esa-sceva/satcom-chunk-collection huggingface dataset:
# Designing Parallel Adaptive Laplacian Smoothing for Improving Tetrahedral Mesh Quality on the GPU
## 3 Parallel Adaptive Laplacian Mesh Smoothing on the GPU
### Step 1: Searching for First-Order Neighbors of Nodes  
The new smoothing position of a node depends entirely on its neighboring nodes. For a tetrahedral mesh, a single tetrahedron is a basic unit, and each node of the tetrahedron is a neighbor of the other three nodes. All neighbors form the first-order domain of the vertex. The center of mass of the first-order domain is taken as the new nodal location during the iteration. The general method of obtaining neighbors is to allocate an array for each node to store its neighboring coordinates, and then to find the neighbors of that node by traversing each neighboring tetrahedron in turn [36]. There are two major disadvantages of this approach. First, the length of the array of neighboring nodes cannot be determined at the time of initial creation, and it should only be opened up gradually at a later stage of storage, which will lead to data conflicts during writing operations. Second, the work of finding neighbors is carried out in the order of nodes; thus, it is impossible to determine all the neighbors of nodes at the same time.  
Based on this approach, we have proposed a new search method in our previous research [36], where the data traversal unit is changed from a node to each side of a triangle. The specific implementation procedures are as follows.  
1. Search for the sideline segments of each triangle in the tetrahedral mesh by multi-threading, and the corresponding values of the start node and the end node of each edge are stored in two integer arrays;
2. Copy two integer arrays where store all sideline segments in reverse;
3. Sort by the value of the first integer array that stores the starting node of all the sideline segments in parallel;
4. Remove duplicate integer pairs with unique operations in parallel and perform segmented scans to quickly find the index of all neighboring nodes of each vertex.  
It is clear that neighbors of all the nodes can be found simultaneously through the above method, and the number of neighboring nodes can also be determined in advance. In the proposed method, there is no complex data structure, and only an integer array is needed; therefore, it is easy to execute efficiently on a GPU. Figure 5 briefly illustrates the process of searching for neighbors.  
Figure 4: Workflow of the proposed parallel adaptive Laplacian mesh smoothing.
# A Practical Group Symbol and Group Name Approach (PSNA) according to EN ISO 14688-2:2018
## 3 Practical Group Symbol and Group Name Approach (PSNA)
### PSAA for Organic Material  
In the present study, particle sizes \(\leq\) 2 mm soils with organic constituents were also classified. For this purpose, EN ISO 14688-2:2018 [21] was considered. The organic material was divided into four groups (Table 5) according to EN ISO 14688-2:2018. Organic material was classified according to the percentage of organic material contained within fine particles. When soil mass contains a fine content of 5% or more, which also includes organic material, the plasticity chart is utilized. In this case, the group symbol was modified by adding the letter \"O\" at the end (e.g., CIHO-High Plasticity Clay (Organic)).  
If the particle size is bigger than 2 mm, it is crucial to consider the type of organic matter, the genetic origin, and the degree of decomposition of the organic constituents of organic material [21].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Term** & **Organic Content \% of Dry Mass** \\ \hline Low-organic & 2 to 6 \\ Medium-organic & 6 to 20 \\ High-organic & \textgreater{}20 \\ Peat/gyttia/dy/humus & - \\ \hline \hline \end{tabular}
\end{table}
Table 5: Soils with particle sizes \(\leq\) 2 mm with organic constituents [21].
# Non-Uniform-Illumination Image Enhancement Algorithm Based on Retinex Theory
## 2 Proposed Method
### Improved Adaptive Region Correction
#### 2.4.1 Low-Light Area Enhancement  
Based on the classification depicted in the flow chart in Figure 5, the pixel gray value should be spread over a larger range in the histogram for areas of low brightness in the  
Figure 4: Histogram corresponding to block areas: (**a**) the dark area of the image; (**b**) the bright area of the image.  
Figure 5: Flow chart of region-based enhancement algorithm in this section.  
image (\(\mu<0.5\)). Therefore, the details in the bright areas can be acquired by increasing the brightness appropriately.  
