esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study
### IEEE RTS-24 Node Case Study System
#### 4.2.3 Analysis of the Impact of Different Planning Methods  
In order to demonstrate the effectiveness of the proposed method in reducing system costs and improving renewable energy acceptance capacity, this section analyzes the planning results in Section 4.2.2. Compared to the planning results of the single-step static method proposed in the literature [21], the total cost of the multi-stage collaborative planning scheme of transmission network and energy storage is reduced by 950 million yuan,  
Figure 4: Planning scheme of transmission network and energy storage in different stages.  
which is about 30.4%. This is because the transmission line construction and energy storage location determination in the single-step static collaborative planning scheme of transmission grid and energy storage are only carried out in the first planning stage, so compared with the multi-stage collaborative planning scheme of transmission grid and energy storage, it cannot enjoy the benefits of energy storage cost reduction due to technological update, and it cannot play the time value of funds. From the comparison of Tables 2 and 3, It can be found that compared to the single grid planning approach described in the literature [14; 15], the multi-stage coordinated expansion planning scheme of the transmission network and energy storage mainly differs in the number of new lines in the third stage. The total cost of line construction has been reduced by 54 million, reflecting the role of energy storage in delaying the upgrade and transformation of transmission lines. The total investment cost has been reduced by 184 million, saving 7.79%, indicating that the multi-stage coordinated expansion planning scheme of transmission network and energy storage has better economy. In terms of the punishment for new energy curtailment, from Tables 1 and 2, it can be seen that the multi-stage coordinated expansion planning scheme of transmission network and energy storage greatly reduces the system's curtailment punishment costs compared with the single transmission network multi-stage planning scheme. Among them, the total wind curtailment punishment cost has been reduced from 1.789 billion to 1.009 billion, and the total solar curtailment punishment cost has been reduced from 74 million to 14 million, indicating that the coordinated expansion planning of transmission network and energy storage can not only alleviate transmission congestion and delay the upgrade and transformation of transmission lines, but also reduce the cost of new energy curtailment and increase the consumption of new energy.
## 2 Materials and Methods
### Field Experiments and Plant Sampling  
A one-year field experiment was carried out during the May-October 2018 growing season at the Agricola Scipioni field (Avezzano, Italy, 42\({}^{\circ}\)02\({}^{\prime}\)26.31\" N, 13\({}^{\circ}\)46\({}^{\prime}\)45.02\" E, 650 m a.s.l.). The climate is relatively continental, with mean annual precipitation ranges of 650-800 mm. The experiments were arranged on a randomized complete block designed with four replicates. The treatments under comparison were represented by seeds inoculated with bacteria (SIB) and autoclaved bacterial application (control). Carrots were sown on June 2018 with a pneumatic seeder (Serie SNT, Agricola Italiana SNC, Massanzago, PD, Italy) at a rate of 250 seeds m\({}^{2}\). The previous crop was potatoes (_Solanum tuberosum_ L.), which were harvested in the middle of October 2017. The soil was previously fallow and was plugughed and narrowed twice before sowing.  
The experimental units consisted of a 30 m\({}^{2}\) (5.0 m \(\times\) 6.0 m) plots with 5 plant tandem rows spaced 5 cm apart. The crop was only protected against fungal disease through the application of Azoxystrobin (Amistar, Syngenta Italia, Milano, MI, USA) at the dose of 0.8 L ha\({}^{-1}\) at development stage 45 [37]. Starting from the development stage 41 (roots beginning to expand, diameter \(>\) 0.5 cm), 30 carrot plant samples within each experimental unit were randomly collected. In total, 3 sampling dates, corresponding to development stage 41, development stage 45 (50% of the expected root diameter reached), and harvest (development stage 49 [37], middle October 2018) were considered. Plants were separated into the aerial part and carrots root (referred as 'roots') and their dry matter (%) were determined after oven drying at 80 \({}^{\circ}\)C, until constant weight was obtained. The estimation of carotenoids (roots) and chlorophylls (aerial parts) contents were carried out on 80% acetone extracts and obtained following the methods and calculations proposed by [PERSON] et al. [38] and [PERSON] et al. [39], respectively. Results were expressed as mg 100 g\({}^{-1}\) FW.
