataset. Experimental results are shown in Table 1. From Table 1, we can see that the code runs more than 14 times faster when using the optimized method.  
The model used the optimal hyperparameter combinations in all subsequent experiments. The learning rate was set to 0.0005. The number of features (fdim) was 64. The number of latent space features (zdim) was 64. The number of map features (mapdim) was 128. The number of heads in the multi-head self-attention mechanism was eight. The batch size was 512. The number of generated candidate endpoints was 20, and the training epoch was set to 650.
###### Abstract  
Defects on rotor blade leading edges of wind turbines can lead to premature laminar-turbulent transitions, whereby the turbulent boundary layer flow forms turbulence wedges. The increased area of turbulent flow around the blade is of interest here, as it can have a negative effect on the energy production of the wind turbine. Infrared thermography is an established method to visualize the transition from laminar to turbulent flow, but the contrast-to-noise ratio (CNR) of the turbulence wedges is often too low to allow a reliable wedge detection with the existing image processing techniques. To facilitate a reliable detection, a model-based algorithm is presented that uses prior knowledge about the wedge-like shape of the premature flow transition. A verification of the algorithm with simulated thermograms and a validation with measured thermograms of a rotor blade from an operating wind turbine are performed. As a result, the proposed algorithm is able to detect turbulence wedges and to determine their area down to a CNR of 2. For turbulence wedges in a recorded thermogram on a wind turbine with CNR as low as 0.2, at least 80% of the area of the turbulence wedges is detected. Thus, the model-based algorithm is proven to be a powerful tool for the detection of turbulence wedges in thermograms of rotor blades of in-service wind turbines and for determining the resulting areas of the additional turbulent flow regions with a low measurement error.  
image processing; pattern recognition; wind energy turbines; turbulence wedges +
Footnote †: journal: Applied Sciences
## 5 Results
We have considered 50 users located at different positions that are unknown. Figure 9 shows an improvement in the rate for all these 50 users. This work focused on developing algorithms for configuring IRS. To benchmark the methods used for IRS-aided wireless communications, the key methods included classical signal estimation techniques and evolutionary algorithms, which efficiently handled the combinatorial nature of the problem. We also considered frequency-selective fading and discrete state configurations (+90\({}^{\circ}\)/\(-\)90\({}^{\circ}\) phase shifts), and accounted for hardware imperfections like unbalanced reflection amplitudes and mutual coupling effects.
## 2 Theoretical Solutions
### Derivation of Analytical Solutions  
The governing equation for 2-D EIT problems is as follows:  
\[0=\sigma\frac{\partial^{2}u}{\partial x^{2}}+\sigma\frac{\partial^{2}u}{\partial y ^{2}}\Rightarrow 0=\frac{\partial^{2}u}{\partial x^{2}}+\frac{\partial^{2}u}{ \partial y^{2}} \tag{8}\]  
where, \(u\) is the electric potential. The boundary conditions for the Shunt model are as follows:  
\[\begin{array}{ll}\int\limits_{c_{l}}\sigma\frac{\partial u}{\partial n}dS=I_ {l},&\quad(\text{x},\text{ y})\ \in\ \text{e}_{l}\\ \frac{\partial u}{\partial n}=0,&\quad(\text{x},\text{ y})\ \in\ \partial\Omega\text{ and }\
ot\in\ \text{e}_{l}\\ u=U_{l}&\quad(\text{x},\text{ y})\in e_{l}\end{array} \tag{9}\]  
where, \(I_{l}\) is the current injected into the \(l\)th electrode, \(\sigma\) is the electrical conductivity of concrete, \(e_{l}\) is the location of the \(l\)th electrode, \(n\) is a normal vector, and \(S\) is the boundary area of domain \(\Omega\). The following equation can be obtained from governing Equation (10):  
\[\begin{array}{l}0=u_{xx}+u_{yy}=X^{\prime\prime}(x)Y(y)+X(x)Y^{\prime\prime} (y)\\ X^{\prime\prime}(x)Y(y)=-X(x)Y^{\prime\prime}(y)\end{array} \tag{10}\]  
\(X(x)\) is an independent equation for the \(x\) variable in the partial derivative, and \(Y(y)\) is an independent expression for the \(y\) variable. By using variable separation, you can obtain general solutions for \(X(x)\) and \(Y(y)\) as follows.  
\[\begin{array}{l}X(x)=A\cdot\cosh(\lambda x)+B\cdot\sinh(\lambda x)\\ Y(y)=C\cdot\cos(\lambda y)+D\cdot\sin(\lambda y)\end{array} \tag{11}\]  
where, \(A,B,C,D\) and \(\lambda\) are the constant values of the general solution of the partial derivative.