The gamma correction expression is as follows:  
\[V_{out}=cV_{in}^{\gamma} \tag{11}\]  
where \(V_{out}\) denotes the intensity of the output image, \(V_{in}\) denotes the intensity of the input image, and the overall brightness of the image is adjusted by using the gamma index number \(\gamma\) and the variable parameter \(c\).  
To increase the brightness of such low-light areas, the value of \(\gamma\) is used to adjust the image brightness. Based on the principle that the lower the value of \(\mu\), the lower the brightness of the image, an enhancement method is designed to use the mean value of \(\mu\) to assess the image brightness level for regional blocks, and a variance \(\sigma\) is introduced into the calculation of gamma factor \(\gamma\) to adjust the enhanced regional brightness. First, the normalized value of \(\mu\) is utilized to classify the areas after division. Then, the area in which \(\mu<0.5\) is enhanced according to the method in Section 2.4.1. The normalization function \(G(\mu)\) is defined as:  
\[G(\mu)=\left\{\begin{array}{ll}0,&\quad\mu\geq 0.5\\ 1,&\quad\mu<0.5\end{array}\right. \tag{12}\]  
After experiments, the parameter \(c\) in this paper is set as:  
\[c=G(\mu) \tag{13}\]  
\[\gamma=\exp\frac{\mu-\sigma}{\mu} \tag{14}\]  
By Equations (11)-(14), the final transformation function is obtained as:  
\[V_{out}=V_{in}^{\exp\frac{\mu-\sigma}{\mu}} \tag{15}\]  
The method described above yields a notably favorable enhancement effect for the low-light areas.
# Automatic Translation between Mixtec to Spanish Languages Using Neural Networks
## 4 Methodology
### Phase 1: Collection of Parallel Texts  
Based on the review of the construction of parallel corpora in low-resource languages [31; 35; 36], we performed corpora search, digitization, and manual correction. Given the low-resource nature of the problem, we implemented techniques to increase the corpora by generating synthetic data with back-translation. The following section describes the search for parallel corpora, generation of synthetic data in Spanish with back-translation, generation of synthetic data with GPT3, digitization, and manual correction.
# Improving Real-Time Position Estimation Using Correlated Noise Models +
## 2 Data Analysis  
The data on which we construct and test our methods were recorded by a single-frequency GlobalSat BU-353S4 unit operating on the L1 frequency, 1575.42 MHz [17]. We used this GPS receiver, fixed in position at one location in Dunedin, New Zealand, to record seven time-series sampled every second over time scales between approximately one and 25 days between the 6 August and the 24 November 2014. The same receiver recorded four additional time-series while fixed in position in three locations in Australia: North Stradbroke Island, Port Douglas and Thursday Island (see Figure 1). These additional time-series were also sampled every second and had time scales between approximately one and three days. The variety of locations and recording dates allowed us to check for possible geographical and temporal variation in the GPS signal, e.g., due to the varying position of GPS constellations. Additional data were taken at the same times and locations using tag-style GPS units, which also used the L1 frequency. This additional data had the same noise characteristics as the original data [8], showing that the noise properties, which we analyse below, are not dependent on the particulars of the GPS receiver, but rather attributed to environmental effects. The results in this paper are therefore expected to be applicable to any single-frequency GPS device operating on the L1 frequency.
# A Platform of Unmanned Surface Vehicle Swarms for Real Time Monitoring in Aquaculture Environments
###### Abstract  
The Internet of Things (IoT) is a rapidly evolving technology that is changing almost every business, and aquaculture is no exception. In this work we present an integrated IoT platform for the acquisition of environmental data and the monitoring of aquaculture environments, supported by a real-time communication and processing network. The complete monitoring platform consists of environmental sensors equipped in a swarm of mobile Unmanned Surface Vehicles (USVs) and Buoys, capable of collecting aquatic and outside information, and sending it to a central station where it will be stored and processed. The sensing platform, formed by the USVs and Buoys, are equipped with multi-communication technology: IEEE 802.11n (Wi-Fi) and Bluetooth for short range communication, for mission delegation and the transmission of data collection, and LoRa for periodic report. On the back-end side, supported by FIWARE technology, an interactive web-based platform can be used to define sensing missions and for data visualization. Results on the sensing platform lifetime, mission control and delay processing time are presented to assess the performance of the aquatic monitoring system.  
