esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Challenges in Terahertz-Based FSO Communication
### Manufacturing Challenges in THz Communication
#### 3.6.5 Scalability and Cost Barriers  
The high manufacturing costs associated with THz technology have prevented its widespread adoption in commercial applications. Unlike the use of millimeter-wave technology in 5G networks, which benefits from mass production, THz components require specialized fabrication processes and exotic materials, making them significantly more expensive. Standardizing wafer-scale fabrication techniques for THz systems, similar to the approach used in silicon photonics, are necessary to drive down costs and enable the mass production of these systems [55].
# Scale-Sensitive Attention for Multi-Scale Maritime Vessel Detection Using EO/IR Cameras
## 4 Experiment Implementation  
### Experiment Setup  
In this study, we utilized various public datasets as well as data collected directly. For camera-based vessel surveillance and reconnaissance, public datasets included the VAIS dataset, which consists of 3000 visible and thermal images of various types of vessels (Cargo, Medium, Passenger, Tug, etc.), the Singapore Maritime Dataset (SMD), which contains EO and IR camera footage captured day and night in the Singapore Strait at a resolution of 1920 \(\times\) 1080, the MassMIND dataset, which detects vessels and obstacles in maritime environments under different times and weather conditions, and the Shipsnet dataset, composed of 4000 RGB images with an 80 \(\times\) 80 resolution, divided into vessel and non-vessel classes.  
Figure 3: The attention process in the SA and SSCA modules of ML-MHA. C represents the number of channels.  
Additionally, we used data captured from the VC2050F camera, installed on Yeongjong Island, South Korea, starting in May 2020. This camera has a visible range of 5-7 km and up to 35\(\times\) optical zoom. Data were collected both during the day and at night, with the camera positioned to face the coastline, allowing it to detect vessel movements near the shore. The Computer Vision Annotation Tool (CVAT) and Roboflow Universe annotation tools were used to label the positions of the vessels. The specifications of the camera used for data collection can be found in Table 2 and Figure 4.  
Publicly available datasets classify various types of vessels; however, this study focuses on determining the presence or absence of vessels in surveillance environments. As such, all vessel types were unified into a single category, \"boat\", for consistency. Similarly, data collected directly for this study were also labeled as \"boat\" to maintain uniformity.  
The experiments in this study were conducted in an Ubuntu 22.04 environment equipped with an Intel 14 th Gen i9 CPU and an Nvidia 4090 GPU. PyTorch 2.4.1 was used as the deep learning framework, with the batch size set to 16. The learning rate was flexibly adjusted between 0.01 and 0.0001 based on the specific requirements of the experiments, playing a key role in optimizing model performance and convergence speed.
# Cooperative UAV Scheme for Enhancing Video Transmission and Global Network Energy Efficiency
## 4 Evaluation  
In this section, we present the simulations to demonstrate the efficiency of the VOEI in transmitting video over FANET with satisfactory QoE. Specifically, we present the methodology and metrics applied to evaluate the VOEI for video surveillance applications in a disaster recovery scenario. We evaluated the impact of different UAV speeds on the energy level, as well as QoE.
## 4 Results and Analysis  
### Results of Single Equation Estimation  
This paper first adopts a single equation model to measure the interaction between industrial development and air pollution, and the estimation results can be compared with that of the simultaneous equation model. The regression analysis on panel data generally considers fixed-effect models and random-effect models. Tables 2 and 3 show the single-equation estimation results of the development of secondary and tertiary industries and air pollution, respectively. The results show that PM2.5 concentration has positive and negative effects on the secondary and tertiary industries, respectively. In turn, the proportion of secondary industry and tertiary industry also has a significant impact on PM2.5 concentration.  
