esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 5G Signal and Detection Performance Analysis  
### 5G Signal Overview  
The 5G system is the evolution of the 4G system and meets the needs for higher data transmission rates, greater data traffic, low latency, and large-scale connections. Additionally, 5G new radio (NR) is a key technology in 5G, which is a global 5G standard based on OFDM's new air interface design. Table 1 presents a comparison of the main parameters of 4G and 5G NR, including waveform, bandwidth, and subcarrier spacing.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **4G** & **5G NR** \\ \hline Frequency Range & \textless{}3 GHz & FR1: \textless{}6 GHz; \\  & & FR2: \textgreater{}24.25 GHz \\ Subcarrier Spacing & 15 kHz & FR1: \textless{}15/30/60 kHz; \\  & & FR2: 60/120/240 kHz \\ Channel Bandwidth & \textless{}20 MHz & FR1: \textless{}100 MHz; \\  & & FR2: \textless{}400 MHz \\ Uplink Waveform & DFT-S-OFDM & DFT-S-OFDM/CP-OFDM \\ Downlink Waveform & CP-OFDM & CP-OFDM \\ \hline \hline \end{tabular}
\end{table}
Table 1: **Overview of the main parameters of 4G and 5G NR.**It can be seen from Table 1 that both the uplink and downlink signals of 5G signals can use cyclic prefix orthogonal frequency division multiplexing (CP-OFDM) waveforms and the physical layer uplink can also use discrete Fourier transform-spread orthogonal frequency division multiplexing (DFT-S-OFDM) waveforms. OFDM technology converts high-speed information streams into lower-rate parallel data streams through serial-to-parallel conversion and then modulates the parallel data with multiple orthogonal subcarriers. The 5G signal used for detection in this paper is the CP-OFDM waveform. When the cyclic prefix is not considered, the OFDM-modulated signal can be expressed as follows:  
\[s_{\text{OFDM}}(t)=\sum_{i=0}^{N-1}d_{i}\exp\biggl{[}j2\pi\biggl{(}f_{c}+\frac{ i}{T_{\text{OFDM}}}\biggr{)}t\biggr{]}=\sum_{i=0}^{N-1}d_{i}\exp\biggl{(}j2\pi f_{c}t+j2 \pi\frac{i}{T_{\text{OFDM}}}t\biggr{)} \tag{1}\]  
where \(T_{\text{OFDM}}\) is the symbol period, \(N\) is the number of subcarriers in the OFDM modulation process, \(f_{c}\) is the carrier frequency, and \(d_{i}\) is the amplitude of the \(i\)-th modulation symbols. When the carrier frequency is not considered, the baseband signal waveform \(x(t)\) can be obtained from (1), which can be represented as follows:  
\[x(t)=\sum_{i=0}^{N-1}d_{i}\exp\biggl{(}j2\pi\frac{i}{T_{\text{OFDM}}}t\biggr{)} \tag{2}\]  
Additionally, the sampled signal can be expressed as follows:  
\[x_{k}=x\biggl{(}k\frac{T_{OFDM}}{N}\biggr{)}=\sum_{i=0}^{N-1}d_{i}\exp\biggl{(} j\frac{2\pi ki}{N}\biggr{)}\text{, }0\leq k\leq N-1 \tag{3}\]  
It can be seen that \(X=[x_{0},x_{1},\cdots,x_{k},\cdots,x_{N-1}]\) is the Inverse Discrete Fourier Transform of \(D=[d_{0},d_{1},\cdots,d_{i},\cdots,d_{N-1}]\); thus, OFDM modulation can be achieved using Inverse Discrete Fourier Transform.
