esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
As IoT services become richer, there is a growing demand for massive connectivity technologies to support simultaneous accesses from a large number of devices to a single BS. In order to fulfill the requirement, one can use NOMA schemes, which improve the connection density by allowing signals from multiple devices superimposed onto the shared resource. In this paper, we discussed the issues relating to performance degradation due to channel estimation errors in uplink NOMA systems. When pilot signals are superimposed onto the shared resource as well, and a large number of devices perform random accesses concurrently, the channels might not be accurately estimated even in high SNR environments. This paper proposed an uplink NOMA scheme, which can alleviate the performance degradation due to channel estimation errors. An optimized scheme assuming perfect channel estimation might not be the best with inaccurate channel estimates and channel estimation errors need to be considered for uplink NOMA schemes in order to support a large number of concurrent random accesses. There are a large number of variations in uplink NOMA systems and more rigorous theoretical analysis needs to be performed with diverse uplink NOMA systems in the future.  
Figure 5: BER before channel decoding with 20 active devices.  
Figure 6: FER with 20 active devices.  
**Funding:** This work was supported in part by the National Research Foundation of Korea(NRF) grant funded by the Korea government (MSIT) (No. 2016R1A2B1008953), in part by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2014-0-00282, Development of 5G Mobile Communication Technologies for Hyper-connected smart services), and in part by 'The Cross-Ministry Giga KOREA Project' grant funded by the Korea government (MSIT) (No. GK18S0400, Research and Development of Open 5G Reference Model).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Spatial and Channel Similarity-Aware Attention-Enhanced Network for Object Counting
## 1 Introduction
The high cost and difficulty of obtaining large-scale datasets hinder the development and application of object-counting methods, especially in scenarios where data collection is challenging. To address the limitations of current object-counting methods, this paper proposes a few-shot spatial and channel similarity-aware attention-enhanced network (SCSAEN). This network leverages similarity-matching architecture in few-shot object counting (FSC) as the foundational framework to reduce the complexity of sample annotation. By comparing the features of support and query images, this approach allows users to customize the objects of interest with only a small number of support images, making it well suited for object-counting tasks with limited training samples. To address the issue of object angle diversity, the network introduces an innovative spatial similarity-aware (SSA) module. This module performs multi-directional slicing of object features along rows and columns in the spatial dimension. By applying convolution operations and nonlinear activation functions, it constructs a spatial deep neural network to analyze and match object features from multiple angles. This enhances the network's feature extraction capability and improves performance in object-counting tasks. In addition, the network incorporates a channel attention enhancement (CAE) module to strengthen feature representation along the channel dimension. This module addresses the channel information discrepancies caused by object angle diversity by accurately calculating channel similarity and assigningappropriate weights. By emphasizing critical channel features, it enhances the network's ability to process objects with varying orientations effectively. The proposed approach provides a novel solution to the challenges of FSC and object angle diversity, improving counting performance and reducing annotation costs. In summary, the original contributions of this paper can be summarized as follows:  
* First, a few-shot spatial and channel similarity-aware attention-enhanced network (SCSAEN) is proposed. This network reduces the complexity of sample annotation by leveraging the similarity-matching architecture in few-shot object counting (FSC). * Second, the innovative spatial similarity-aware (SSA) module is introduced to handle object angle diversity, enhancing the feature extraction ability. * Third, the channel attention enhancement (CAE) module is incorporated to strengthen the feature representation along the channel dimension and improve the network's ability to process objects with different orientations.
