esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Caching with Graph Neural Network-Based Deep Reinforcement Learning on SDN-Based ICN
## 5 Experimentation and Results
### Effect of Network Topology  
To further evaluate the effectiveness of the proposed caching scheme, we conducted experiments on different network topologies, namely ROCKETFUEL [18], TISCALI [19], and GARR [19]. The aim was to assess the robustness of the caching scheme in diverse network environments.  
Figure 3: The cache performance of GNN-DDQN, MLP-DDQN, LCD, PROB_CACHE, LCE, and CL4M varied with the number of content items in the GEANT network.  
Table 5 presents the distribution of each network topology's source, router, and receiver nodes. It is important to note that, in the TISCALI network, only router nodes with a degree higher than 6 possess caching capabilities, resulting in 36 router nodes equipped with cache functionality.  
This section evaluates the caching performance of different strategies in the ROCKETFUEL, TISCALI, and GARR network topologies. The experiments were conducted with an item number of 1000, and all routers with caching capabilities had a uniform cache size of one item. The results are summarized in Table 6.  
Across all network topologies, GNN-DDQN consistently outperformed the other strategies. Specifically, in ROCKETFUEL, GNN-DDQN achieved a 2.89% higher CHR than MLP-DDQN. In TISCALI, the margin became even more significant, with GNN-DDQN achieving a 25.72% higher CHR than MLP-DDQN. These results highlight the superior caching performance of GNN-DDQN, particularly in large networks such as ROCKETFUEL and TISCALI.  
Furthermore, GNN-DDQN demonstrated a significant margin over MLP-DDQN and other traditional caching schemes in the GARR network. This further emphasizes the robustness and effectiveness of GNN-DDQN across various network topologies.  
Figure 4: The cache performance of GNN-DDQN, MLP-DDQN, LCD, PROB_CACHE, LCE, and CL4M varied with the router’s cache size in the GEANT network.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Topologies** & **Source Nodes** & **Router Nodes** & **Receiver Nodes** \\ \hline ROCKETFUEL [18] & 10 & 104 & 104 \\ \hline TISCALI [19] & 44 & 160 & 36 \\ \hline GARR [19] & 13 & 27 & 21 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The number of source, router, and receiver nodes for different network topologies.
# A Construction Method for the Random Factor-Based G Function
## 3 Analysis of the Principles and Limitations of Commonly Used G Functions
### The Time-Varying Iterative Decomposition G Function  
The G function, based on iterative decomposition [18], expresses the relationship between the chaotic sequence \(\mathrm{Y_{n}}\), the RS code RS, and the parameter controlling the fan-out coefficient of each sub-block \(\mathrm{P_{n}}\), as follows:  
\[F_{n}=G(F_{n-1},X_{n},Y_{n},m,RS,P_{n}) \tag{5}\]  
This function is built upon the G function based on the m sequence. The data information \(\mathrm{X_{n}}\) is scrambled using both the m sequence and the chaotic sequence \(\mathrm{Y_{n}}\). The frequency point interval is controlled by the RS code, while the frequency hopping subset is controlled by the m sequence. This method divides all possible frequency transfer pairs into blocks, allowing for changes in the frequency transfer relationship over time.  
Figure 4 is a schematic diagram of this iterative decomposition process. This approach ensures the time-varying nature of the G function and improves the performance of the frequency-hopping sequence to a certain extent. However, it does not specify the optimal  
Figure 3: Construction diagram of AES algorithm.  
decomposition order or decomposition sub-block method for different channel environments. Additionally, the de-mapping result of this method is not unique, requiring an additional receiving algorithm, which may hinder practical application.
## 3 Available Techniques to Reduce the Bandwidth of the 360-Degree Video
### Tiling
#### 3.2.6 Multicast Virtual Reality (MVR)  
In [38], the Multicast Virtual Reality (MVR) streaming technique, which is a basic rate adaptation mechanism, serves all members in a multicast group with the same data rate to ensure that all members can receive the video. The data rate is selected based on the member with the poorest network conditions. However, a better tile weighting technique with data-driven probabilistic and an improved rate adaption algorithm is required to improve the user experience.