## 4 Discussion
### Gpp  
The seasonal variation of GPP (Figures 5 and 6) is controlled by the seasonal dynamics of climatic variables such as radiation, temperature and precipitation [73]. The range of simulated GPP under baseline climate conditions is relatively lower compared to the values obtained for Mediterranean pine forests, for example, in Tuscany with a mean estimated value of 1760 g C m\({}^{-2}\) year\({}^{-1}\)[74], but closer to values reported in other modelling studies, ca 900 g C m\({}^{-2}\) year\({}^{-1}\)[34]. The analysis of eddy-flux tower data reports GPP values for Mediterranean pine forest in the range of 714-1151 g C m\({}^{-2}\) year\({}^{-1}\)[75]. With our simulations, we were able to explore the effect of stand LAI and elevation on the primary productivity of typical Mediterranean pine forest on the island of Lesvos. Under baseline climate conditions, GPP ranged from ca 445 to 880 g C m\({}^{-2}\) year\({}^{-1}\) across plots of varying LAI, determined by the time since the last stand-replacing fire. GPP followed the expected trajectory with stand age, increasing from younger to mature stands and then gradually decreasing following variation in LAI [43; 76]. Simulations along our elevation (temperature) gradient plots, which are also characterized by decreasing LAI with elevation (Table 5), ranged from ca 605 to 1070 g C m\({}^{-2}\) year\({}^{-1}\) and revealed a stronger effect of LAI than temperature variation on GPP (Figure 4a). The above suggests that based on our model parameterization and the environmental conditions found across our study plots, a temperature difference of ca four degrees has a \"weaker\" effect on GPP than a ca four-unit difference in LAI. One study, based on a global dataset, highlighted temperature and LAI as important GPP drivers, among others, with mean annual temperature explaining 23% of the GPP variation and LAI explaining about 40% of the GPP variation [76].  
Under climate change conditions, the simulated annual GPP was lower across all post-fire chronosequence plots, as well as the plots located along the elevation gradient (Figures 5 and 6). These outputs suggest that photosynthetic water limitation could significantly reduce the assimilated carbon in Mediterranean pine forests. Our findings contradict simulations using the BIOME-BGC model, which predicted an increase in GPP for Mediterranean conifers following the RCP4.5 scenario [77], but do agree with the strong decrease in GPP projected for some Mediterranean conifer forests under the RCP8.5 scenario [78]. In general, our results show that across our study plots the potential positive effects of rising temperature and CO\({}_{2}\) fertilization on photosynthetic carbon gain seem to be neutralized by enhanced water limitations [79; 80].
## 4 Model
### Clause Segmentation  
We modeled clause segmentation as a sequence-labeling task, where the input sentence is a sequence \(X=(x_{1},\ldots,x_{i},\ldots,x_{n})\) with \(n\) tokens and the output label sequence \(Y=(y_{1},\ldots,y_{i},\ldots,y_{n})\). Note that \(y_{i}\) is binary, i.e., \(y_{i}=1\) if \(x_{i}\) is the head word token for a clause, and otherwise, \(y_{i}=0\). Therefore, we encountered the clause segmentation task with a sequence-tagging model, DisCoDisCo [17], used in the discourse segmentation task: embed the input sentence; encode with a single Bi-LSTM; decode with a linear projection layer; indicate the first token of each clause in the output tag sequence.  
In the embedding layer, we relied on three kinds of word embeddings concatenated together:  
1. Bi-LSTM encoded character embeddings;
2. Static word embeddings from fastText [33];
3. Fine-tuning word embeddings from a pretrained language model (PLM).  
Additionally, we introduced and embedded various grammatical information such as lemmas, parts-of-speech (POSs), and syntactic dependencies generated by Stanza [31]. These tokenwise feature embeddings were concatenated with word embeddings, and the word \(w_{i}\) in the input sentence was embedded as:  
\[u_{i}=\text{Concat}(u_{i}^{word}+u_{i}^{feat}) \tag{1}\]  
where \(u_{i}^{word}\) is concatenated from three kinds of word embeddings and \(u_{i}^{feat}\) is grammatical features embeddings.  