# xLSTM-Based Urban Traffic Flow Prediction for Intelligent Transportation Governance
## 2 Materials and Methods  
Traffic forecasting is a foundational element of intelligent transportation systems (ITSs), enabling congestion management, route optimization, and emergency response. Traditional statistical models such as ARIMA, Kalman filters, and support vector regression offer baseline temporal prediction capabilities but often fail to capture the nonlinear and high-dimensional nature of urban traffic dynamics [5,16]. With the rise in deep learning, Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) models have become prevalent due to their effectiveness in modeling temporal dependencies. LSTM, in particular, addresses issues of long-range memory and vanishing gradients, making it suitable for sequence-based traffic data. However, conventional LSTM architectures remain limited in large-scale urban contexts, where complex spatial dependencies, irregular congestion patterns, and vehicle heterogeneity pose additional modeling challenges. Recent advances such as graph neural networks (GNNs), attention mechanisms, and Transformer-based models have been developed to address these issues, although they often incur significant computational costs--making them less practical for real-time applications like police dispatch systems that require fast, low-latency inference. Moreover, while predictive analytics has seen success in crime hotspot detection, its integration with traffic forecasting for real-time police deployment remains underexplored. Current dispatch systems largely operate reactively, based on emergency calls or field reports, which delays response and undermines resource efficiency [5,6,13,28].  
The xLSTM model, first introduced by [PERSON] et al. [8], offers a promising solution. By incorporating matrix-based memory cells and exponential gating mechanisms, xLSTM enhances spatial-temporal representation, increases adaptability to non-stationary traffic patterns, and improves long-range learning. These innovations make it particularly suitable for congestion-aware and operationally scalable applications in urban governance. In Taiwan, where high vehicle density, limited public transit use, and dispersed urban development intensify congestion, such predictive models are especially relevant. Yet, prior research has rarely addressed the combined challenge of real-time traffic forecasting and tactical emergency coordination. This study seeks to bridge that gap by applying the xLSTM architecture to urban traffic flow prediction and integrating its outputs into police dispatch protocols--delivering a validated, data-driven approach to intelligent transportation governance [10].
## 2 Modeling of IES
### HCNG-CCS-P2G IES Mathematical Model  
#### 2.1.1 Hcng-CCS-P2G Coupled Devices  
The HCNG-CCS-P2G system can be divided into two parts: HCNG-CCS and P2G. The HCNG-CCS part is coupled through a Combined Heat and Power (CHP) unit. Hydrogen and natural gas are mixed in a time-varying volumetric ratio to form HCNG, serving as a cleaner fuel for the CHP unit. The carbon emissions produced by combustion are processed by the CCS equipment, as shown in the upper half of Figure 2. This coupling structure efficiently utilizes the gas CO\({}_{2}\), improving carbon reduction, tightening the coupling of various energy sources, and reducing energy losses.  
Figure 1: System structure.  
Figure 2: HCNG-CCS-P2G coupling mechanism.  
The expression of the HCNG-CHP model is as follows:  
\[\begin{cases}r(t)=\frac{T_{\text{chp}}(t)/L_{\text{t}}}{T_{\text{chp}}(t)/L_{\text {t}}+G_{\text{chp}}(t)/L_{\text{g}}}\times 100\%\\ L_{\text{mix}}(t)=r(t)L_{\text{t}}+[1-r(t)]L_{\text{g}}\\ M_{\text{chp}}(t)=L_{\text{mix}}(t)\Big{[}T_{\text{chp}}(t)/L_{\text{t}}+G_{ \text{chp}}(t)/L_{\text{g}}\Big{]}\end{cases} \tag{1}\]  
where \(L_{\text{mix}}(t)\) is the equivalent calorific value of the mixed gas at time \(t\). \(M_{\text{chp}}(t)\) is the mixed gas power of the CHP at time \(t\).  
