esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Massive MIMO System Model  
Consider a massive MIMO system in Figure 1, where the base station BS is equipped with a large number of antenna \(M\) to server single-antenna users \(K\) with some time and band frequencies [20]. The receiver baseband signal is given by  
\[\mathbf{y}=\sqrt{\mathcal{P}}\mathbf{H}\mathbf{x}+\mathbf{z}, \tag{7}\]  
where \(\sqrt{\mathcal{P}}x\) is the signal data (where the average of the transmitted power is \(\mathcal{P}\)), \(\mathbf{H}\) is the channel between the BS and the \(K\) users which represent the [PERSON] fading channel model and \(\mathbf{z}\) is the independent and identical distributed (i.i.d.) AWGN with zero mean and variance (0, \(\sigma^{2}\)) [55]. At the uplink, the linear detection is used to separate \(K\) user by multiplying the \(M\times K\) weight linear detector matrix \(A\) as:  
\[\mathbf{y}=\sqrt{\mathcal{P}}\mathbf{A}^{H}\mathbf{H}\mathbf{x}+\mathbf{A}^{H} \mathbf{z}, \tag{8}\]  
Then, the corresponded elements of \(k\)th vector of user, it can be addressed as a receiver signal detection vector as:  
\[\mathbf{y}_{k}=\sqrt{\mathcal{P}}\mathbf{a}^{H}\mathbf{h}_{k}\mathbf{x}_{k}+ \sqrt{\mathcal{P}}\sum_{l\
eq k}\mathbf{a}^{H}\mathbf{h}_{l}\mathbf{x}_{l}+ \mathbf{a}^{H}\mathbf{z}. \tag{9}\]  
Considering the ergodic channel fading, the \(k\)th UE's possible uplink SE, \(\mathbf{R}_{k}\), is given by:  
\[\mathbf{R}_{k}=\mathbb{E}\{log_{2}(1+\frac{\mathcal{P}|\mathbf{a}^{H}\mathbf{ h}_{k}\mathbf{x}_{k}|^{2}}{\mathcal{P}|\sum_{l\
eq k}\mathbf{a}^{H}\mathbf{h}_{l} \mathbf{x}_{l}|^{2}+|\mathbf{a}^{H}\mathbf{z}|^{2}})\}. \tag{10}\]  
Furthermore, the sum of uplink rate can be defined as  
\[\mathbf{R}=\sum_{k=1}^{K}\mathbf{R}_{k}. \tag{11}\]  
The weight matrix of linear detection can be conduct with different linear receivers such as MRC, ZF, and MMSE.
# Remote Sensing Image Enhancement Based on Non-Local Means Filter in NSCT Domain
###### Abstract  
In this paper, a novel remote sensing image enhancement technique based on a non-local means filter in a nonsubsampled contourlet transform (NSCT) domain is proposed. The overall flow of the approach can be divided into the following steps: Firstly, the image is decomposed into one low-frequency sub-band and several high-frequency sub-bands with NSCT. Secondly, contrast stretching is adopted to deal with the low-frequency sub-band coefficients, and the non-local means filter is applied to suppress the noise contained in the first high-frequency sub-band coefficients. Thirdly, the processed coefficients are reconstructed with the inverse NSCT transform. Finally, the unsharp filter is used to enhance the details of the image. The simulation results show that the proposed algorithm has better performance in remote sensing image enhancement than the existing approaches.  
Keywords:NSCT; contrast stretching; non-local means filter; unsharp filter +
Footnote †: journal:
# Design of a Chaotic Trajectory Generator Algorithm for Mobile Robots
## 6 Multiple Robots
### Presence of Chaos in Generated Trajectories for Multiple Mobile Robots
#### 6.4.2 Gottwald-Melbourne Test  
We applied the Gottwald-Melbourne test to verify the presence of chaos in the generated trajectories. We used 300 iterations and different initial conditions for each trajectory. Table 2 shows the average results for \(x\) and \(y\) after 100 repetitions, and each one is an average of the results of the number of mobile robots used in each case.  
For example, in the case of using three robots, the value shown for the \(x\) axis is an average of the test result for the three robots, 100 times. We can conclude that, for \(1\leq n\leq 10\) mobile robots moving simultaneously in the work area, chaos is present in their trajectories.
## 5 Experimental Results
### Security Analysis
#### 5.6.7 Comparative Analysis  
Compared to traditional D-H implementations that rely on larger prime numbers for enhanced security, the proposed mechanism achieves an optimal balance between security and performance. As highlighted in Table 3, the proposed approach effectively mitigates SCAs while significantly reducing computational complexity. This dual capability makes it particularly well-suited for resource-constrained environments. By addressing key challenges identified in prior research, the proposed mechanism not only strengthens security against SCAs but also ensures practical applicability in IoT deployments, where efficiency and resource optimization are essential.
