esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 LSTM Prediction Algorithm  
The \(L\) unfolded stages version of the LSTM prediction framework is illustrated in Figure 2 and consists of the following two layers:  
* The LSTM prediction layer: it performs the time series prediction by providing the storage of the internal states; we consider the case of a single layer composed by \(L\) LSTM Cell Blocks (LCB) referred to as \(LCB_{j}\) (\(j=n-L+1,\cdots,n\));
* The feed forward network layer: it evaluates from the output of the last LSTM layer the \(h\) steps ahead of the predicted bandwidth values \(\hat{b}_{n+j}\) (\(j=1,\cdots,h\)) stored in the vector \(\hat{\mathbf{b}}_{n,h}\).  
The VNF-FG bandwidth predictions are performed by the LSTM layer, which has as inputs the VNF-FG bandwidth values \(b_{j}\) (\(j=n-L+1,\cdots,n\)). The output \(\mathbf{h}_{n}\) is processed by a feed forward neural network, which provides an evaluation of the vector \(\hat{\mathbf{b}}_{n,h}\) of predicted VNF-FG bandwidth values.  
In the LSTM layer the state variable \(\mathbf{s}_{j}\) (\(j=n-L+1,\cdots,n\)) is also updated. In the LSTM Cell Block \(LCB_{j}\), the state variable \(\mathbf{s}_{j}\) in the \(j\)-TI depends on the following variables: (i) the VNF-FG bandwidth value \(b_{j}\); (ii) the output \(\mathbf{h}_{j-1}\) in the \((j-1)\)-th TI; (iii) the state variable \(\mathbf{s}_{j-1}\) in the \((j-1)\)-st TI.  
The operation mode of a single LCB is well known because LSTM has been applied in many fields (handwriting recognition, speech recognition, power consumption prediction, etc.)[16]. However, the training of the LSTM recurrent neural network is performed by minimizing the symmetric loss function.  
At the same time all of the prediction-based resource allocation algorithms in NFV environments aims at exactly forecasting either the traffic [15] or the resources [12] to be allocated. They are based on the minimization of symmetric cost functions of the errors \(e_{n+j}=b_{n+j}-\hat{b}_{n+j}\) (\(j=1,\cdots,h\)) where \(b_{n+j}\) is the real VNF-FG bandwidth value in the \((n+j)\)-st TI. Examples of these functions are the Mean Squared Error (MSE) or the Mean Absolute Error (MAE). The choice of symmetric cost functions leads to equally weigh positive and negative errors. Conversely, being aware that an exact traffic prediction is not possible, our objective is to make mistakes where it is more convenient according to the cloud resource allocation the QoS degradation costs. For this reason we consider asymmetric cost functions and because of its simplicity we choose a cusp linear loss function as represented in Figure 3 where the slopes are dependent on the resources allocation cost \(C_{RA}\) and QoS degradation cost \(C_{QoS}\) both defined in $ per Gbit. As reported in Figure 3 the training process minimizes the Asymmetric Mean Absolute Error \(AMAE_{n,h}\) expressed by:  
\[AMAE_{n,h}=\frac{1}{h}\sum_{j=1}^{h}(C_{RA}I(\hat{b}_{n+j}-b_{n+j})+C_{QoS}I(b_ {n+j}-\hat{b}_{n+j})) \tag{1}\]  
where \(I(x)\) is the indicator function that is \(I(x)=1\) for \(x>0\) and \(I(x)=0\) for \(x<0\).
## 6 Conclusions  
Position falsification attacks can be very dangerous; if they are not detected in the connected vehicular environment, they can cause minor mishaps to life-threatening incidents. In this paper, we proposed and evaluated the multi-BSM approach for the detection of anomalies, i.e., position falsification attacks, to be more precise. It was proven to be quite effective in detecting attacks. The multi-BSM also alerts the legitimate vehicles about the attacker present in the network even when the legitimate vehicle is not in the transmission range of the attacker vehicle. The RSUs demonstrate optimum real-time detection and prevention as they are equipped with better computational resources than those of vehicle  
Figure 6: Multi-BSM evaluated in accordance with different intervals of BSM generations. (**a**) Average transmission delay vs. vehicle density. (**b**) Packet delivery ratio vs. vehicle density. (**c**) Packet delivery ratio vs. velocity.  
Figure 7: The comparison of average routing delays of the algorithms.  
OBUs. All the RSUs are connected with each other and linked to a central database, which assures a better anomaly detection in real time. However, our work is limited to position falsification attacks at the current level, and we are not targeting other attacks that malicious attackers can initiate. This area has a lot of potential for future work.