It is worth noting that the estimation results of the single equation indicate that there is a clear two-way causal relationship between industrial development and air pollution. Further, this two-way causal relationship makes it impossible to effectively solve the endogenous problem, and ultimately may cause the single equation estimation result to be inaccurate, so it is necessary to measure the relationship between air quality and industrial development more accurately based on the simultaneous equation model evaluation.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Variable** & & & & & & & \\
**Name** & & & & & & & \\ \hline _PM2.5_ & PM2.5 concentration & – & 234 & 58.86 & 21.15 & 14 & 108 \\ _2S_ & The proportion of secondary & \% & 234 & 47.71 & 8.37 & 19.01 & 62.1 \\ _3S_ & The proportion of the tertiary & \% & 234 & 40.24 & 11.49 & 24.44 & 80.56 \\ _FA_ & Fixed asset investment & Ten thousand & & & & & \\ _Innovation_ & Micro invention patent & – & 234 & 4883.39 & 14,455.83 & 36 & 106,948 \\ _Open_ & Actual use of foreign investment & \% & 234 & 2.42 & 2.50 & 0.07 & 15.36 \\ _Pop_ & The population density & People / km\({}^{2}\) & 234 & 2971.22 & 2148.00 & 614 & 11,880 \\ _FDI_ & Actual use of foreign investment & Ten thousand & 234 & 1,122,386 & 2,648,440 & 3799 & \(2.05\times 10^{7}\) \\ _Trans_ & Private car ownership per unit of & – & 234 & 39.86 & 41.55 & 3 & 209.94 \\  & highway mileage & & & & & & \\ _Enreiro_ & Green area coverage in & \% & 234 & 39.33 & 7.62 & 23.34 & 92.87 \\ _UL_ & Urban population as a & \% & 234 & 43.45 & 14.81 & 20 & 84 \\  & proportion of total population & & & & & & \\ _CE1_ & CO\({}_{2}\) emissions per unit of GDP & Ton / million & 234 & 3.43 & 1.86 & 0.38 & 9.36 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical characteristics and units of variables.
## 2 System Model
### Multiple Criteria Decision-Making (MCDM) Technique  
The MCDM decision metric is applied to access the link cost function of all the multiple routes and choose the best route based on the estimated parameter value of the relay devices. The MCDM metric aggregates all the parameter value (energy consumption, mobility, queue length size, and link quality of the relay devices) into the one single metric and provides weight to the devices based on the estimated parameter value. The parameter's value is estimated and monitored periodically during the route discovery and topology sensing stage, which is comprised of link sensing, neighbors, and topology discovery by broadcasting the topological packets to the neighbor's devices. The MCDM metric value is estimated locally at every device and broadcasts to their neighbors' devices into a single metric value by the individual device sending multiple metrics in order to mitigate the control overhead packets in the network. The MCDM metric of relay devices \(c\) in the routes is calculated as follows:  
\[MCDM=\left\{W_{RE}\times EC_{(m,n)}^{c}(t)+W_{mob}\times Moly_{(m,n)}^{c}(t)+W_ {LQ}\times LQ_{(m,n)}^{c}(t)+W_{QL}\times QL_{(m,n)}^{c}(t)\right\} \tag{10}\]  
where \(W_{RE}\), \(W_{mob}\), \(W_{LQ}\), and \(W_{QL}\) are weight assigned according to the estimated parameters value and ranging from \"0\" to \"1\". The device which has a high MCDM metric value has a higher chance of selection in the optimal route. Whereas, the device which has below threshold MCDM metric value is avoided in the route selection procedure. Therefore, the MCDM metric used these parameters value to make a decision to establish an optimal path between the sources and the destination device. The flow chart for the optimal route selection procedure of the proposed EMBLR routing scheme is shown in Figure 4.
# An Algorithm to Assist the Robust Filter for Tightly Coupled RTK/INS Navigation System
###### Abstract  
The Real-Time Kinematic (RTK) positioning algorithm is a promising positioning technique that can provide real-time centimeter-level positioning precision in GNSS-friendly areas. However, the performance of RTK can degrade in GNSS-hostile areas like urban canyons. The surrounding buildings and trees can reflect and block the Global Navigation Satellite System (GNSS) signals, obstructing GNSS receivers' ability to maintain signal tracking and exacerbating the multipath effect. A common method to assist RTK is to couple RTK with the Inertial Navigation System (INS). INS can provide accurate short-term relative positioning results. The Extended Kalman Filter (EKF) is usually used to couple RTK with INS, whereas the GNSS outlying observations significantly influence the performance. The Robust Kalman Filter (RKF) is developed to offer resilience against outliers. In this study, we design an algorithm to improve the traditional RKF. We begin by implementing the tightly coupled RTK/INS algorithm and the conventional RKF in C++. We also introduce our specific implementation in detail. Then, we test and analyze the performance of our codes on public datasets. Finally, we propose a novel algorithm to improve RKF and test the improvement. We introduce the Carrier-to-Noise Ratio (CNR) to help detect outliers that should be discarded. The results of the tests show that our new algorithm's accuracy is improved when compared to the traditional RKF. We also open source the majority of our code, as we find there are few open-source projects for coupled RTK/INS in C++. Researchers can access the codes at our GitHub.  