## 2 System Reliability Evaluation Methods
### System Reliability Evaluation Method Based on the Modified AHP
#### 2.2.6 System Reliability  
The superstructure of the prefabricated RC hollow slab bridge is composed of several slab segments. The performance and damage condition of each slab segment will have different effects on the performance of the superstructure system. In order to evaluate the system reliability more reasonably, the influence of the reliability of each slab segment on the system reliability is considered comprehensively by means of the idea of the AHP method. Then, the system reliability can be expressed as a weighted summation of the reliability of each slab segment, as shown in Equation (13):  
\[\beta_{S}=\beta\cdot\mathbf{W}=\beta_{1}\omega_{1}+\beta_{2}\omega_{2}\cdots\beta _{n}\omega_{n}. \tag{13}\]
# The Necessity of LED to Ambient Light Ratio Optimization for Vehicular Optical Camera Communication
## 4 Simulation  
### Simulation Procedure  
The simulations were conducted using MATLAB version 2017b. Figure 10 describes the entire simulation procedure. In the optimization phase, a set of 100,000 LED panel sample frames was generated. These frames were replicated with randomly varying values of LED luminance, ambient light illuminance, and LED density, within given ranges. Subsequently, the frames were processed to identify the LED features, and the Bvar and Wvar were calculated. The optimal L2A ratios, subjected to different levels of LED density, were then obtained. In the testing phase, two sets of LED panel frames were generated. The first set contains frames replicated with random LED luminance values. The second set contained frames replicated with optimal LED luminance values corresponding to the given ambient light illuminance and LED density. Both sets of frames were then processed to identify the LED features and detect the transmitted bits. The resulting BER from either set was calculated to verify the effectiveness of the result of the optimization process.
# The Fast Discrete Tchebichef Transform Algorithms for Short-Length Input Sequences
## 3 Reduced Complexity DTT Algorithms for Short-Length Input Sequences
### Algorithm for 6-Point DTT
We have diagonal matrix \(\mathbf{D}_{16}=diag(1,1,4,1,4,2,4,4,1,2,2,4,2,1,1,1)\) at the third layer of calculation and matrices \(\mathbf{W}_{14\times 16}\) and \(\mathbf{W}_{6\times 14}\) at the fourth and fifth layers of calculation:  
Figure 4: The data flow graph of the six-point 1D DTT algorithm. \[\mathbf{W}_{14\times 16}=\left[ \begin{array}{ccccccccccccc}1&&&&&&&&\\ &1&&&&&&&&\\ &1&1&&&&\\ &&1&1&&&&\\ &&&1&1&1&&&&\\ &&&&1&1&&&&\\ &&&&1&&&&\\ &&&&1&&&&\\ &&&&2&&&&\\ &&&&1&&1&&1\\ &&&&1&&1&&\\ &&&&1&&1&&\\ &&&&1&&1&&\\ &&&&1&&1&\\ &&&&1&&1\end{array} \right], \tag{30}\]  
\[\mathbf{W}_{6\times 14}=\left[ \begin{array}{ccccccccc}1&1&&&&&&&&1&&\\ &1&&-1&&-1&&\\ &&1&&1&&1&&\\ &1&&1&&1&\\ &&1&&1&&\\ &&1&&1&&1&\\ &&&1&&-1&&-1\\ &&1&&-1&&-1\\ &&1&&-1&&-1\end{array} \right]. \tag{31}\]  
Next, we take into consideration the permutation matrices \(\mathbf{P}_{6}^{(1)}\) and \(\mathbf{P}_{6}^{(0)}\) and matrices \(\mathbf{W}_{6}\), \(\mathbf{W}_{16\times 6}\), \(\mathbf{W}_{14\times 16}\), \(\mathbf{W}_{6\times 14}\) at different computing layers of the data flow graph from Figure 4 to obtain factorization of the 6-point 1D DTT matrix:  
\[\mathbf{Y}_{6\times 1}=diag(\mathbf{s}_{6\times 1})\mathbf{P}_{6}^{(1)}\mathbf{W }_{6\times 14}\mathbf{W}_{14\times 16}\mathbf{D}_{16}\mathbf{W}_{16\times 6} \mathbf{W}_{6}\mathbf{P}_{6}^{(0)}\mathbf{X}_{6\times 1}. \tag{32}\]  
Let us consider the data flow graph of the developed 6-point 1D DTT algorithm (Figure 4). It should be noted that the initial 6-point real DTT requires 36 real-valued multiplications and 30 additions. If we apply the developed 6-point 1D DTT algorithm, the number of real-valued multiplications is reduced to 6. The number of additions is decreased from 30 to 27, and 15 shifts are required.