# Investigations of Working Characteristics of Transferred Arc Plasma Torch Volume Reactor
## 1 Introduction
Consequently, the material to be treated must also be relatively electrically conductive or, if impermeable, should not completely cover the entire surface of the anode (certain defined reaction zones) in order to realize an electric arc discharge. This type of plasma device is widely used in metallurgy and in the neutralization processes of electrically conductive waste (e.g., various types of carbon and hazardous metal compounds, etc.). The cooling of this type of plasma device usually takes place using water, and the service life of the electrodes is 200-500 h. The operating capacity of these facilities is usually up to 6 MW. DC-type electric current is mostly often used for these devices to ensure operational stability [19; 20; 21; 22; 23]. As it was mentioned, both non-transferred arc torch and transferred arc torch type devices are cooled by water, which makes it possible to use heated water for heating of premises or electricity generation. The main disadvantage of a non-transferred arc torch is that the resulting plasma current is narrow, making it difficult to maintain a constant and sufficiently high enough temperature throughout the volume of the reaction zone. Meanwhile, in a transferred arc torch type plasma chemical reactor, the electric arc moves freely over the entire surface of the anode vessel, heating all its areas evenly. Since in this case the processing of the raw material takes place in the anode region, the temperature of the reaction zone is kept constant throughout the area [1; 2]. It has been observed that there is a lack of data in the scientific literature on the performance studies of transferred arc torch type reactors and their use for the neutralization of various wastes. Therefore, the aim of this work is to perform some studies related to plasma arc formation and characteristics in transferred arc torch type devices. Research will also be carried out to gain a better understanding of the conversion processes of various wastes or other feedstocks in this type of plasma chemical reactor. In addition, the novelty of this work is that the literature usually deals with this type of reactor when processing electrically conductive material. Meanwhile, the authors of this work used an electrically nonconductive material (hydroquinone with clay) to evaluate the plasma flow behavior and operation parameters of the reactor and their mutual influence. One of the tasks of this work was to show the versatility of this type of reactor with respect to the material being processed.
## 3 Results
### Comparison at the Sub-Catchment Scale
Therefore, all GPPs outperformed, particularly in SBs IV and V, whereas their low performance was observed at high elevated SBs, for example, III, II, and I, due to orographical effects (Figure 6, Table S3). However, during winter, the precipitation was lowest at a high elevation, such as SBs I and III. In contrast, higher precipitation amounts are recorded from mid to low elevation in SBs II, IV, and V. In addition, during the pre-monsoon season, all GPPs revealed higher average values of RMSE, BIAS, FAR, and lower CC, POD, and CSI compared with the monsoon, because these high elevated SBs are covered by ice and snow during the winter and partially in the pre-monsoon season. Consequently, the reflectivity of snow and ice influences the accuracy of GPPs in high-elevated regions. So, their performance was low in that season. Nevertheless, all GPPs tended to overestimate the light (1 mm/day) precipitation intensities, except ERA-5, which tended to underestimate the light but overestimated moderate (1-20 mm/day) and heavy precipitation (>20 mm/day) events. Among all GPPs, the MSWEP revealed an intimate relationship with gauge data in capturing the light, medium, and high precipitation intensities, suggesting it has the superior capability to detect precipitation events (0-50 mm/day) more precisely in all seasons. Figure 6: The performance of GPPs at SBs during pre-monsoon and monsoon seasons. The lines of each box from bottom to top represent the minimum, first quartile, median, third quartile, and maximum values, respectively. **(a)** Correlation coefficient, **(b)** Root Mean Square Error, **(c)** relative Bias, **(d)** Relative Bias in percentage, **(e)** Probability of Detection, **(f)** False Alarm Ratio, **(g)** Critical Success Index.