The embedding \(\mathbf{u}_{1:n}\) of the input sentence with \(n\) tokens were fed through a BiLSTM network, and the output \(s_{i}\) was then calculated by a linear projection layer to predict the segmentation tag for the \(i\)-th token:  
\[s_{i}=\text{Linear}(\text{BiLSTM}(\mathbf{u}_{1:n},i)). \tag{2}\]  
Given \(s_{i}\), we obtained the predicted tag sequence \(\mathbf{\hat{t}}\) and optimized the cross-entropy loss of \(\mathcal{L}(\mathbf{\hat{t}},\mathbf{t})\) to train the weights of the PLM, the BiLSTM network, and the linear projection layer, where \(\mathbf{t}\) is the gold tag sequence.
# MPVT: An Efficient Multi-Modal Prompt Vision Tracker for Visual Target Tracking
## 3 Experimental Results and Discussion
### Model Analysis
#### 3.6.3 Model Efficiency  
The efficiency of MPVT was analyzed from three aspects: training time, GPU memory usage, and inference speed, as shown in Table 4. The training time and GPU memory usage by prompt learning decreased significantly compared with those of full-parameter fine-tuning models, saving 43.8% of GPU memory and 62.9% of training time. In terms of inference speed, due to the addition of model parameters and computational complexity, the inference speed slightly decreased by about 1 fps. The dynamic adaptive prompted fusion (DAPF) module maintained consistent computational complexity for all auxiliary modalities. This minimal inference performance difference enables significant advantages in practice. Regarding edge deployment, 43.8% memory reduction allows operation on resource-constrained devices, and efficiency gains enable the simultaneous processing of more streams.
## 2 Numerical Simulations
### Computational Domain and Boundary Conditions  
The computational flow domain of a typical FAOT includes the flow field of a fluidic oscillator, the front and rear chambers of the cylinder, the connecting passage F connected with the front chamber, the connecting passage R connected with the rear chamber, the flow field in front of the cylinder lid, and the side passages connecting to the vents and the flow field in front of the cylinder lid. A typical computational domain, as presented in Figure 2, consists of around 80,000 cells and is generated in such a way that nearly all the features of the FAOT can be reflected. Note that one of the side passages was masked in Figure 2, in order to visualize the partial structures of the computational domain.  
Figure 1: Schematic diagram of a fluidic axial oscillation tool [18]. 1–Mandrel, 2–Top tie-in of axial oscillation assembly, 3–Inner sleeve of axial oscillation assembly, 4–Exteral sleeve of axial oscillation assembly, 5–Disc springs, 6–Middle tie-in of axial oscillation assembly, 7–Middel tie-in of mandrel, 8–Step seal, 9–Bottom tie-in of mandrel, 10–Bottom tie-in of axial oscillation assembly, 11–Top tie-in of pressure pulsation assembly, 12–Disc springs, 13–Exteral sleeve of pressure pulsation assembly, 14–Gland, 15–output-fed bistable fluidic oscillator, 16–Cylinder, 17–Adjusting rod, 18–Piston, 19–Dowel pin, 20–Cylinder lid, 21–Inner sleeve of pressure pulsation assembly, 22–Throttle plate, and 23–Bottom tie-in of pressure pulsation assembly.  
The computational domain is a fine, mapped grid, moderately clustered in the interaction region of the fluidic oscillator.  
Figure 2 also indicates the imposed boundary conditions of the computational domain. A velocity inlet boundary condition was adopted at the inlet of the fluidic oscillator, and a pressure outlet boundary was applied at the bottom face of the flow domain in front of the cylinder lid. The pressure outlet boundary condition was set to atmospheric pressure, corresponding to the operating conditions in the experimental tests. Besides, the no-slip boundary conditions were imposed upon all the walls.
# Could We Realize the Fully Flexible System by Real-Time Computing with Thin-Film Transistors? +
## 2 Materials and Methods
### Fault-Tolerant Image Processing Algorithm
#### 2.2.2 Classification Algorithms: Feature Processing  
Multiple Layer Perceptron (MLP) [20] is the typical and classical algorithm of artificial neural networks (ANN) due to its computation units is common in the ANN. It is also widely used in the image classification and image fitting fields. In general, MLP mainly contains multiply-accumulate units and non-linear (NL) units.  