# Recent Advances of the BIRALET System about Space Debris Detection
###### Abstract
Previous measurement sessions reported too few detections to be considered a solid benchmark for future campaigns. The measurements discussed herein were taken in a kind of _beam-parking_ mode: the objects were part of a predetermined list provided by the Italian Air Force. This compromise was necessary, as the transmitter is located in a military facility. Unfortunately, this also means that we had no control of the pool of observable objects. For every object in the list, the SRT was pointed at the right coordinates (predicted by means of two-line element sets--TLE [15]), waiting for the passage of the object inside the beam. During the campaign, performed between December 2018 and October 2019, a total of 27 objects were correctly detected, with a RCS variation in the range of 0.13-13.4 m\({}^{2}\) and a slant range (SR--intended as the sum of the ranges of the object from the receiver and the transmitter) variation between 918 and 2077 km. The results show also that the estimated Doppler shift was predicted with a maximum error of about 1 kHz.
# Inter-Annual Variability of Peatland Vegetation Captured Using Phenocam- and UAV Imagery
## 1 Introduction  
### Vegetation Drives Seasonal Carbon Dynamics  
Terrestrial ecosystems and their vegetation are key components of the global carbon cycle and an important control on its dynamics [1]. Indeed, the seasonal greening of vegetation in the northern hemisphere and its photosynthesis are large enough to be observed in global atmospheric carbon-dioxide (CO\({}_{2}\)) concentrations [2, 3]. This plant phenology describes the timing of lifecycle events, including spring green-up, flowering, and autumn senescence [4]. These events are regulated by internal biological mechanisms and tightly coupled to a range of environmental drivers, which makes them sensitive to change [5]. Global shifts in phenology have been observed since the mid-20 th Century in response to climate change [6, 7, 8, 9, 10], with earlier spring growth and later senescence [11, 12] leading to a longer growing season [8, 13, 14]. That said, predicted future increases in the frequency and severity of extreme weather events (e.g., drought) [15] may limit the length of the vegetation period [16, 17]. In either case, future phenological shifts are expected to have large implications for the global carbon cycle [5, 18, 19].
## 2 Background
### Upper Confidence Bound Algorithm  
In this subsection, we calculate the upper bounds for the family of UCB algorithms, which is a delicate matter. These bounds are typically derived using a statistical theory called concentration of measure. In this theory, exponential tail inequalities play a central role in analyzing the tail of the distributions and, thus, finding tight upper bounds. Choosing among the variants of UCB revolves around the defined bounds and their tightness [17]. The basic UCB algorithm relies on the concept of subgaussian random variables. For a \(\sigma\)-subgaussian random variable, the tail decay is approximately as fast as that of a Gaussian with the same variance.  
**Definition 1**.: _A random variable, \(X\), is said to be \(\sigma-\)subgaussian if it holds that_  
\[E[\exp(\Lambda X)]\leq\exp(\lambda^{2}\sigma^{2}/2) \tag{1}\]  
In this paper, the delivery of the packets is the reward, and different transmission powers are the arms. Given that PDR is always bounded to [0, 1], one possible upper bound can be derived using the following bound:  
**Lemma 1**.: _If a random variable, \(X\), has a mean zero and \(X\in[a,b]\), then \(X\) is \((b-a)/2\)-subgaussian._  
Another slightly tighter bound can be obtained using _Hoeffding's inequality_, which states that \(X\) is \((b-a)^{2}/4\)-subgaussian for a zero-mean [a,b]-bounded random variable, \(X\). (For proof, see the Appendix A.)  