# Monitoring of Sugarcane Harvest in Brazil Based on Optical and SAR Data
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ANN & Artificial neural network \\ ASF & Alaska Satellite Facility \\ BRT & Brasilia Time \\ CART & Classification and Regression Trees \\ CNN & Convolutional neural networks \\ DT & Decision tree \\ ESA & European Space Agency \\ EVI & Enhanced Vegetation Index \\ GIS & Geoinformation system \\ GPM & Global Precipitation Measurement \\ GPS & Global Positioning System \\ GRU & gated recurrent unit \\ LSTM & Long short-term memory \\ MODIS & Moderate Resolution Imaging Spectroradiometer \\ MSI & Multispectral Instrument \\ NDVI & Normalized Difference Vegetation Index \\ NDWI & Normalized Difference Water Index \\ OBIA & Object based image analysis \\ OLI & Operational Land Imager \\ RBF & Radial basis function \\ RF & Random forest \\ RVI & Ratio Vegetation Index \\ RNN & Recurrent neural network \\ SAR & Synthetic Aperture Radar \\ SLC & Single Look Complex \\ SNAP & Sentinel Application Platform \\ SVM & Support vector machine \\ SWIR & Short-wave Infra-Red \\ UAV & Unmanned aerial vehicle \\ UTC & Coordinated Universal Time \\ VH & Vertical transmit and horizontal receive \\ VV & Vertical transmit and vertical receive \\ \end{tabular}
# Characteristic Volatile Composition of Seven Seaweeds from the Yellow Sea of China
## 2 Results and Discussion
### Headspace VOC Composition of Saweeds
#### 2.1.1 Headspace VOC Composition of Rhodophyta  
There were 34, 19 and 18 compounds identified from _Gratelcupia filicina, Polysiphonia senticulosa_ and _Callithamnion corymbosum_, respectively (Table 1), and, among them, four compounds were common to all three species, including two norisoprenoids (\(\beta\)-cyclocitral and (\(E\))-\(\beta\)-ionone), one alkene (3,5,5-trimethyl-1-hexene) and one alkene (heptadecane). This is the first report on the headspace VOC composition for these three red algae species. The VOCs of _G. filicina_ exhibited the most chemical diversity, with the most abundant being pentadecanal (20.11% in polydimethylsiloxane (PDMS) fiber), tridecanal (19.20% in PDMS fiber), 1-hexen-3-ol (19.58% in divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) fiber) and 1-octen-3-ol (18.72% in DVB/CAR/PDMS fiber). [PERSON] et al. [17] analyzed the semi-volatile organic compounds (SVOCs) of _G. filicina_ using ethyl acetate as an extraction solvent and found that the main SVOCs were aldehydes, fatty acids and alcohols, with pentadecanal having the highest concentration. Our VOC  
Figure 1: The number of volatile organic compound (VOC) categories identified in seaweeds GF—_Gratelcupia filicina,_ PS—_Polysiphonia senticulosa,_ CC—_Callithamnion corymbosum_, ST—_Sargassum thunbergii,_ DD—_Dictyota dichotoma,_ EP—_Enteromorpha prolifera_ and UL—_Ulva lactuca_. analysis results are consistent with the results for SVOCs in the literature, suggesting that aldehydes play an important role in the metabolism of _G. filicina_. Both _P. senticulosa_ and _C. corymbsum_ are from the same taxonomical order, Ceramiales Oltmanns and were found to contain two common compounds, 5-methyl-1-undecene and 1-undecyne, which were not found in _G. filicina_. Moreover, ectocarpene (21.83% in DVB/CAR/PDMS fiber; 14.71% in PDMS fiber) was a uniquely abundant compound in _P. senticulosa_. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**NO.
## 3 Proposed Scheme
### Training Stage
#### 3.1.2 Network Architecture  
The purpose of our work is to design a new perceptual hash scheme based on deep learning, which can realize integrity authentication of HRRS images based on robust edge feature. Hence, we use a modified U-net model to extract the robust edge feature of the images. In this study, the U-net architecture is slightly different from the original U-net [33] as follows.  
1. Exponential linear unit (ELU) is applied to replace rectified linear unit (ReLU) used by the original U-net model as activation function, which improves the performance of the network, resulting in more robust and accurate edge feature extraction.
2. Batch normalization (BN) is used in this network to accelerate the convergence. For every mini-batch, BN normalizes the inputs using the mini-batch mean/variance and de-normalizes them with a learned scaling factor and bias. It significantly reduces the internal and external variances in various attributes, and it improves the performance of the network [39; 40; 41].  
Figure 5 shows the architecture of the modified U-net model for robust feature extraction, which consists of a contracting path and a symmetric expanding path. The contracting path consists of repeated application of two \(3\times 3\) convolutions (unpadded convolutions), each followed by one \(2\times 2\) max pooling operation with stride 2 for down-sampling, and one batch normalization (BN) for convergence acceleration. The expansive path consists of an up-sampling of the feature map followed by an up convolution (\(2\times 2\)) with stride 2 and two \(3\times 3\) convolutions with stride 1 in which each is also followed by batch normalization. At the same time, the correspondingly cropped feature from the contracting path will be concatenated. As exponential linear unit (ELU) is beneficial for learning representations more robust to noise [41], we leveraged ELU rather than ReLU as the primary activation function during training.  
What is more, as the detection of edge features is essentially a binary classification problem of image pixels, that is, the pixels are either points on the edge or not, we choose binary cross-entropy as the loss function for binary classification in the model.