Considering HCNG, the output power and capacity constraints of electricity and heat from CHP can be expressed as follows:  
\[\begin{cases}P_{\text{chp}}(t)=\eta_{\text{wt}}M_{\text{chp}}(t)\\ H_{\text{chp}}(t)=(1-\eta_{\text{wt}}-\eta_{\text{loss}})M_{\text{chp}}(t)\end{cases} \tag{2}\]  
The modeling of CCS equipment is as follows:  
\[\begin{cases}\Omega_{\text{a,chp}}(t)=\beta_{\text{h}}\Big{[}\varphi_{\text{e, h}}P_{\text{chp}}(t)+H_{\text{chp}}(t)\Big{]}\\ P_{\text{ccs}}(t)=\beta_{\text{c}}\Omega_{\text{a,chp}}(t)\\ \Omega_{\text{a,ccs}}(t)=\eta_{\text{ccs}}\Omega_{\text{a,chp}}(t)\end{cases} \tag{3}\]  
The P2G configuration constitutes another key component of the HCNG-CCS-P2G system, comprising an electrolyzer (EL), a methanation reactor (MR), and a Hydrogen Fuel Cell (HFC). The EL employs grid electricity to perform water electrolysis for hydrogen production. The generated hydrogen is then directed to the MR, where it reacts chemically with the CO\({}_{2}\) captured by the CCS equipment from CHP emissions, resulting in the production of synthetic natural gas. Additionally, a fraction of the hydrogen produced by the EL can be utilized as fuel for the HFC, converting it into electrical and thermal energy, as illustrated in the lower part of Figure 2. The mathematical models of the three devices are as follows:  
\[T_{\text{el}}(t)=\eta_{\text{el}}P_{\text{el}}(t) \tag{4}\]  
\[G_{\text{mr}}(t)=\eta_{\text{mr}}T_{\text{mr}}(t) \tag{5}\]  
\[\begin{cases}P_{\text{hfc}}(t)=\eta_{\text{e,hfc}}T_{\text{hfc}}(t)\\ H_{\text{hfc}}(t)=\eta_{\text{h,hfc}}T_{\text{hfc}}(t)\end{cases} \tag{6}\]
# Adaptive Allocation Algorithm for Multi-Radio Multi-Channel Wireless Mesh Networks
###### Abstract  
The wireless mesh network (WMN) has proven to be a great choice for network communication technology. WMNs are composed of access points (APs) that are installed and communicate with each other through multi-hop wireless networks. One or more of these APs acts as a gateway (GW) to the internet. Hosts of WMNs are stationary or mobile. According to the structure of WMNs, some network features may be affected, such as the overall performance, channel interference, and AP connectivity. In this paper, we propose a new adaptive channel allocation algorithm for a multi-radio multi-channel wireless mesh network. The algorithm is aimed to minimize the number of channel reassignments while maximizing the performance under practical constraints. The algorithm defines a decision function for the channel reassignments. The decision function aims to minimize the traffic around the GW. Whenever the traffic changes in the wireless mesh network, the decision function decides which channel radio reassignment should be done. We demonstrated the effectiveness of our algorithm through extensive simulations using Network Simulator 2 (NS-2).  
a _Article_  
Adaptive Allocation Algorithm for Multi-Radio Multi-Channel Wireless Mesh Networks
# Revolution or Evolution? Technical Requirements and Considerations towards 6G Mobile Communications
## 5 6G RF Spectrum Technologies
### THz Spectrum  
THz waves are electromagnetic waves with a spectrum that ranges from 0.1 to 10 THz (licensed spectrum) and a wavelength that ranges from 30 to 3000 microns. The spectrum falls within the transition region between macro-electronics and micro-photonics, specifically between microwave (from its lower band) and infrared light (up to its higher band) [55]. Although the frequency band (which ranges from microwave to optical wave) for THz may not be fully developed, THz communication exhibits the strengths of rich spectrum resources and high transmission rate, which offer highly valuable broadband wireless access for future mobile communications [56]. Additionally, THz communication is linked to an extensive bandwidth range (up to 10 THz). Apart from its capacity to  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Characteristic** & **5G** & **6G** \\ \hline \hline \multicolumn{2}{c}{Haptic communication} & Partial & Fully \\ \hline \multicolumn{2}{c}{Smart city components} & Separated & Integrated \\ \hline \multicolumn{2}{c}{IRS} & - & Yes \\ \hline \multicolumn{2}{c}{Standards} & 5G/NR & - \\ \hline \multicolumn{2}{c}{Core network} & IoT & IoE \\ \hline \multicolumn{2}{c}{HetNets} & Flexible & Ultra-flexible \\ \hline \multicolumn{2}{c}{Usage scenarios} & EMBB, URLLC \& mMTC & uMUB, uHSLLC \& uHDD \\ \hline \multicolumn{2}{c}{Main technologies} & mmWave, mMIMO, UDN, SDN & THz, SM-MIMO, Laser and VLC, Quantum, Blockchain, AI/ML \\ \hline \multicolumn{2}{c}{
\begin{tabular}{c} \\ \end{tabular} } & VR/AR/360\({}^{\circ}\) videos, UHD videos, V2X, IoT, Smart city/factory/home, telemedicine, and wearable devices & Holographic, tactile/haptic internet, full-sensory and reality, fully automated driving, industrial internet, space travel, deep-sea sightseeing, and Internet of bio-nano-things \\ \hline \multicolumn{2}{c}{Flexible spectrum} & Flexible duplex & Free duplex \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 3: 6G spectrum technologies.  
penetrate visually non-transparent objects, THz communication also enables interactions of micro-scale objects (e.g., nanonetwork) due to its scale-down antennas (\(\lambda\) of about 1 mm for 300 GHz). Figure 4 illustrates the spectrum bands for the generations of 4G to 6G, including mmWaves, THz, and VLC bands.