## 1 Introduction
### Related Work
#### 1.1.3 The Relationship between User Performance, Risk, and Error Communication  
Furthermore, it has been shown that being transparent about limitations such as low predictive power (low accuracy) could be beneficial for appropriate user expectation management. As such, it could lead to a higher model acceptance [60], too. One of the key pillars of the XAI concept is transparency. Derived from sociology, the authors in [61] outlined several principles for XAI. Among others, transparently informing about non trustworthy AI is critical. This guideline addresses communicating situations to the user where the AI system would not work as expected.  
Numerous authors have also studied the effect of conveying AI accuracy to users regarding the user's performance. For example, it has been shown that the model's stated accuracy affects the user's willingness to check the model's decision. The participants agreed with the model's prediction more readily if the AI's stated accuracy was higher [30]. In [31], the authors attempted to objectively measure how much participants trusted the AI model while varying the reported predictive power of the AI on a trial-by-trial basis. The results showed that, starting at an accuracy level of 70%, the participants were more willing to delegate the decision to the AI once they were aware of the model's confidence.  
Alternatively, the decision of whether to communicate errors to the user can also depend on the application's risk level. In [30], where an AI was represented by an assistant to predict a person's yearly income with certain qualities, the researchers did not find any influence on the amount at stake on the participants' behavior. The authors recognize, however, that the absence of the effect can be explained by negligible consequences for the participants in the case of an incorrect final decision. In assistive technology, however, the role of the risk of AI failure to correctly predict a user's intention grows significantly as the user physically relies on the system's performance. The failure of an AI to successfully support the user can cost them various resources, while financial and temporal costs are the least, physical trauma and health threats are more severe safety-related consequences [62; 63].
## 2 EVs Performing Multiple Functions in Cancer Formation
### EVs in Reprogramming Energy Metabolism  
The metabolic adaptations made possible by cancer cells allow them to adapt to conditions of nutritional deprivation and/or stress. TEM is heterogeneous in cancer cells, fibroblasts, endothelial cells, mesenchymal stem cells, and extracellular matrix, participating in the metabolic reprogramming crucial for cancer progression [39].  
EVs released from cancer cells contribute to the transformation of normal fibroblasts into distinct functional subtypes. Cancer-associated fibroblasts (CAFs), a major component in TME, promote tumor growth and progression through various pathways, e.g., by the secretion of inflammatory factors [40]. On this note, early- and late-stage colorectal cancer (CRC) cell-derived EVs both can activate normal quiescent fibroblasts in phenotypically and functionally distinct subsets of CAFs, which could facilitate tumor progression [40]. Additionally, triple negative breast cancer (TNBC) cells overexpressing ITGB4 are capable of remodeling fibroblast metabolism and promoting glycolysis in CAFs [41].  
EVs released from cancer-associated endothelial cells (CAECs) might also have an important influence on tumor progression. Analyses suggest that a decrease in levels of EC-derived EVs after chemotherapy in patients with metastatic breast cancer is associated with disease-free survival [42]. The intercellular communication between MSC and cancer cells is not well understood, and neither are the metabolic changes due to this interaction. However, TME could promote the development of cancer-associated mesenchymal stem cells (CA-MSCs) with the ability to differentiate into CAFs and influence cancer cells by secreting various metabolites by EVs. In fact, the metabolic crosstalk between tumor cells and CAFs profoundly affects TME remodeling and drives tumor growth [43]. In addition, glioma cell-derived EVs induced tumor-like phenotypes in MSCs by activatingglycolysis [44] and EVs from prostate cancer promote the formation of a pre-metastatic niche through the transfer of PKM2 into MSCs [45] (Figure 1).
## 2 Study Area and Data
### Se Crop Content  
The collected crop samples included wheat, rice, rapeseed, tea leaves, tobacco leaves, potatoes, and corn. Table 1 summarizes the average Se content in different crop types. Rice grain, wheat, rapeseed, and tea leaves exhibited Se content within a reasonable range. Crop samples with extremely low (e.g., potato: 0.004 mg/kg; corn: below detection limit)  
Figure 1: Xiaoyan Town, Nanzhang County: (**a**) distribution map of Se soil content and (**b**) classified map of Se soil content.  
or unusually high Se content (e.g., rice root: 9.94 mg/kg) were excluded to avoid skewing the modeling process. The remaining crop samples were utilized to predict the Se content distribution in the study area and to delineate optimal planting zones for Se-rich crops.