## 5 Discussion
### AI Chipset Selection
* Qualcomm Cloud AI 100: The Qualcomm Cloud AI 100 is a GPU-based AI chipset designed for AI inference acceleration, addressing the main power efficiency, scale, process node advancements and signal processing challenges. The computational capacity of the AI 100 family ranges from 70 to 400 TOPS, with a power consumption ranging from 15 to 75 W [52]. * AMD Insinct(tm) MI25: The AMD Insitinct family is equipped with the Vega GPU architecture to handle large data sets and diverse compute workloads. The MI25 model contains 4096 processors with a maximum computational capacity of 12.29 TFlops [53]. * Lattice sensAI: The Lattice sensAI is an FPGA-based ML/AI solution targeting low power applications in the range of 1 mW-1 W [13]. In this case, the programmable architecture of the FPGA can be defined using custom NN cores tailored for AI applications and implemented directly using the major ML frameworks [54]. * Xilinx Versal AI Core: This chipset from Xilinx combines all the three architectures in a single device, providing complete flexibility. In terms of performance, it is on the top of the food chain; however, power consumption may be too elevated [55].
# Customized Convolutional Neural Networks Technology for Machined Product Inspection
## 3 Experimental Method
### Customized Model Design  
Based on the comparison results after training each model in Section 3.2, the VGG19 model had the highest accuracy, so we modified it with the model architecture of VGG19. We still set the Batch Size and Learning Rate to 8 and 0.0001, respectively, which remained  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **VGG19** & **ResNet34** & **LeNet** & **DarkNet19** & **DarkNet53** \\ \hline First training accuracy (\%) & 99.00 & 89.37 & 97.36 & 98.53 & 99.04 \\ Second training accuracy (\%) & 99.07 & 97.22 & 99.07 & 98.92 & 97.56 \\ Third training accuracy (\%) & 98.72 & 94.72 & 98.93 & 98.05 & 98.54 \\ Fourth training accuracy (\%) & 98.96 & 91.28 & 98.22 & 98.84 & 98.91 \\ Fifth training accuracy (\%) & 98.90 & 95.81 & 98.67 & 98.46 & 99.15 \\ Average accuracy (\%) & 98.93 & 93.68 & 98.45 & 98.64 & 98.64 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Training accuracy for the VGG19, ResNet34, LeNet DarkNet 19 and DarkNet53 models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **VGG19** & **ResNet34** & **LeNet** & **DarkNet19** & **DarkNet53** \\ \hline First prediction time (s) & 0.889 & 0.666 & 2.325 & 0.824 & 1.84 \\ Second prediction time (s) & 0.315 & 0.245 & 0.623 & 0.265 & 1.02 \\ Third prediction time (s) & 0.27 & 0.204 & 0.591 & 0.211 & 1.009 \\ Fourth prediction time (s) & 0.202 & 0.172 & 0.532 & 0.136 & 0.945 \\ Fifth prediction time (s) & 0.281 & 0.229 & 0.661 & 0.21 & 1.023 \\ Average prediction time (s) & 0.391 & 0.303 & 0.946 & 0.329 & 1.167 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Prediction time of the implementation of the 5 models.  
Figure 6: (**a**) Examples of six OK images and (**b**) six NG images.  
unchanged. Table 3 details the customized CNN model. First, we added a convolutional layer after the convolutional layer, and set the Stride of the added second convolutional layer to 2, so that the OutPut Size after the convolution could be reduced by half. This method could achieve the function of the pooling layer, and adding Batch Normalization and ReLU activation functions between the two convolutional layers helped to slow down the disappearance of gradients and accelerated the convergence of the model, which had the effect of regularization.  
The OutPut Size of the sixth layer of the customized design model was 24 \(\times\) 24, so we also set the Kernel Size of the seventh layer to 24 \(\times\) 24, so that the OutPut Size of the seventh layer could reach 1 \(\times\) 1, and set the Filters to 512 to replace the effect of the fully connected layer. Using a convolutional layer has fewer parameters than using a fully connected layer. We compared the accuracy of using a fully connected layer with that of using a convolutional layer instead of a fully connected layer, and found that using convolution instead of a fully connected layer performed relatively well.