a +
Footnote †: journal: Sensors  
_Article_  
A Platform of Unmanned Surface Vehicle Swarms for Real Time Monitoring in Aquaculture Environments
# A Versatile 100 Hz Laser System with Few-Cycle and TeraWatt Pulses for Applications
## 2 Design Considerations  
The major aim was to design a versatile system providing ultrashort pulses for time-resolved ultrafast spectroscopic research and high peak power applications such as target development, laser-generated X-ray source, and nonlinear optical studies. Due to the high demand, the system should be quickly and conveniently switchable between these two modes. One of the standard methods for few-cycle pulse generation is to spectrally broaden the mJ energy short pulses in a hollow core fiber and subsequently compress them [15]. Hence, the front end of the system called for a design which provides high efficiency and reliability. That is, the pulses with a mJ level need to be stretched only to a few tens of picoseconds.  
High peak power laser pulses are expected to exhibit a sufficiently high temporal contrast [8]. As is widely known, regenerative amplifiers are efficient but leaking pulses as the finite extinction ratio of the polarizers limits the achievable temporal contrast [5]. Hence, only multipass amplifiers can be used to avoid the need for utilizing more complicated temporal cleaning methods like plasma mirrors.  
A natural feature of broadband, high gain, saturated amplification is that the amplification bandwidth is substantially narrowed, and hence the amplified pulse spectrum supports a pulse duration of around 50 fs. One way to overcome this limitation is changing the sign of chirp of the pulses between the amplifier units [13]. For example, the first unit amplifies the negatively stretched pulses, where the temporally leading spectral parts, the \"blue\" components of the seed pulses, are amplified more than the trailing edge (\"red\" components), making the spectrum of the amplified pulse shift to \"blue\". Before the next amplifier stage(s) have a similar gain, the sign of chirp is reversed, so that the \"red\" components would be amplified more than the trailing \"blue\" ones. The resulting amplified pulse spectrum would preserve more of the bandwidths than the pulses with only one sign of temporal stretching.  
Both requirements can be simultaneously satisfied with the following scheme (Figure 1). Let us stretch the oscillator pulses negatively in a transmission grating stretcher to about 40 ps, then make a multipass front end amplifier where an acousto-optical dispersive programmable filter (AOPDF) is embedded. Towards the few-cycle branch, the pulses are compressed in bulk and with chirped mirrors, providing a high throughput. In the TW branch, the pulses go through a grating stretcher, providing the necessary large amount of group delay dispersion to reverse the sign of the chirp and lengthen the pulse duration to hundreds of ps. The two further multipass amplifiers provide the pulses with the necessary energy.  
Figure 1: Schematics of the laser system, displaying the measured pulse energies and pulse durations after each major sub-system.
# Multi-Objective Optimization for EE-SE Tradeoff in Space-Air-Ground Internet of Things Networks
###### Abstract  
The Internet of Things (IoT) has become increasingly popular, and its communication requirements have grown beyond what traditional ground networks can handle. The space-air-ground (SAG) integrated network has been proposed as a potential solution, where unmanned aerial vehicles (UAVs) collect data from IoT devices and transmit them to satellites. However, the limited energy of UAVs is one of the key factors restricting communication performance, so it is necessary to consider communication energy efficiency (EE). In addition, the improvement of EE will bring about the decline of spectral efficiency (SE). Therefore we consider the tradeoff between EE and SE. Considering a SAG-IoT network, the focus of the paper is to optimize sub-channel selection, power control, and UAV position deployment to maximize EE and SE of the network, which is a multi-objective optimization (MOO) problem. On this premise, we try to improve the data throughput between IoT devices and UAVs. To solve this MOO problem, we use the \(\epsilon\)-constraint to convert it into a single-objective optimization problem. We then employ various optimization algorithms such as the Dinkelbach algorithm, successive convex approximation algorithm, Lagrange dual algorithm, and block coordinate descent algorithm to solve the mixed integer non-convex problem. Simulation results show that the proposed algorithm converges to at least one sub-optimal solution.  