## 1 Introduction  
Digital technology in modern healthcare is anticipated to dramatically revolutionize healthcare systems around the globe. Smart healthcare uses digital technology to link people, services, and institutions; quickly navigates health data; and immediately addresses medical environment requirements [1]. Patients, physicians, hospitals, and insurance providers are just a few of the groups that benefit from smart healthcare's ability to link the many players in medical systems. New and emerging technologies, including artificial intelligence (AI), the Internet of Things (IoT), fog computing, cloud services, blockchain, monitoring devices, 5G technology, and the IoMT, make this feasible. It is essential that these technologies support the healthcare 5.0 paradigm change. The IoT solutions for the medical industry are shown in Figure 1. The ability to transmit data through a network without the need for computer-to-computer or human-to-human communication has helped in securing data transmission, managing duties, analyzing opportunities, and connecting gadgets [2; 3].  
The IoT will have a huge impact on healthcare 5.0. Medical organizations have made extensive use of IoT-to-network medical equipment to exchange patient records online. There are various new types of sensor-based IoT used in healthcare, each having its own characteristics. For instance, IoT has paved the way for the development of the IoHT, IoMT, IoNT, and IoMT (all acronyms for various Internet-based medical and cognitive wearable things) [4]. The Internet of Nano Things (IoNT) refers to the networking of nanoscale gadgets and communication systems for purposes including detecting, acting with, and transmitting electromagnetic waves. So, modern IoT variants offer interconnected healthcare, which allows for the seamless incorporation of modern medical instruments and the remote, all-encompassing exchange of health information. As an illustration, the idea of conventional healthcare is transformed into \"smart healthcare\" because advanced devices allow for a monitoring system in medical services [5; 6]. As a result, healthcare providers will be able to remotely connect, analyze, and evaluate the health information sensed by biomaterials and interactive wearable technology using variants of the IoT to enhance the quality of the treatment they provide. The Internet of Health Things (IoHT), Internet of Nonmedical Things (IoNT), and Internet of Mobile-Health Things (IoMT) all offer widespread health services meant to aid in patient-specific therapies, compliance, assertive supervising, efficient prognosis, timely and accurate detection of diseases, constant care, and intelligent restoration [7; 8]. Portable healthcare is developing as a result of the combination of advancing digital technology and devices, allowing for wireless connectivity and assistance of patients suffering from long-term disorders outside of healthcare institutions. As a result, biosensors hold great promise as tools for disease detection and management [9; 10].  
Figure 1: Solutions for the IoT in the medical field.
# An Embedded Sensor Node Microcontroller with Crypto-Processors
## 3 Microcontroller Design
### Communication Accelerator  
To support an efficient and secure communication, the TNODE5 design implements a baseband controller that complies with the DIN EN 13757-4 standard. The DIN EN 13757-4 is a communication standard for meters and remote reading of meters. It is specified for short range devices running at frequencies of 868-870 MHz with data rates ranging from 2.4 to 66.6 Kbit/s. The baseband supports 3-out-of-6 and Manchester coding defined by the standard. The baseband is extended to support the direct sequence spread spectrum (DSSS) to improve communication in a highly jammed environment (Barker 7 and 11 modulation). The DSSS uses the spreading code for converting the narrow band information into wideband information. In DSSS each of the information bit is XOR-ed with the barker code at the point of the transmission. As a result, the whole information is converted into a very wide band and the information signal remains the same. This spread spectrum is able to reject interferences or jamming attacks and allows a recovery of signal where less than fifty percent of the data bits have been damaged during transmission. Besides its high robustness it allows a data transmission in a very noisy environment, which makes a detection of the data transmission very hard. The implemented baseband core includes clock gating and wake-up support for maximum power efficiency. It can process an input data stream with a clock offset higher that 2%. The offset is compensated during the frame reception. This feature can improve communication with devices that have large clock drift caused by any reason. The core implements a programmable clock prescaler that allows the programming of various bitrates derived for different input clock frequencies.  
Figure 3: Hash generation flow.