## 4 Mathematical Model
### Objective Function  
It is crucial to arrange or designate a place for each machine in each cell in order to minimize production costs and flow times. The objective function with constraints, which is important for decreasing flow time and raising production rate, is provided below. Every cell has the same set of constraints and the same goal function.  
\[Objective=\max[\sum\limits_{j=1}^{M_{L}}\frac{F_{ijmL}}{d_{jmL}}\times X_{ ijmL}\times X_{ijIL}\times X_{imll}] \tag{2}\]  
Subject to  
\[\sum\limits_{l=1}^{Loc_{L}}X_{ijIL}\leq 1 \tag{3}\]  
\[\forall\ j=1,2,\ldots,M\ \text{and}\ L=\{Ass,Cel,\text{Pro}\}\]  
\[\sum\limits_{j=1}^{M_{L}}X_{ijIL}\leq r_{jL} \tag{4}\]  
\[forall\ l=1,2,\ldots,Loc_{L}\ \text{and}\ L=\{Ass,Cel,\text{Pro}\}\]  
\[\sum\limits_{j=1}^{M_{L}}F_{ijmL}\times X_{ijmL}\leq dem_{iL} \tag{5}\]  
\(\forall\ i=1,2,\ldots,N\ \text{and}\ L=\{Ass,Cel,\text{Pro}\}\)  
where  
\(F_{ijmL}\) is the quantity of the part \(i\) moved from machine \(j\) to \(m\) in layout type \(L\),  
\(d_{jmL}\) is the distance of machine \(j\) from \(m\) in layout type \(L\),  
\(X_{ijmL}\) is the binary variable equivalent to \(1\) if part \(i\) is moved from machine \(j\) to \(m\) in layout type \(L\), and it is zero otherwise,  
\(X_{ijll}\) is the binary variable equivalent to \(1\) if machine \(j\) is on location \(l\) in layout type \(L\), and it is zero otherwise,  
\(X_{imll}\) is the binary variable equivalent to \(1\) if machine \(m\) is on location \(l\) in layout type \(L\), and it is zero otherwise.  
The first constraint states that for \(\forall\), \(j=1,2,\ldots,M\), and \(L=\{Ass,Cel,\text{Pro}\}\), all locations are assigned by only one location. The second constraint states that for \(\forall\), \(l=1,2,\ldots,Loc_{L}\), and \(L=\{Ass,Cel,\text{Pro}\}\), each replicate of a machine in a particular layout type is to be assigned one location. The third constraint indicates that for \(\forall\ i=1,2,\ldots,N\), the total quantity of the parts to be produced in \(\forall\ L=\{Ass,Cel,\text{Pro}\}\) should not exceed its production demand in that layout type.  
\(Loc_{L}\) indicates a location matrix for each type of layout. Since the number of machines in each layout type is different, the location matrix has different sizes depending on the number of machines assigned to the layout type.
## 3 Results and Discussion
### Effect of Light and Glycerol Concentration on the Growth Performance of _A. platensis_
In order to answer this question, chlorophyll fluorescence analysis was employed and maximum quantum yield (QY) of the Photosystem II (PSII) was recorded, which is a quantifiable indicator of the efficiency of PSII photochemistry [24]. As shown in Table 1, the QY of _A. platensis_ did not change much (0.48-0.56) between the experimental series, which suggest that _A. platensis_ was not directly inhibited by the addition of glycerol, as any kind of inhibition would have been indicated by a significant decrease in the QY (values less than 0.4) [25; 26; 27]. Moreover, the pH of the cultures was almost the same in all experimental series ranging from 9.8-10.1, indicating that the pH of the growth medium was neither a cause for the biomass concentration reduction. All the above results suggest that _A. platensis_ was unable to take advantage of the addition of glycerol regarding biomass growth and moreover it was strongly competed by the bacteria grown resulting in lower cyanobacterial biomass concentrations. These results come in contrast to the results of [PERSON] et al. [8], who reported that the addition of glycerol stimulated growth demonstrating an almost 50% increase in biomass concentration. However, the differences on the cultivation mode (batch vs. semi-continuous) the different strains used, the differences in the cultivation conditions (non-aseptic etc.) apparently have a significant effect on the results. \begin{table}
\begin{tabular}{c c c c c} \hline
**Glycerol Concentration g/L** & **15 Klux** & **10 Klux** & **5 Klux** & **NH3-15 Klux** \\ \hline
0 & 0.52 & 0.49 & 0.50 & 0.52 \\
0.5 & 0.54 & 0.51 & 0.56 & 0.53 \\
1 & 0.54 & 0.50 & 0.56 & 0.53 \\
1.5 & 0.53 & 0.51 & 0.54 & 0.53 \\
3 & 0.49 & & & 0.50 \\
6 & 0.48 & & & 0.49 \\
9 & 0.49 & & & 0.49 \\ \hline \end{tabular}
\end{table} 
Table 1: Quantum Yield of _A. platensis_ cultivated in the presence of glycerol.