Internet of Things (IoT); space-air-ground integrated network (SAGIN); energy efficiency; spectrum efficiency; multi-objective optimization (MOO); resource allocation +
Footnote †: journal: _electronics_  
Article
# Engineering a Combinatorial Laplacian Solver: Lessons Learned +
## 4 Evaluation  
### Settings  
#### 4.1.1 Software, Hardware and Data  
We implemented the KOSZ solver in C++ using NetworkKit [34], a tool suite focused on large-scale network analysis. Our code is publicly available [35]. As compiler we use g++ 4.8.3. The benchmark platform is a dual-socket server with two eight-core Intel Xeon E5-2680 at 2.7 GHz each and 256 GB RAM. We present a representative subset of our experiments, in which we compare our KOSZ implementation to existing linear solvers as implemented by the libraries Eigen 3.2.2 [36] and Paralution 0.7.0 [37], both libraries with fast sparse matrix solvers.  
We mainly use two graph classes for our tests: (i) rectangular \(k\times l\) grids given by \(\mathbb{G}_{k,l}:=\left([k]\times[l],\left\{\left(x_{1},y_{1}\right),\left(x_{ 2},y_{2}\right)\right\}\subseteq\binom{V}{2}:|x_{1}-x_{2}|=1\vee|y_{1}-y_{2}|=1\right\}\)); Laplacian systems on grids are, for example, crucial for solving boundary value problems on rectangular domains; note that \(\mathbb{G}_{k,l}\) is very uniform, i.e., most of its nodes have degree 4; (ii) Barabasi-Albert random graphs with parameter \(k\)[38]. These random graphs are parametrized with a so-called attachment \(k\). They are constructed by starting with \(K_{k}\) and iteratively adding \((n-k)\) nodes. We connect a new node to \(k\) random existing nodes where each existing node is weighted by its current degree, i.e., nodes are preferentially attached to nodes that already have a high degree. We denote the distribution of Barabasi-Albert random graphs with \(n\) nodes and attachment \(k\) by Barabasi\((n,k)\). Their construction models that the degree distribution in many natural graphs is not uniform at all.  
For both classes of graphs, we consider both unweighted and weighted variants (uniform random weights in \([1,8]\)). We also did informal tests on 3D grids and also real-world graphs, in particular complex networks. These graphs did not exhibit significantly different behavior than the two graph classes above and are therefore omitted from the presentation of the results.
# A PreTI: A Thermal Image Dataset for Face and Nose Segmentation with Apes
## 3 Proposed Methods
### Face Detection  
In order to detect the face from the thermal images, we use the cascade HRNet model [31] pretrained on a COCO object detection task and modified to only recognize a chimpanzee's faces from a one-channel input: the temperature. We use the same training procedure as in [31] except that the input temperature image is not normalized. We use usual random transformations, including rotation (\(\pm 10^{\circ}\)), translation (ratio of 0.1), shear (max degree of 2), scaling (from 0.5 to 1.5), and flip (with probability 0.5) on the fly to augment our data. The model is fine-tuned for 50 epochs using stochastic gradient descent, a learning rate of \(10^{-5}\), a weight decay of \(10^{-4}\), and a momentum of 0.9 with 500 warm-up iterations. We annotate this version of HRNet \"Tifa\" (Thermal Image Face Ape) model.  
Since the face should be the warmest area in an image, segmentation based on thresholding the thermal region is also an option for face detection. Therefore, we implement a nonsupervised threshold method, annotated \"Thresh\", for finding the largest area with a temperature above a certain threshold. As depicted in Figure 3, the method is sensitive to the threshold parameter, and a search for the best parameter is necessary in order to obtain the best segmentation. This method is used as a baseline comparison, but can also be combined with other approaches for improving performance.  
Figure 2: Landmark computation pipeline: face detection followed by a CNN encoding the position of the landmarks in its final layer’s channels. The landmark model features of the first and last convolutional layers are depicted.
## 2 Numerical Modeling
### Verification of the Numerical Model
#### 2.5.2 Ribbed Pipe Verification  
In the case of a ribbed tube, the results were verified by comparison with the experimental results of [PERSON] et al. [21], in which a tube of rectangular grooves was studied. The height, pitch, and width of the groove are 3, 12, and 6 mm, respectively. The inner tube diameter is 36 mm, while the diameter at the groove section is 42 mm. It is important to note that the Re and other parameters are based on the inner diameter of 36 mm. The k-\(\epsilon\) and k-\(\omega\) models were tested as well as Grid 1 and Grid 2.  