As shown in Figure 2a, is the typical structure of three-layer perceptron. The input layer contains the data from the image sensors that have been pre-processed. Once the input data are loaded, then the weights of the first layer will be multiplied with the input data and the results are accumulated to the data of hidden layer. Next, the data of hidden layer will be processed by non-linear algorithm and then working out the results of the second layer. The non-linear used here is the sigmoid function, whose curve is shown in Figure 2b, and the function expression is:  
\[\mathrm{Sigmoid}(x)\ =\ \frac{1}{1\ +\ \mathrm{e}^{-x}} \tag{4}\]  
After the multiply-accumulate operation of the output layer, the classification results are sent to the back propagation (BP) units to update weights. Larger size of hidden layer means higher classification accuracy; therefore, it is an excellent solution to balance accuracy and efficiency in complex tasks. Moreover, the training process of neural network can gradually eliminate the error that is caused by imperfect computation.
# Volumetric Techniques for Product Routing and Loading Optimisation in Industry 4.0: A Review
## 4 Volumetrics
### Optimisation Problems: Container and Pallet Loading Problems  
The Container Loading Problem (CLP) is a combinatorial optimisation problem that focuses on efficiently packing a heterogeneous set of items or boxes into one or more containers of a fixed size, with the objective of maximising space utilisation or minimising the number of containers used. The primary goal is to find an arrangement of the items within the container(s) that optimises a specific objective function, often related to space efficiency [5; 6; 7]. This problem is of practical importance in the field of logistics and transportation, where efficient cargo loading can lead to cost savings and improved overall supply chain efficiency [8; 9; 10]. There are different variants of the CLP, including the Pallet Loading Problem, and the 2D and 3D packing problems. In 2D variants, only the length and width of items and the container are considered, while in 3D variants, the height or depth of items and the container are also taken into account [11].  
The CLP has a wide range of practical applications, including optimising cargo loading in shipping containers, loading items into storage spaces, and even in the layout design of warehouses and transportation vehicles [8]. Solving the CLP is NP-hard, which means that finding an optimal solution is a computationally challenging task due to its combinatorial nature and the need to consider various constraints [5; 11]. These constraints encompass container-related factors such as weight limits and weight distribution, item-specific considerations like loading priorities, orientations, and stacking limits, cargo-related constraints regarding parcel completeness and item allocation, and positioning constraints for hazardous items, relative positioning, and multidrop scenarios, as well as load-related constraints, focusing on cargo stability and operational complexity [12]. Different algorithmic, heuristic, and metaheuristic approaches are commonly used to find near-optimal or optimal solutions to this problem, depending on the specific objectives and constraints of the problem instance [13; 14].
# Leveraging Blockchain and Consignment Contracts to Optimize Food Supply Chains Under Uncertainty
## 4 Formation of Demand Equation
### Demand Function Without Blockchain: Model WBC  
Without adoption of a blockchain platform, the customer's decisions to buy food items are influenced by factors such as the price, hassle cost, and safety concerns. The expenses related to finishing a transaction, such as the time and care required, or the price of completing paperwork, are known as hassle costs. They can also be used to describe the degree of aggravation or frustration someone feels when they are inconvenienced. Demand may also be affected by privacy concerns in the WBC model. As this is a centralized system, a third party is always monitoring all transactions and information sharing. In the food supply chain, the buyer will generally be curious about the previous reputation of the seller. Thus, if the seller's information regarding the quality of food is already uploaded in the blockchain platform in a decentralized manner, the buyer can easily access this and gather the information about the previous reputation of the seller. Information gathering through a decentralized blockchain platform is not available in the case of WBC. Therefore, some amount of demand must be affected by this. Without blockchain, the total demand for the food is as follows:  
\[D_{WBC}=(D_{0}^{WBC}-\eta_{1}p_{WBC}-\alpha-\beta h_{c})x \tag{1}\]
## 3 Results
### Simulation Results
#### 3.2.3 Tracking Errors  
Figure 9 shows the tracking errors only during the satellite tracking phase, where negligible difference are observed between using offline (open-loop) or online (closed-loop) motion planner. Notably the proposed A-PI compensates for slowly time varying disturbances, producing such small errors, with smooth control effort.  