## 3 The Design of GAF MIMO Antenna Array
### The Configuration of GAF MIMO Antenna Array  
The proposed GAF MIMO antenna has four independent and identical antenna elements. The distance (\(d\)) between the four antennas is 0.4\(\lambda\) (\(\lambda\) is the free space wavelength of 3.5 GHz), and the configuration is shown in Figure 3a.  
Due to symmetry, the performances of Antenna 1 and Antenna 2 can represent the entire GAF MIMO antenna array. The simulated S-parameters of Antenna 1 and Antenna 2 are depicted in Figure 3b,c. The operating frequency of the MIMO antenna array is slightly deflected due to the influence of mutual coupling. Moreover, the isolation of adjacent antennas is less than 20 dB at 3.5 GHz (\(\left.\right|\left.\right.\)\(\left.  
which is proved in Figure 4. The E-plane radiation patterns of Antenna 1 and Antenna 2 are not deflected, while the H-plane patterns deviated by 9\({}^{\circ}\) and 26\({}^{\circ}\), respectively.
## 5 Performance Evaluation
### Path Loss Variance  
For changing this parameter, we had to enhance the radio model in Cooja to support a zero-mean normal distribution. Figure 17(b) reveals that if the link qualities are fluctuating more (due to the environment characteristics) it gets more difficult to track the MN so SDMob shows a slight escalation in localization error.  
In Section 4, the degrading impact of path loss variance on reception ratio in the simulations was compared with the analytical evaluations in Figure 17(a). It is worth mentioning that although the reception ratio for control traffic is not affected much, the data traffic's PDR is about 10 percent less than control traffic. This can be explained by the fact that the RSSI fluctuations contribute to packet loss.  
Figure 16: Simulation scenarios with different mobility patterns: (**a**) with RDM mobility pattern and (**b**) with TLW mobility pattern.  
Figure 17: Simulation results for different mobility patterns with SDMob—(**a**) PDR; (**b**) localization error; and (**c**) end-to-end delay.
## 3 Structure-Aware Global Orientation Estimation System  
In this work, we built a SLAM framework based on semantic-aware structural information for estimating the robot's global orientation state, as shown in Figure 1. The framework takes a synthesized surround view image as the input and outputs a drift-free trajectory and a map consisting of marker lines. As we can see, the proposed SLAM system consists mainly of two procedures, structured semantic information extraction and orientation estimation. During the structured semantic information extraction stage, the passable space in the image is firstly extracted with the help of the semantic segmentation network model as a mask for feature extraction; thus, we can improve the computational efficiency of the system while retaining most of the effective information. Since the boundary of the passable area contains geometric information about the environment, it could also be converted into a point cloud representation by LiDAR, which effectively complements the visual feature point information. In addition, since the passable space contains rich pavement marking edges, by further using the distortion characteristics of the reverse-perspective projection process, the distortion edges could be effectively eliminated, and the hybrid edge information could be used for mapping and localization tasks. During the orientation estimation stage, based on the dominant orientation information, the global orientation of each frame can be preliminarily estimated without drift. In order to further improve the anti-interference performance, we built a local map and designed it to optimize the state factor in the local map using the global orientation error constraint. Finally, the estimation of the global orientation is obtained, and a line map that reflects the structure of the real scene can be reconstructed.
# Influence of Soil Characteristics on Wood Biodeterioration by Brown Rot Fungi
## 2 Materials and Methods
### Effect of Humic Acid Treatment on Leaching of Cu-Based Wood Preservatives  
The effect of humic acid (HA) on the impregnated wood stakes was investigated according to [PERSON] et al. [16] with the following modifications: wood specimens (2T \(\times\) 2R \(\times\) 8L cm\({}^{3}\)) were impregnated by vacuum-pressure according to EN 807 [33] with the following retention rates: 21.1 kg m\({}^{-3}\) CC (copper-chromium), 26.7 kg m\({}^{-3}\) CCB (copper-chromium-boron), 20.3 kg m\({}^{-3}\) Cu-HDO (Bis-(N-cyclohexyldiazeniumdioxy)-copper) and 22.6 kg m\({}^{-3}\) ACQ (Alkaline Copper Quaternary). After impregnation, the wood specimens were placed into Erlenmeyer flasks containing 0.000%, 0.020%, 0.005% of HA (Sigma-Aldrich) dissolved in 500 mL of deionized water. Three Erlenmeyer flasks for each combination of impregnated wood specimens and HA concentration were prepared. After 10 days immersion of wood specimens in each solution, the specimens were removed and the leaching of Cu into water was evaluated with inductively coupled plasma optical emission spectrometry (ICP-OES). Additionally, the Cu content in the wood specimens was measured before and after water exposure. For this purpose, 1 g of sawdust from each wood sample was extracted and mixed with 3 mL of 2% HNO\({}_{3}\) and 1 mL of 35% H\({}_{2}\)O\({}_{2}\). After 10 min digestion in the microwave at 500 Watt, the Cu content in solution was measured using ICP-OES which subsequently enabled calculation of Cu leaching from wood in %.