RTK; INS; tightly coupled; RKF; CNR
# Segmentation of Liver Anatomy by Combining 3D U-Net Approaches
## 4 Models
### 3D Dense U-Net Model  
3D Dense U-Net is based on the original U-Net and 3D U-Net version. The architecture adds residual and dense interconnections between layers processed in convolutional step blocks (see Figure 2b). A single step block contains the 3D convolution of size \(3\times 3\times 3\)  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **U-Net** & **MultiRes U-Net** & **Dense U-Net** \\ \hline Depth & 4 & 4 & 4 \\ Dropout & 0.2 & 0.2 & – \\ Epochs & 100 & 100 & 100 \\ Batch size (slabs) & 8 & 8 & 8 \\ Batch size (full) & 1 & 1 & 1 \\ Batch normalization & True & True & True \\ Loss & Dice loss & Dice loss & Dice loss \\ Optimizer & Adam & Adam & Adam \\ Momentum & 0.99 & 0.99 & 0.99 \\ Learning rate & 0.0001 & 0.0001 & 0.0001 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model parameters on slabs and full volume.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **U-Net** & **MultiRes U-Net** & **Dense U-Net** \\ \hline Depth & 4 & 4 & 3 \\ Dropout & 0.25 & 0.2 & – \\ Epochs & 10 & 10 & 10 \\ Steps per epoch & 1000 & 1000 & 1000 \\ Boxes per step & 32 & 32 & 32 \\ Batch normalization & False & True & True \\ Loss & binary & binary & binary \\  & cross-entropy & cross-entropy & cross-entropy \\ Momentum & – & 0.99 & 0.99 \\ Optimizer & Adam & Adam & Adam \\ Learning rate & 0.0001 & 0.0001 & 0.0001 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model parameters on boxes.  
followed by the ReLU activation and the batch normalization layer with a momentum of 0.99. The output of this operation is concatenated with an input. Then, the process is repeated for the result of the previous concatenation. The contracting and expanding paths look the same as in the 3D U-Net model. The contracting path consists of four convolutional step blocks, each followed by 3D max-pooling of size \(2\times 2\times 1\). The expanding path also consists of four step blocks, each followed by 3D upsampling of size \(2\times 2\times 1\). The outputted feature maps by upsampling operation are concatenated with the corresponding ones from the contracting path. Finally, the last convolutional step block is applied, followed by a 3D convolution of size \(1\times 1\times 1\). A sigmoid activation is used to produce a final probability map.  
To reiterate, the model above was used for two setups--the full 3D and the slab-based setups. Further, it was used for liver volume segmentation in the preprocessing step.  
In the box-based set-up, a shallow version of the Dense U-Net was used to adapt the model to the smaller resolution of inputs. Particularly, both the number of filters and the depth of the model were reduced. The latter was reduced from four to three. The number of filters for the contracting and the expanding path was reduced from (6; 12; 24; 48; 96; 48; 24; 12; 6) to (2; 4; 8; 16; 8; 4; 2), respectively.
## Appendix A Fluorescence Retrieval
### Performance of the Retrieval Algorithm
This means that longer integration time would be required in the O\({}_{2}\)-B band on vegetation, as canopy reflectance is lower in the red than in the far red. After convolution by the spectrometer ISF and the addition of noise, \(n\) channels (four in the O\({}_{2}\)-B, three in the O\({}_{2}\)-A band) were extracted from the whole spectrum at wavelengths indicated in Table 10 to simulate TriFLEX measurements. To increase SNR, spectral samples were binned by computing the mean of several spectral samples around the central wavelength of each channel. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**O\({}_{2}\)-A**} & \multicolumn{4}{c}{**O\({}_{2}\)-B**} \\ \hline Central wavelength (nm) & 757.86 & 760.51 & 770.46 & 683.14 & 685.00 & 686.97 & 697.06 \\ Channel width (nm) & 0.5 & 0.8 & 0.9 & 0.5 & 0.7 & 0.1 & 0.9 \\ Number of spectral samples & 5 & 8 & 9 & 5 & 7 & 1 & 9 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Positions and width of the TriFLEX channels in the O\({}_{2}\)-A and O\({}_{2}\)-B bands. This leads to the system of equations for the TriFLEX channels:  