# Continuous Space Wireless Communication Tower Placement by Hybrid Simulated Annealing
## 4 Hybrid Simulated Annealing Algorithm  
SA, originally proposed by [PERSON], [PERSON], and [PERSON] [27] in 1983 and then by [PERSON] in 1985 [28], has been applied to many optimization problems of different kinds. SA is a global optimization technique. It starts with an initial solution and performs global optimization by iteratively generating, evaluating, and selecting a new solution from the current solution with a controlled annealing schedule. At each step, SA considers a neighbor of the current solution and probabilistically decides whether the current solution should be replaced with the new solution using a probability-based acceptance strategy. Typically, this step is repeated until the search reaches a solution that is good enough for the application or until a given computation budget has been exhausted.  
When applying conventional SA to the placement problem initially, we observed that its exploration capacity is powerful, but its local exploitation capacity is poor (this is discussed in the next section of this paper). Thus, we developed a hybrid SA that capitalizes on the powerful global exploration capability of the conventional SA and the effective and efficient exploitation capability of a local optimization algorithm. Algorithm 1 is a high-level description of the hybrid SA algorithm.  
```
randomly generate an initial solution \(P\) \(t=0\) \(T_{0}=1\) while\((T(t)>T_{lowest})\) and \((Cost(P)\
eq 0)\)do  randomly generate a neighbor of the current solution \(P\), \(P^{\prime}\) if\(Cost(P^{\prime})<Cost(P)\)then \(P\) is replaced by \(P^{\prime}\) else \(P\) is replaced by \(P^{\prime}\) probabilistically endif if\(P\) was updated then  maximum coverage-based local optimization(P) endif \(t=t+1\) endwhile
```  
**Algorithm 1** Hybrid SA algorithm  
In the following, we discuss the details of the hybrid SA, including cost function, cooling schedule, neighbor generation, acceptance of a new solution, termination condition of the hybrid SA, and the local optimization algorithm.
# Layer-Wise Query Selection to Eliminate Redundant Queries in DETR
## 4 Experiments
### Experimental Setting
#### 4.1.3 Training Details  
All baseline detectors in Table 2 are trained for the exact number of epochs reported in their original papers (500 for DETR-DC5, 108 for Conditional-DETR, 50 for Anchor-DETR, 36 for Efficient-DETR, 50 for Deformable-DETR, etc.). SQ-DETR follows a 72-epoch schedule by default, but its validation AP peaks at epoch 62 and drops slightly thereafter, indicating overfitting. We therefore report both the best checkpoint at 62 epochs and the final checkpoint at 72 epochs, and we use the former for subsequent ablation studies.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Model** & **Backbone** & **Epoch** & **Params** & **GFLOPs** & \(\mathbf{AP^{\rm val}}\) & \(\mathbf{AP^{\rm val}_{50}}\) & \(\mathbf{AP^{\rm val}_{75}}\) & \(\mathbf{AP^{\rm val}_{S}}\) & \(\mathbf{AP^{\rm val}_{M}}\) & \(\mathbf{AP^{\rm val}_{L}}\) \\ \hline DETR-DCS [1] & R50 & 500 & 41 & 187 & 43.3 & 63.1 & 45.9 & 22.5 & 47.3 & 61.1 \\ DETR-DCS [1] & R101 & 500 & 60 & 253 & 44.9 & 64.7 & 47.7 & 23.7 & 49.5 & 62.3 \\ Anchor-DETR-DCS [22] & R50 & 50 & 39 & 172 & 44.2 & 64.7 & 47.5 & 24.7 & 48.2 & 60.6 \\ Anchor-DETR-DCS [22] & R101 & 50 & - & - & 45.1 & 65.7 & 48.8 & 25.8 & 49.4 & 61.6 \\ Conditional-DETR-DCS [21] & R50 & 108 & 44 & 195 & 45.1 & 65.4 & 48.5 & 25.3 & 49.0 & 62.2 \\ Conditional-DETR-DCS [21] & R101 & 108 & 63 & 262 & 45.9 & 66.8 & 49.5 & 27.2 & 50.3 & 63.3 \\ Efficient-DETR [4] & R50 & 36 & 35 & 210 & 45.1 & 63.1 & 49.1 & 28.3 & 48.4 & 59.0 \\ Efficient-DETR [4] & R101 & 36 & 54 & 289 & 45.7 & 64.1 & 49.5 & 28.2 & 49.1 & 60.2 \\ SMCA-DETR [29] & R50 & 108 & 40 & 152 & 45.6 & 65.5 & 49.1 & 25.9 & 49.3 & 62.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison with other object detectors in COCO.