# An Audio Watermarking Algorithm Based on Adversarial Perturbation +
## 3 The Proposed Methodology
### Using Pre-Trained Module for Transformations
#### 3.2.1 Feature Extractor in a Classification Network  
We trained an audio classification network. The part before its linear layer is employed is the deep feature extractor \(f_{cf}:\mathcal{A}\rightarrow\mathcal{F}\), where \(\mathcal{A}\) is denoted as the audio domain and \(\mathcal{F}\) as the feature space. The network structure is shown in Figure 3, where the feature extractor is in the dashed box.  
The input audio is first fed into the differentiable Mel frequency cepstrum coefficient (MFCC) layer, followed by 16 residual blocks. The outputs of each residual block are passed to a SUM module by skip connection. These outputs are accumulated into a feature map and passed through two \(1\times 1\) convolution layers and a rectified linear unit (ReLU) and finally through a mean pooling layer to obtain a 512-dimensional feature. This deep feature will predict the audio as belonging to different categories after passing through a linear layer and softmax layer.  
The structure of the ResBlock is shown in Figure 4. Each ResBlock contains a highway connection and a branch containing two convolution layers, the second of which is with a \(1\times 1\) convolution kernel, whose output is passed to the SUM module. The first convolutional layer is followed by a gated activation function, as  
\[g(x)=\tanh(W_{1}x)\odot\text{sigmoid}(W_{2}x) \tag{1}\]  
where \(W_{1}\) and \(W_{2}\) are learnable parameters and \(\odot\) means the Hadamard product, as it is shown that this activation function is more suitable for audio feature extraction [53].  
Figure 3: Block diagram of the classification network. The part in the dashed box is the deep feature extractor \(f_{cf}\). This structure is also used for feature extraction based on contrastive learning.  
The feature extractor \(f_{cf}\) should meet two properties: (1) \(f_{cf}(\mathbf{x})\) will change as the input audio \(\mathbf{x}(n)\) is perturbed imperceptibly to achieve the watermark embedding; and (2) the extracted features for different augmented forms of one audio should be as consistent as possible. Since the extractor \(f_{t}\) we use is differentiable, information embedding can be achieved by adding perturbations to the audio via backpropagation. A reasonable solution for satisfying feature invariance is to train the \(f_{cf}\) using augmentations of training data.  
**Data augmentation during training**: Randomized data augmentation can be performed on the audio, and the transformations involved are shown in Table 1. The audio in the dataset is transformed and fed into the network for training to produce a transform-insensitive feature extractor.
# Accumulative Roll Bonding--A Review
## 3 Parameters  
ARB is affected by numerous parameters from which material type, surface characteristics as well as rolling conditions are adjudged as most important. The present section identifies four distinct parameters having both significant effects on bonding quality as well as being readily controllable in practical conditions.
# Rethinking Learnable Proposals for Graphical Object Detection in Scanned Document Images
## 4 Experimental Results
### Result and Discussion
#### 4.4.3 Cross-Dataset Evaluation  
Deep learning methods for object detection in document images are preferred over rule-based methods due to the fact that these methods generalize better over different and distinct datasets. To investigate its generalization capabilities, the SR-CNN model is evaluated over three datasets: IIIT-AR-13k, PubLayNet, and DocBank. Only the common classes among the datasets were utilized for the cross-dataset evaluation. Baseline Faster R-CNN is also evaluated for comparison. The results are presented in Table 11.  
Table 11 shows that the SR-CNN model gives a mean average precision of 0.56 and 0.45 on the PubLayNet and DocBank, respectively, when trained on the IIIT-AR-13K dataset. This may be because IIIT-AR-13K has a diverse collection of real-world images of company reports, which allows the model to learn a more general representation of document images and performs reasonably well on the much simpler dataset with document images of academic articles.  
When the SR-CNN model is trained on the PubLayNet, it gives 0.510 and 0.323 mean average precision on the DocBank and IIIT-AR-13K datasets. The model's performance suffers on the IIIT-AR-13K dataset as the model is trained on a dataset that contains primarily academic and research articles. The IIIT-AR-13K offers an entirely different distribution of document images. However, when tested on the DocBank dataset, which consists mainly of document images from the academic domain, better performance is observed than for IIIT-AR-13k.  