When it is known that the underlying distribution of rewards is \(\sigma-\)subgaussian, the relation between the probability of violating and the bound is as follows [17]:  
\[P[X(t)\geq\bar{X}_{t}(a)+\sqrt{\frac{2\sigma^{2}\log(1/\delta)}{N_{t}(a)}}]<\delta \tag{2}\]  
Assuming some values for \(delta\) (usually \(\delta=1/t^{2}\)), we can calculate the UCB:  
\[UCB_{i}(t,\delta)=\begin{cases}\infty&\text{if }N_{i}(t-1)=0\\ \bar{X}_{t}(a)+\sqrt{\frac{2\sigma^{2}\log(1/\delta)}{N_{t}(a)}}&\text{ otherwise}\end{cases} \tag{3}\]  
Building upon this upper bound, the UCB algorithm runs a simple loop which for iteration (\(t\)) consists of the following operations:  
1. Choose the Action(t) = \(\arg\max\)\(UCB(t-1)\)
2. Observe \(X_{t}\) and update the bounds  
Assume \(\Delta_{i}\) is the suboptimality gap or the immediate regret for action \(i\), and \(N_{i}(t)\) is the number of times arm \(a\) has been pulled before time \(t\). Then, regret, \(R_{n}\), can be formulated as follows:\[R_{n}=\sum_{a\in A}\Delta_{i}E[N_{a}(t)] \tag{4}\]  
The theorem below presents an analysis of the regret when using the UCB algorithm. In the rest of this paper, by UCB algorithm, we mean UCB(\(\delta\)). For a regret analysis of the discounted version of UCB, please refer to [18].  
**Theorem 1**: _Consider the UCB algorithm in a stochastic 1-subgaussian k-armed bandit problem. For any time horizon, \(t\), when accepting an error probability of \(\delta\), then the regret, \(R_{n}\) (defined in Equation (4)), is bounded by the following:_  
\[R_{n}\leq 3\sum_{i=1}^{k}\Delta_{i}+\sum_{i:\Delta_{i}>0}\frac{16\log n}{ \Delta_{i}}\]  
This theorem ensures that the regret growth of UCB is sub-linear relative to time.
# LIMUNet: A Lightweight Neural Network for Human Activity Recognition Using Smartwatches
## 3 Materials and Methods
### LIMUNet Algorithm Design
#### 3.2.3 Lightweight IMU Feature Extraction Method Based on Dual Attention Mechanism
Generally, an IMU includes three sensors, capable of collecting nine sets of data, which are three sets of accelerometer data, three sets of gyroscope data, and three sets of magnetometer data. The nine sets of sensor data are independent of each other, thus requiring specific methods to find the correlation information between different sensors, while not compromising the independence of the sensor data. In previous user behavior recognition schemes, these nine sets of sensor data were directly combined into a large tensor and then directly input into the classification module after feature extraction [31]. In LIMUNet, the convolutional neural networks used for feature extraction employ one-dimensional convolution kernels, meaning they only perform convolution on the time dimension of the data, while the different sensor data are in a parallel relationship during the feature extraction phase. The data from the accelerometer, gyroscope, and magnetometer do not belong to the same modality, so processing these types of sensor data in the same way is inefficient. The data collected by the magnetometer is affected by ferromagnetic interference, the accelerometer is influenced by gravity in addition to human behavior, and the gyroscope may experience drift issues; these  
Figure 4: Channel attention mechanism. situations can all reduce the reliability of the data collected by the sensors [47]. Among the various categories of behavior, actions like walking generally only require the participation of the accelerometer, and data from other modalities do not enhance the recognition of this behavior; instead, the presence of noise introduces uncertainty. In order to address this issue, this paper proposes a multi-sensor feature extraction method based on the attention mechanism, as shown in Figure 5. This method enhances or suppresses the feature maps of each sensor's data using the attention mechanism, thereby improving the capability of feature extraction for IMU data. The design of the dual attention mechanism is of great significance for the independent feature extraction enhancement at the channel level and sensor level of the neural network model. Compared to methods that independently adopt a single attention mechanism, the dual attention module enhances the weight control of the feature map from one-dimensional to two-dimensional, thereby significantly improving feature extraction capability. Due to the computational process of the dual attention mechanism primarily involving fully connected layers and feature map weighting, both of which operate on the already compressed one-dimensional feature map, the impact on computational complexity and parameter count is minimal, ensuring that the model retains its lightweight characteristics.
## 5 Methodology
### Feature Extraction
#### 5.1.1 Image Features
In this way, significant improvements in terms of both accuracy and efficiency are achieved by utilizing the innovative structural elements of ViT, while maintaining the strengths of traditional CNNs. ConvNeXt has different versions, such as Tiny, Small, Base, and Large. The Base version was used in this study. In the feature extraction process, global average pooling was applied to the final convolutional outputs of each CNN architecture. A fully connected layer with 128 ReLU-activated units was subsequently added to reduce the feature dimensionality. Each fish surface image was thus represented by a 128-dimensional feature vector. This uniform dimensionality across different models ensured compatibility during the multimodal fusion phase.