# Bidirectional Long Short-Term Memory Neural Networks for Linear Sum Assignment Problems
## 1 Introduction  
The linear sum assignment problem (LSAP) is a special case of the linear programming problem that aims to minimize the cost of assigning multiple tasks to a number of agents on a one-to-one basis. In wireless communications, many resource allocation tasks can be modeled as the LSAP, such as joint relay selection and resource allocation for device-to-device communications [1], joint resource allocation with multi-cell cooperation in virtual multiple-input multiple-output (MIMO) systems [2], and unlicensed channel allocation for LTE systems [3].  
For LSAPs, the Hungarian algorithm has been proposed to obtain the optimal solution without an exhaustive search [4]. Furthermore, heuristic algorithms such as deep greedy switching [5], the interior point [6], or the dual forest algorithm [7] have been investigated to estimate near-optimal solutions in real-world problems with time constraints.  
In recent years, deep neural networks (DNNs) have recently achieved state-of-the-art performance in different fields such as audio processing [8, 9], visual object recognition [10], and other domains [11, 12, 13]. Neural networks (NNs) can extract features from input implicitly and approximate an arbitrary math function. Several approaches have applied DNNs to solve mathematical optimization problems [14, 15]. In [2], a DNN was used to approximate an optimization algorithm in interference management. In [16], an LSAP was decomposed into several sub-assignment problems and two types of DNNs, i.e., the feed-forward neural network (FNN) and convolutional neural network (CNN) [17] were applied to address sub-assignment problems. However, the DNNs discussed in [16] have not achieved much success owing to the increasing size of the problem.  
Hence, a bidirectional long short-term memory neural network (BDLSTM) is proposed to solve LSAPs. In the proposed method, an LSAP is decomposed into a sequence of smaller sub-assignment tasks that become the model input, in which each layer consists of two separated smaller LSTM layers. The intermediate outputs of these layers are combined to process data in both the forward and backward directions. The BDLSTM structure is better at remembering the LSAP than other structures by combining the hidden layer outputs of each sub-assignment problem together.  
Finally, a collision-avoidance algorithm is applied to the output to obtain the final result. Simulation results indicate that the proposed BDLSTM outperforms the FNN and CNN models when the size of the problem increases, while requiring less parameters to model the patterns.
## 3 Results and Discussion
### Metal Bioaccumulation in the Gills, Liver, and Kidneys  
The analysis of the data is presented in Figure 2. A two-way analysis of variance was applied to evaluate and compare the concentrations of metals in different tissues from all three species analyzed with different feeding habits: carnivorous (BB), omnivorous (SC), and herbivorous (CN).  
Renal cadmium accumulation was significantly higher in BB compared to SC and CN, while no significant differences between species for gills and liver were noticed (Figure 2A). Similarly, Pb accumulated at highest level in the kidneys of carnivorous species compared to the omnivorous and herbivorous ones (Figure 2E). Zn was also bioconcentrated mainly in the kidneys, compared to the gills and livers. The carnivorous species accumulated significantly more Zn compared to the herbivorous fish (Figure 2C).  
The gills of BB species presented the highest concentration of Cr (\(p<0.001\) compared to SC; \(p<0.05\) compared to CN). The same situation was also noticed for the liver tissue compared to the other two species (Figure 2B). By contrast, Cu accumulated mostly in the liver of the omnivorous species compared to the herbivorous (\(p<0.01\)) and carnivorous (\(p<0.005\)) ones (Figure 2D).  
Figure 2: Heavy metal concentrations (mean \(\pm\) SEM) in the gills, kidneys, and liver of _Barbus barbus_ (BB), _Squalius cephalus_ (SC), and _Chondrostoma nusus_ (CN) for cadmium (**A**) chromium (**B**), zinc (**C**), copper (**D**), lead (**E**) significant difference at \(p<0.001\); \({}^{\star}\) significant difference at \(p<0.05\).  
Overall, Cd, Pb, and Zn accumulated at the highest concentrations in the kidneys, while Cr was recorded in the gills, and Cu in the liver. The BB carnivorous species was more sensitive to heavy metals and bioconcentrated the largest amount of Cd, Pb, Zn, and Cr, whereas Cu was accumulated at the highest level by omnivorous species. No analyzed heavy metal has accumulated mainly in the herbivorous species.