\[L_{u}^{TOC}=\rho_{u}\;L_{u}^{WREF}+SIF_{u}\] \[\rho_{u}=\sum_{i=0}^{n-2}a_{i}\;\lambda^{i}\] \[SIF_{u}=K_{u}\;SIF\]  
This system has \(3n\) unknowns (\(\rho_{u}\), \(SIF_{u}\), \(a_{i}\)) and \(3n\) equations, and can be solved exactly. The solution for \(SIF\) is given by:  
\[SIF=\frac{\sum_{u=1}^{n}(-1)^{u}\frac{L_{u}^{TOC}}{L_{u}^{WREF}}\prod_{i<j,i\
eq u }(\lambda_{i}-\lambda_{j})}{\sum_{u=1}^{n}(-1)^{u}\frac{K_{u}}{L_{u}^{WREF}} \prod_{i<j,i\
eq u}(\lambda_{i}-\lambda_{j})} \tag{10}\]  
For each set of SCOPE input parameters from Table 1, we performed 30 fluorescence retrievals in the O\({}_{2}\)-A and O\({}_{2}\)-B bands. This value corresponds approximatively to the number of measurements averaged with the TriFLEX instrument in moderate light conditions at a final sampling rate of 1 min used in this study. In high light conditions, or when SIF is averaged according to EC sampling rate (30 min), the number of averaged samples is higher, and the final dispersion of the error is reduced. For each simulated case, we computed the mean bias (\(SIF_{bias}\)), we indicated the amount of error between the actual value and the mean retrieved value:  
\[SIF_{bias}=\frac{1}{30}\sum_{i=1}^{30}\left(SIF_{ret(i)}-SIF_{true}\right) \tag{11}\]  
The mean, the median, and the root mean squared error (\(RMSE\)) were computed over the whole database from the bias values to evaluate the retrieval method, with \(N\) being the number of simulated cases (\(N=36\)). Results are summarized in Table 2, with three or four channels for O\({}_{2}\)-B and three channels for O\({}_{2}\)-A.  
\[\overline{SIF}_{bias}=\frac{1}{N}\sum_{i=1}^{N}SIF_{bias}\] \[\overline{SIF}_{median}=Median\left(SIF_{bias}(i)\right);\quad i \in[1,N] \tag{12}\] \[\overline{SIF}_{RMSE}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(SIF_{ bias}\right)^{2}}\]
# Designing a Softwarized Network Deployed on a Fleet of Drones for Rural Zone Monitoring
## 4 Computational Model of the Backbone Node Drones  
In this section, considering the service chains described above, we model the computational load of the backbone node drones hosting the platform elements. Let us note that each drone can be, at the same time, the ingress node for some VTs and the egress node for some clients. For this reason, limiting our attention to the traffic related to the considered platform, in the generic backbone drone \(\wp\) the total network traffic is the sum of the traffic entering from the VTs (for the acquirer role of \(\wp\)) and the traffic entering from the other backbone drones, to be forwarded to the clients (for the edge streamer role of \(\wp\)).  
The overall computational load of the generic drone \(\wp\) is:  
\[\Omega^{(ALL)}=\Omega^{(ACh)}+\Omega^{(ESn)} \tag{1}\]  
where \(\Omega^{(ACh)}\) and \(\Omega^{(ESn)}\) are the computational loads related to the acquirer and the edge streamer roles, respectively. They can be calculated as the sum of the contributions due to each element of the service chains shown in Figure 5:  
\[\Omega^{(ACh)}=\Omega^{(ACh)}_{LB}+\Omega^{(ACh)}_{DPI}+\Omega^{(ACh)}_{A}+ \Omega^{(ACh)}_{PM}+\Omega^{(ACh)}_{TC}+\Omega^{(ACh)}_{ST} \tag{2}\]  
\[\Omega^{(ESn)}=\Omega^{(ESn)}_{LB}+\Omega^{(ESn)}_{DPI}+\Omega^{(ESn)}_{ES}+ \Omega^{(ESn)}_{PM}+\Omega^{(ESn)}_{FR}+\Omega^{(ESn)}_{TC} \tag{3}\]  
where each element, representing the computational load of all the blocks depicted in Figure 5, will be defined in Sections 4.1 and 4.2.  
The components of the acquirer computational load will be derived in Section 4.1, while the ones related to the edge streamer will be derived in Section 4.2.  
Following the literature [55], we model the load of a VM hosting a virtual function with a linear model constituted by a base part needed to maintain the VM active, and a part proportional to the managed traffic. Therefore, in the following, for the generic VM \(v\) hosting the generic virtual function \(V\), we will indicate the base computational load needed to run \(v\) as \(\wp_{v}\), and the computational load per unit of flow related to the virtual function \(V\) as \(\xi_{V}\).