Finally, when the model is trained on DocBank and tested on IIIT-AR-13K and PubLayNet, it gives a mean average precision of 0.184 and 0.735 on the IIIT-AR-13k and PubLayNet, respectively. Hence, it can be inferred that the distribution of document images in the DocBank and PubLayNet datasets is similar. Therefore, the model achieves relatively good results when trained on one dataset and tested on the other.
## 2 Materials and Methods
### Physical Characterization  
The crystal analysis of the NiSb\({}_{2}\)O\({}_{6}\) powders was performed at 700, 800, and 900 \({}^{\circ}\)C through X-ray powder diffraction (Panalytical Empyream). Cu\(\alpha\) radiation (\(\lambda\) = 1.5406 A) was used, scanning from 10 to 90\({}^{\circ}\) at a rate of 0.02\({}^{\circ}\)/second. To determine the forbidden band gap of the calcined oxide powders at 700, 800, and 900 \({}^{\circ}\)C, a spectrophotometer (UV-Vis-NIR, model UV-3600 Plus) was used coupled to a deuterium and tungsten-halogen lamp. The absorbance spectra were taken in a range of 200 to 800 nm. The microstructure was analyzed with field-emission scanning electron microscopy (FE-SEM, Tescan MIRA 3 LMU) with an acceleration voltage of 10 kV in a high vacuum.
# Can Anaerobically Digested Food Effluent Support _Arthrospira platensis_ Cultivation in Open Ponds?
## 1 Introduction
Despite its potential, there is limited knowledge on the use of ADFE as a nutrient source for Spirulina cultivation. Anaerobic digestates have been explored as alternative culture media for Spirulina cultivation, with previous studies demonstrating their potential to support microalgal growth under specific conditions [24]. However, the unique composition of ADFE and its effects on Spirulina remain largely unexplored. Unlike other digestates, ADFE may have distinct nutrient compositions and alkalinity levels, necessitating further investigation into its suitability as a substitute for synthetic culture media. This study aims to assess the potential of ADFE to replace up to 70% of Zarrouk medium nutrients for Spirulina cultivation, with a focus on growth performance, biomass productivity, and chlorophyll a content under outdoor conditions.
# Dynamic Simulation Model and Performance Optimization of a Pressurized Pulsed Water Jet Device
## 3 Simulation of Dynamic Characteristics of the PPWJ Device
### Numerical Model Verification  
The simulation results were compared with the experimental data from reference [35] under different operational parameters, and the corresponding results are illustrated in Figure 6. The curves of inlet pressure, jet pressure, and outlet pressure profiles observed in the experiment corresponded closely with the simulation results, thereby confirming the accuracy of the simulation. The experimental results show that in the initial stage of jet pressurization, i.e., the stroke acceleration stage of the piston, the actual pulse pressure curves fluctuated within a certain range. In this process, the system transformed from a transient state to a steady state, and the system load changed violently, which leads to the vibration of the valve core of the relief valve and causes the oil inlet pressure to fluctuate. The pressure fluctuation gradually decreased when the system transformed from a transient state to a steady state. However, the relief valve model provided by the simulation software library is too simple to truly reflect the transient process of the sudden changes in pressure and flow rate. The transformation process is so short that the local pressure fluctuation has little effect on the overall trend of the curves, and the influence of valve core vibration was ignored in the simulation model. Therefore, the pressure curves obtained by the simulation were relatively smooth.  
Figure 6a,b illustrates some of the results at maximum throttle opening and varying oil inlet pressures. Increasing the oil inlet pressure not only amplifies the pulse pressure but also accelerates the backward stroke speed of the piston, leading to a reduction in the reciprocation period and an increase in pulse frequency. The diameter and length of the oil-return pipeline result in the occurrence of a certain outlet pressure during the backward stroke stage of the piston, while the outlet pressure in the stroke stage of the piston is nearly zero. Figure 6c,d illustrates some of the results at constant oil inlet pressure and different throttle openings. As the throttle openings decrease, the piston's stroke speed remains constant, but the backward stroke diminishes. Consequently, the backward stroke time of the piston increases, resulting in a decrease in pulse frequency.  
Figure 5: The operational state curves of the PPWJ device: (**a**) displacement curves of the valve core and piston, (**b**) piston velocity and jet pressure curves.