Both the average Nusselt number, Nu\({}_{\text{av}}\), and the friction factor, f, were compared at a Prandtl number of 0.71, as shown in Figure 5. For the same numerical model, the results of Grid1 and Grid2 of the current simulation are almost identical. The results for both the numerical models show good agreement with those of [PERSON] et al. [21]. The k-\(\omega\) numerical model shows better agreement at low Re values, while the k-\(\epsilon\) model results in better estimations at higher Re values. Either of the two numerical models can be used. Based on the overall verification, k-\(\epsilon\) was selected for use as the simulation model of the current study.  
Figure 4: Verification of the present study of a smooth tube: (a) Nusselt number at Pr = 0.71 and (b) friction factor.
# A Review of Research on Secure Aggregation for Federated Learning
## 5 Challenges and Future Directions
### Global Model Quality  
In federated learning, each client possesses a varying amount of data. When secure aggregation schemes are applied, the computational or communication overhead increases, and clients may not always train locally within a given time frame, which can consequently degrade the global model's accuracy. To address these challenges, cryptographic secure aggregation requires lightweight encryption schemes tailored for federated learning, as demonstrated in works like [73; 76]. Another method to reduce communication overhead involves using Top-K gradient selection algorithms for compressive screening of data, as shown in [71]. Differential privacy-based secure aggregation schemes, on the other hand, emphasize balancing trade-offs between model quality and performance, as explored in [111].  
Striking an optimal balance between performance and model quality is complex, highlighting the urgent need for further research to effectively integrate different techniques and secure aggregation protocols. Future work should focus on enhancing aggregation algorithms to handle client heterogeneity, improve generalization, and pre-process client data [139; 140]. These improvements aim to enhance the quality of locally trained models and achieve acceptable global model performance.
## 2 System Model  
### OFDM-Based VLC System  
The basic block diagram of the DCO-OFDM system, including the channel estimation, equalization, and symbol de-mapping module, is shown in Figure 1. Assume that an OFDM symbol has \(N\) sub-carriers. First, the bit stream \(\mathbf{t}\) is modulated on the frequency domain symbol vector \(\mathbf{X}=\left[X(0),X(1),\cdots,X(N-1)\right]^{T}\) based on an _M-ary_ quadrature amplitude modulation (M-QAM) constellation, where \(X(0)=0\) is the DC component. Then, Hermitian symmetry is imposed on \(\mathbf{X}\) to form new symbols as follows:  
\[\mathbf{C}=\left[X(0),X(1),\cdots,X(N-1),0,X^{\ast}(N-1),\cdots,X^{\ast}(1) \right]^{T}. \tag{1}\]  
Subsequently, the time domain signal \(\mathbf{x}\) is produced by 2N-point inverse discrete Fourier transform, defined as follows:  
\[x(n)=\frac{1}{\sqrt{2N}}\sum_{k=0}^{2N-1}C(n)e^{\frac{2 mk}{2N}},n=0,1,\cdots,2 N-1. \tag{2}\]In addition, a certain length of CP is inserted at the beginning of \(x(n)\) so as to combat the inter symbol interference (ISI). The PAPR of \(\mathbf{x}\) is defined as follows:  
\[\Upsilon_{d}=\frac{\max_{0\leq n\leq 2N-1}\Bigl{\{}|x(n)|^{2}\Bigr{\}}}{\mathbb{E} \Bigl{\{}|x(n)|^{2}\Bigr{\}}}=\frac{\max_{0\leq n\leq 2N-1}\Bigl{\{}|x(n)|^{2} \Bigr{\}}}{\sigma_{x}^{2}}. \tag{3}\]  
To modulate the power intensity of the LED (Cree(r) CR6-800L), the DC bias \(I_{DC}\) is added to the bipolar OFDM signal and thus shifts the negative signal to positive values. The IM/DD structure is always used to deploy the signal propagation in VLC, where the amplitude of the electrical signal is used to modulate the information and then converted into optical signals. After a free-space propagation, the photodetector (PD) captures the optical intensity and converts them into electrical signals \(y(n)\).