Figure 9: Task errors for closed–loop configuration space trajectories (dashed–lines after (27)) vs. open–loop configuration space trajectories (continuous lines after (28)).
# Understanding Oxygen Concentrator Failures in Low Resource Settings: The Role of Dust and Humidity
## 3 Results and Discussion
A comparison of the samples can be made by plotting the oxygen percentage of the gas emitted from the OC instrument at the time of zeolite sampling against the adsorbed amount of water calculated from the TGA results (Figure 7) as previously reported in Equation (2). The two variables appear to be closely connected, with the adsorbed amount notably impacting OC efficiency and potentially leading to instrument failure. Since water constitutes the main adsorbed compound, and it is well known from the literature that it competitively adsorbs on N\({}_{2}\) active sites [44], it is possible to conclude that the accumulation of moisture in the solid adsorbents hinders N\({}_{2}\) adsorption, causing a premature failure of OCs adopted in the African countries considered in this study. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Country** & **Acronym** & \begin{tabular}{c} **Si/Al** \\ **(mol/mol)** \\ \end{tabular} & \
\begin{tabular}{c} **BET Area** \\ **(m\({}^{2}\)/g)** \\ \end{tabular} &
\begin{tabular}{c} **Pore Volume** \\ **(cm\({}^{3}\)/g)** \\ \end{tabular} \\ \hline Ethiopia & E-1 & 1.18 & 561.99 & 0.228 \\  & E-2 & 1.20 & 538.99 & 0.220 \\ \hline Uganda & U-1 & 1.22 & 410.12 & 0.231 \\  & U-2 & 1.20 & 518.19 & 0.298 \\ \hline South Africa & SA & 1.16 & 560.40 & 0.285 \\ \hline n.a. & C-X & 1.24 & 338.48 & 0.193 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Characteristics of the zeolite samples. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Country**} & \multirow{2}{*}{**Acronym**} & \multicolumn{2}{c}{**Temperature**} & \multirow{2}{*}{**\(\boldsymbol{\alpha\%\text{ (wt\%)}}\)**} & \multirow{2}{*}{**\(\boldsymbol{q_{t}}\)**} \\  & & **(\(\boldsymbol{\text{{}^{\circ}}}\)C)** & & \\ \hline \multirow{2}{*}{Ethiopia} & E-1 & 156.9 & 22.28 & 0.286 \\  & E-2 & 158.1 & 23.00 & 0.298 \\ \hline \multirow{2}{*}{Uganda} & U-1 & 141.5 & 21.28 & 0.270 \\  & U-2 & 144.0 & 21.30 & 0.270 \\ \hline South Africa & SA & 144.3 & 20.15 & 0.252 \\ \hline n.a. & C-X & 132.6 & 19.00 & 0.234 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: TGA results for each zeolite sample including the temperature at which the maximum mass loss rate occurs, the mass loss percentage (\(\boldsymbol{\alpha\%}\)), and the total adsorbed amount \(q_{t}\). Figure 5: Plots of the results of the thermo-gravimetric analysis (TGA). Comparison between sample U-1 and U-2 (**a**) and sample E-1 and E-2 (**b**). Additional comparisons for samples C-X, U-2, E-2, and SA for the mass variation (**c**) and derivative of the mass variation (**d**). Figure 6: Plot of the first-order derivative of the mass variation over the duration of the TGA test (DTG) against MS curves, with the characteristic water mass–charge ratio (m/z) of 18.