## 4 Experimental Results Analysis
### Algorithm Performance  
The simulation results for two representative task scenarios, presented in Table 2, reveal that MOPSO-h demonstrates superior performance compared to the other algorithms in terms of MMD and TLS, achieving a 25% improvement over traditional MOPSO. In comparison to the top-performing NSGA-III, MOPSO-h exhibits a 6% reduction in ETP. This can be attributed to MOPSO-h's adaptive hybrid mutation strategy, which balances breakthrough velocity and strike precision by spending more time adjusting attitudes under physical constraints, thereby slightly relaxing the requirement for flight time. In consideration of task difficulty and convergence time requirements, the parameters of MOPSO-h are meticulously designed, with the specific configurations presented in Table 1. The experimental results demonstrate that the algorithm achieves complete convergence within 200 generations, as illustrated in Figure 3. MOPSO-h exhibits effective convergence behavior, avoiding premature entrapment in local optima while maintaining a balance between global exploration and local exploitation. However, this approach comes with a trade-off: the algorithm's runtime is relatively lengthy, comparable to that of NSGA-III.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Model** & \multicolumn{2}{c}{**MOPSO-h**} \\ \hline
**Variable** & **Value** & **Variable** & **Value** \\ \hline \(\delta_{\epsilon_{\theta}}\) & 0.02 rad & \(I\) & 300 \\ \(\delta_{\epsilon^{\prime}_{\theta}}\) & 0.01 rad & \(N\) & 50 \\ \(\delta_{\epsilon^{\prime}_{\varphi}}\) & 0.01 rad & \(N_{A}\) & 10 \\ \(R\) & 6371 km & \(p\) & 0.9 \\ \(\delta_{\epsilon_{d}}\) & 1 m & \(a_{1}\) & 2 \\ \(n_{\max}\) & 15 g, g = 9.8 m/s\({}^{2}\) & \(a_{2}\) & 2 \\ \(V_{\max}\) & 2 Ma & \(a_{3}\) & \(U(-1,1)\) \\ \(D\) & 5 m & \(a_{HV}\) & 0.05 \\ \(F\) & 100 Hz & \(r_{1}\) & \(U(0,1)\) \\ \(N_{\max}\) & 60 s & \(r_{2}\) & \(U(0,1)\) \\ \(R_{\theta}\) & [0, 1200 m] & \(\delta_{0}\) & 0.1 \\ \(K_{2},K_{3},\ldots,K_{11}\) & [0, 10] & \(\Delta I\) & 5 \\ \hline \end{tabular}
\end{table}
Table 1: Parameter configurations of the simulation models and algorithms.  
Additionally, the performance gap between single-objective and multi-objective optimization algorithms is smaller than anticipated. The primary reasons may include: (1) the limited number of terminal trajectory optimization objectives for anti-ship missiles; and (2) the sufficient conditions adopted in single-objective optimization's objective function design, where the coupling relationships under different conditions are weak, facilitating the identification of global optima.
# New Detection Paradigms to Improve Wireless Sensor Network Performance under Jamming Attacks
## 3 Analysis of Detection Mechanism
### Scenario 3  
This scenario has a mitigation technique. There is a _control packet_ which is sent by a node based on the review of three parameters: CSMA retries of listening to the channel, packets retransmissions and energy consumption by the node. These three performance metrics are sent as information within the HELLO packet, which is sent when a node wants to know its neighbors. The thresholds of these parameters are calculated and compared with the same parameters of other nodes. This comparison of metrics depends on whether these nodes are directly connected so the metrics are compared with these neighbor nodes, or these parameters are obtained from the perspective of the collector node under an average value of the nodes in the same group of the topology. If a node notes an abnormal performance, while the node compares its performance metrics with those stored in its neighbor table, it can be thought as an \"attacked node\". Then, this node itself sends a control packet to its neighbors to get it out of their neighbor tables. Then, this node leaves from the neighbor tables of its neighbors with a flag on \"marked node\".  
The following algorithm describes the collaborative detection mechanisms. When a node leaves the routing tables there is a reconfiguration topology and a resilience effect of the network according to each routing protocol.