## 4 UAVs and Artificial Intelligence
### AI and UAVs: Breakthroughs and Crossovers
This allows an intuitive human-drone communication, a delegation of high-level goals rather than step-by-step directions, and fluid handoffs of control between automated and manual modes [188]. AI facilitates a human-drone team coordination similar to human teams. **Swarm Coordination:** For drone swarms, AI enables a decentralized coordination between units to achieve collective goals. Algorithms allow large-scale swarms to cooperatively survey areas, navigate tightly in formation, dynamically self-organize, and exhibit collective intelligence [189]. More advanced swarming AI leverages emergent intelligence principles to complete the global objectives. Each drone follows the rules for assessing its environment and self-organizing based on the distributed feedback. This opens applications ranging from distributed sensing to impressive aerial displays. **Mission Planning:** AI planning and reasoning algorithms empower drones to translate high-level mission objectives into customized, optimized flight plans accounting for the conditions [190]. Automated mission planning with contingencies reduces the operator workload. During missions, drones can utilize AI algorithms to adjust the routes and actions based on the sensor data. **Air Combat:** In simulated dogfights, AI-controlled drones have defeated human pilots using maneuvering and tactics learned through deep reinforcement learning [191]. While still nascent, AI can eventually revolutionize aerial combat by outthinking human opponents. Drone dogfighting offers a proving ground for advanced air combat AI. As the AI research accelerates, UAVs are poised to benefit immensely from embedded intelligence, making them smarter, safer, and more useful across industrial, civil, and defense domains.
# High-Performance Computing and Parallel Algorithms for Urban Water Demand Forecasting
## 1 Introduction  
Urban water management is increasingly challenged by rapid urbanization, climate variability, and growing water demand, necessitating accurate forecasting methods for effective resource allocation [1]. Traditional forecasting techniques, such as autoregressive integrated moving average (ARIMA) and multiple regression models, have been widely applied but often fail to capture complex, non-linear consumption patterns, particularly in dynamic urban environments [2, 3]. The availability of high-resolution temporal data has led to the adoption of advanced machine learning (ML) and deep learning (DL) models, which have demonstrated superior capabilities in modeling intricate temporal dependencies.  
Among these, long short-term memory (LSTM) networks have gained prominence due to their ability to retain long-range dependencies and capture sequential patterns in time-series forecasting [4]. Hybrid models, such as convolutional neural networks (CNN) integrated with LSTMs, have further improved multivariate forecasting accuracy by leveraging both spatial and temporal dependencies [5]. Additionally, attention-enhanced bidirectional LSTMs (Bi-LSTMs) with XGBoost-based residual correction have shown success in improving short-term urban water demand prediction [6]. Generative adversarial networks (GANs) have also been explored to enhance forecasting accuracy, particularly in scenarios with limited training data [7].  
Despite these advancements, the computational complexity of deep learning models presents scalability challenges, especially in large urban water networks where models require frequent updates and must process high-velocity data streams. The need for scalable, efficient computation has led to increased interest in high-performance computing (HPC) frameworks that optimize resource utilization while ensuring timely forecasting [5].