## 2 Materials and Methods  
The material investigated in the present study is a cast polycrystalline superalloy K465, and the nominal chemical composition (wt %) of K465 alloy is shown in Table 1. After vacuum induction melting (VIM) and casting, alloy bars received a conventional solution treatment at 1210 \({}^{\circ}\)C for 4 h. The temperature for heat treatment was controlled within \(\pm\)1 \({}^{\circ}\)C. These alloy bars were then machined into cylindrical specimens with the gauge length of 25 mm and diameter of 5 mm. In the present study, all creep tests were conducted at 975 \({}^{\circ}\)C with applied stress of 225 MPa by following the International Organization for Standardization (ISO) standard [25]. This accelerated creep condition was chosen to reduce the change of specimen geometry induced by oxidation. Before precrypt test and RHT, four specimens with the same casting conditions were creep-tested to fracture. The average creep life of this alloy is 110 \(\pm\) 10 h. A number of specimens were creep-tested with an accumulated strain of 0.03, 0.05, and 0.07 respectively. After that, the specimens underwent rejuvenation treatment in a furnace with vacuum circumstance. The RHT was carried out at 1220 \({}^{\circ}\)C for 6 h in order to restore the microstructure, and then the specimens were creep-tested again. Before performing the creep test, the surfaces of all the samples were carefully polished to remove the thin oxidation layers formed during the accumulated test and RHT. The gauge length and diameter of cross section were then measured again to ensure that the applied stress was the same as the first creep test.  
For microstructural examination, all samples were cut from the gauge section of crept samples and mechanically polished. After chemically etching in a solution mixture of 40 mL C\({}_{2}\)H\({}_{5}\)OH, 1.5 g CuSO\({}_{4}\) and 20 mL deionized water, all samples were observed by a JSM-6301F field-emission scanning electron microscope (SEM) (JEOL, Tokyo, Japan). Back scattering electron (BSE) images and electron probe microanalysis (EPMA) (JEOL, Tokyo, Japan) were used to identify the types of carbides. To quantitatively evaluate the area fraction of carbides, we used an image analysis system, and at least 6 images were taken from one sample. Thin foils were also prepared for transmission electron microscope observation by using the twin-jet thinning method in 7 % perchloric acid ethanol solution at -20 \({}^{\circ}\)C, 15 V. All transmission electron microscope (TEM) micrographs were taken using a TECNAI 20 (FEI, Eindhoven, Netherlands) instrument operating at 200 kV.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**C** & **Cr** & **Al** & **Ti** & **Mo** & **W** & **Co** & **Nb** & **Ni** \\ \hline
0.20 & 8.95 & 5.76 & 2.90 & 2.06 & 11.0 & 9.80 & 1.12 & Balanced \\ \hline \end{tabular}
\end{table}
Table 1: Nominal chemical composition of K465 alloy (wt %).
## 5 Simulation Results and Discussions
### Impact of Node Density
Thanks to its fusion of two-hop neighbor information, trajectory prediction, and reinforcement-learning-based selection, Traj-Q-GPSR delivers about 28% higher throughput than GPSR at 30 nodes, increasing to 37% at 100 nodes, thereby markedly improving data-delivery efficiency in dense UAV networks. Figure 4: Impact of node density on network performance: (a) Impact of node density on packet loss rate. (b) Impact of node density on average end-to-end delay. (c) Impact of node density on routing efficiency. (d) Impact of node density on throughput.
# Device-To-Device Communication in 5G Environment: Issues, Solutions, and Challenges
## 7 Challenges in D2D Communication
### Challenges in Security
#### 7.3.3 Lack of Standardization  
To ensure the secure communication of D2D user equipment, there are no global standards or policies available. Furthermore, the mechanism that is used to authenticate different applications could vary, which makes it is difficult to guarantee interoperability. Therefore, there is a vital need for standardized documentations that highlight issues like the procedure in securing an interaction between a user and a particular application, and the amount of the user's data that needs to be shared to protect the security and privacy of the information database management. Especially in the decentralized D2D network environment, these issues are becoming crucial and need to be mitigated.