# Representing Deep Neural Networks Latent Space Geometries with Graphs
## 4 Applications
### Knowledge Distillation
We have chosen the Frobenius norm for its simplicity, ease of use in current DL frameworks and to facilitate comparisons with RKD-D. In practice, many such additive terms can be added, one per pair of layers to match in teacher and student architectures. Note that the layers chosen to form a pair do not have to come from the same depth in their respective architectures, allowing for students and teachers to have different depths. Let us point out that the dimensions of latent spaces in \(T\) and \(S\) are likely to be very different. As such, the LGGs are susceptible to be hard to compare directly. This is why we make use of graph normalization (as described in step 4 of LGG graph construction), where similarities are considered relatively to each other. Despite not being ideal, graph normalization allows us to obtain considerable gains in accuracy, as illustrated in the following experiments. The GKD loss measures the discrepancy between the adjacency matrices of teacher and student LGGs. In this way, the geometry of the intermediate representations of the student will be enforced to converge to that of the teacher (which is already fixed). Our intuition is that since the teacher network is expected to generalize well to the test, mimicking its latent geometry should allow for better generalization of the student network, as well. Moreover, since we use normalized LGGs, the similarities are considered relative to each other (so that each vertex on the graph has the same \"connection strength\"), contrary to initial works in RKD [9], where each distance is taken in its absolute value, and, thus, one sample can eclipse all the others (e.g., being too far away from the others). **Experiments:** To illustrate the gains we can achieve using GKD, we ran the following experiment. Starting from a WideResNet28-1 [39] teacher architecture with many parameters, for which an error rate of 7.27% is achieved on CIFAR-10, we first train a student without KD, called baseline, containing roughly 4 times less parameters. The resulting error rate is 10.37%. We then compared RKD and GKD. Results in Table 1 show that GKD doubles the gains of RKD over the baseline. More details and experiments can be found in Reference [10], where it is shown that the gains can be explained by the fact the GKD student presents decisions that are more consistent with the teacher than the RKD student. In addition, other experiments in Reference [10] suggest that simple modifications to graph construction (e.g., connecting only data points of distinct classes) can improve even further the gains reported in Table 1.
## 3 Results and Discussion
### Analysis of Sintering Mechanism  
In order to analyze the phase transformation of sintered bricks during sintering, the phase composition of sintered bricks at different temperatures was analyzed, and the results are shown in Figure 10. When the sintering temperature is 900 \({}^{\circ}\)C, the diffraction peaks of gibbsite and kaolinite in the structure disappear, and the diffraction peaks of quartz, spinel, perovskite, etc. become weak, indicating that these phases decompose when calcined at 900 \({}^{\circ}\)C, but no new phases are formed at this time. When the sintering temperature is 1100 \({}^{\circ}\)C, the diffraction peaks of perovskite, part of spinel and anatase in the structure disappear. Quartz and other silicates melt to form new phases, albite and amphibole, and albite has been proved to improve the strength of sintered bricks [45; 46].  
Figure 11 demonstrates the SEM micrographs of sintered bricks at different temperatures. Unburned brick particles of polyaluminum chloride slag are arranged in a disorderly fashion (Figure 11a) and are consolidated by physical action at this time. When the temperature reaches 900 \({}^{\circ}\)C (Figure 11b), the sintered brick just reacts and the debris structure on the particles gradually disappears. When the temperature reaches 1100 \({}^{\circ}\)C (Figure 11c), more molten-liquid phase is formed in the structure to make the particles more closely connected, which improves the compactness of the sintered brick, thus improving the performance of the sintered brick.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Project**} & \multirow{2}{*}{**pH**} & **Cl\({}^{-}\)** & **Pb** & **Cr** & **Cd** & **Cu** & **Mn** & **Zn** \\  & & **(mg/L)** & **(mg/L)** & **(mg/L)** & **(mg/L)** & **(mg/L)** & **(mg/L)** & **(mg/L)** \\ \hline Sintered brick & 8.2 & 7.1 & 0.0003 & 0.0231 & 0.0008 & 0.0088 & 0.021 & 0.0176 \\ GB8978-1996 & 6–9 & - & 1.0 & 1.5 & 0.1 & 1.0 & 5.0 & 5.0 \\ \hline \hline \end{tabular}
\end{table}
Table 7: pH value of pollutants, mass concentration of Cl\({}^{-}\) and heavy metals in sintered brick leaching solution.  
Figure 10: XRD spectrum of sintered bricks at different temperatures.