## 3 Results
### Incremental Finetuning Using a Mixed Data Set  
Results of re-training are provided in Table 3. The highest improvement for mIoU was achieved for both data sets for the batch size b = 100%. By including BAGLS data in the re-training process, catastrophic forgetting could be avoided. However, there was also a smaller increase of mIoU for BAGLS-RT as compared with the incremental finetuning, indicating that including BAGLS training data also hindered better adaption of the models towards BAGLS-RT data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Training with**} & \multicolumn{2}{c}{**mIoU**} & \multicolumn{2}{c}{**\(\Delta\)mIoU (Baseline)**} \\ \cline{2-5}  & **BAGLS** & **BAGLS-RT** & **BAGLS** & **BAGLS-RT** \\ \hline Baseline & 0.7642 & 0.7354 & - & - \\ \hline b = 25\% & 0.7763 \(\pm\) 0.0056 & 0.7449 \(\pm\) 0.0019 & 1.21\% & 0.95\% \\ \hline b = 50\% & 0.7740 \(\pm\) 0.0032 & 0.7504 \(\pm\) 0.0099 & 0.98\% & 1.50\% \\ \hline b = 75\% & **0.7768 \(\pm\) 0.0031** & 0.7497 \(\pm\) 0.0025 & **1.26**\% & 1.43\% \\ \hline b = 100\% & 0.7767 \(\pm\) 0.0043 & **0.7520 \(\pm\) 0.0057** & 1.25\% & **1.66**\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Segmentation performance of the U-Net after re-training from scratch on the corresponding test data of BAGLS and BAGLS-RT.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Training with**} & \multicolumn{2}{c}{**mIoU**} & \multicolumn{2}{c}{**\(\Delta\)mIoU (Baseline)**} \\ \cline{2-5}  & **BAGLS** & **BAGLS-RT** & **BAGLS** & **BAGLS-RT** \\ \hline b = 25\% & 0.7495 \(\pm\) 0.0025 & 0.7597 \(\pm\) 0.0011 & \(-\)1.47\% & 2.43\% \\ b = 50\% & 0.7514 \(\pm\) 0.0042 & 0.7609 \(\pm\) 0.0029 & \(-\)1.28\% & 2.55\% \\ b = 100\% & 0.7563 \(\pm\) 0.0055 & 0.7571 \(\pm\) 0.0029 & \(-\)0.71\% & 2.17\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Segmentation performance of the U-Net after incremental finetuning with BAGLS-RT.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Training with**} & \multicolumn{2}{c}{**mIoU**} & \multicolumn{2}{c}{**\(\Delta\)mIoU (Baseline)**} \\ \cline{2-5}  & **BAGLS** & **BAGLS-RT** & **BAGLS** & **BAGLS-RT** \\ \hline b = 25\% & 0.7694 \(\pm\) 0.0079 & 0.7498 \(\pm\) 0.0020 & 0.52\% & 1.44\% \\ b = 50\% & 0.7644 \(\pm\) 0.0051 & 0.7491 \(\pm\) 0.0016 & 0.02\% & 1.37\% \\ b = 100\% & **0.7715 \(\pm\) 0.0035** & **0.7520 \(\pm\) 0.0011** & **0.73\%** & **
## 4 Case Study of an IoT Enabled Bridge
### Cyber-Physical Attack Scenarios against the IoT Based WSN
The abovementioned cyber-physical attack is composed of four scenarios, that reflect the application of different control barriers in cyber and physical space in the IoT enabled bridge sensing area. The existence of control barriers reduces the decision score of Attack vector (X\({}_{\text{AV}}\)) and Attack complexity (X\({}_{\text{AC}}\)). The control barriers are applied to prevent, detect or respond to the energy depletion attack. Scenario 1--No control barriers. The threat source infiltrates the physical area of the IoT based WSN, launching the attack against the victim ZigBee devices. Physical accessibility to the IoT WSN increases the score of X\({}_{\text{AV}}\). Due to the lack of control barriers in cyber space, the threat source can consume the energy of ZigBee devices without carrying out additional steps. This increases the score of X\({}_{\text{AC}}\). Scenario 2--Physical control barriers only. Physical control barriers, such as intelligent video-surveillance systems, motion detectors or line crossing [75,76], can detect or prevent physical access of the threat source to the IoT based WSN. The presence of such control barriers should reduce the score assigned to X\({}_{\text{AV}}\). However, if the threat source has sufficient knowledge, motivation, resources, and terrorism experience to overcome the physical control barriers, energy of IoT devices can still be depleted. Scenario 3--Cyber control barriers only. The threat source can infiltrate the physical area of the IoT based WSN, as discussed in Scenario 1. Cyber control barriers increase the steps that should be carried out by the threat source to exploit the vulnerability and reduce the level of Attack complexity (X\({}_{\text{AC}}\)). An example of the cyber control barrier that could prevent the threat source from sending bogus messages is the development of a blacklist of misbehaving devices [56]. A cyber control barrier that could detect the energy depletion attack is the application called Intrusion Detection Systems, which could detect known and new types of attacks against ZigBee devices and alert operators when malicious actions are detected [77]. Scenario 4--Integrated Control barriers (ICBs). These control barriers are a combination of the physical and cyber control barriers described in Scenarios 2 and 3, respectively.