# Physicochemical Properties and In Vivo Hepatoprotective Effect of Polysaccharides from Grape Pomace
## Materials and Methods
### Evaluation of In Vivo Hepatoprotective Effect of GPPs  
#### 2.6.1 Animals and Treatment  
As an important organ with metabolic function, the liver performs key detoxification functions in the metabolism of endogenous and exogenous substances [27]. A large number of studies have shown that oxidative stress is one of the pathological processes of liver injury [28; 29; 30]. Carbon tetrachloride (CCI\({}_{4}\)) can cause acute liver failure and is an effective hepatotoxic agent widely used in animal models [14; 15]. The mechanism of CCl\({}_{4}\)-caused acute liver injury is the production of highly reactive trichloro-methyl radical and trichloromethyl peroxy radical through metabolism [16]. In order to explore the activity of grape pomace polysaccharides, a liver-injured murine model induced by CCl\({}_{4}\) was established and used for evaluation. The study was conducted according to the guidelines of the Declaration of Helsinki and was approved by the Animal Care and Use Committee of Qingdao Agricultural University. The animal experiments followed the guidance of the ethics committee of Qingdao Agricultural University (code: IACUC-20220722-2). After 3 days of adaptive feeding under standard conditions (24 \(\pm\) 2 \({}^{\circ}\)C, humidity at 50-60%, light-dark cycle time of 12/12 h, free access to water and standard pellet feed) [31], SPF ICR mice (obtained from Jinan Pengyue Laboratory Animal Breeding Co., LTD., Shandong, China) were randomly divided into 5 groups (\(n\) = 6, 3 males and 3 females; weight, 20 \(\pm\) 2 g). Group I was the normal control group, Group III was the CCl\({}_{4}\)-induced acute liver injury model group; in the normal control group and the model group, the same amount of normal saline (10 mL/kg body weight per day) was intragastrically administered for 14 consecutive days. Group II was the positive control and received silymarin solution at the dose of 100 mg/kg body weight per day. Groups IV-V received GPP solution at different doses (100 and 200 mg/kg body weight per day, respectively) for 14 consecutive days. On the 15 th day, all mice except for those of group I were intraperitoneally injected with olive oil solution containing 0.1% CCl\({}_{4}\) (10 mL/kg body weight per day), whereas group I mice were intraperitoneally injected with the same volume of olive oil without CCl\({}_{4}\). All mice were forced to fast but had free access to enough water for 16 h. All mice were then killed by means of blood extraction from the eyeballs, and the blood from the eyeballs was collected into sterile tubes. Blood samples were centrifuged (3000 r/min, 4 \({}^{\circ}\)C) for 10 min in order to acquire serum and stored at \(-\)80 \({}^{\circ}\)C. Livers were immediately obtained, dissected and rinsed with PBS solution [32]. Half of the liver was supplemented with 9 times the volume of PBS solution, and the liver tissue was ground. The supernatant from liver tissue was centrifuged at 5000 r/min and 4 \({}^{\circ}\)C for 15 min and stored at \(-\)80 \({}^{\circ}\)C for further analysis.
# The Campus as a Smart City: University of Malaga Environmental, Learning, and Research Approaches
## 3 UMA IoT and Telecommunication Infrastructure
### OAM Smart-Campus Platform  
With the amount and heterogeneity of the different ICT infrastructures described, a shared system for the gathering of data and distribution to the community [1] is deemed indispensable. In this line, the data management platform deployed under the framework of the previous UMAIoT/RAT testbed is used as the central point to integrate all the information coming from the different sources in the campus. As shown in Figure 5, a set of IoT agents provide connectivity to multiple IoT messaging protocols (e.g., Message Queuing Telemetry Transport-MQTT, Constrained Application Protocol - CoAP [39]), making their details transparent for managing the data.  
In this way, all gathered information would be stored in an IoT cloud platform based on the aforementioned FIWARE technology [37; 38]. This is an open-source platform fostered by the European Union, which allows the connection with IoT, the storage, publication, and analysis of data on a large scale, as well as the co-creation of apps and development of advanced user's interfaces by using the common standards and information models.  
In this way, the platform provides access to real-time context information, which can be consulted or shared for all of the university community for informative or educative purposes, turning the Smart-Campus into a Living Lab.  
For the participants of the Smart-Campus, this platform enables them to have a competitive position in research activities. Moreover, it is expected to be a catalyst for the creation of new applications and services supporting the entrepreneurial ecosystem around it, where it is expected to take advantage of the drive provided by FIWARE and its FIWARE Foundation [38]. The latter, dedicated to promoting, augmenting, protecting, and validating the FIWARE technologies, has more than 150 members, including well-known enterprises such as Telefonica, Atos, Orange, Engineering, and NEC.
## 7 Results and Performance Analysis
### Multilingual Transfer and Zero-Shot Generalization
These results highlight how domain constraints improve both structural fidelity and decision-making reliability, preventing the generation of medically inappropriate outputs. Ablation Study 5. Without Federated Update Loop. In this final ablation, we disabled the federated update mechanism and trained the model in a centralized fashion using aggregated data. This approach removes local model personalization and ignores the distributional differences across institutions or regions that are naturally captured in federated learning settings. Without federated fine-tuning, the model exhibited reduced adaptability to local linguistic or clinical nuances, especially in multilingual or region-specific cases. For instance, in a Romanian-language input describing a stroke scenario with subtle phrasing (\"nu poate vorbi si cade intr-o parte\", meaning that the person is unable to speak and collapses to one side), the centrally trained model failed to interpret the symptom structure correctly and assigned an urgency level of \"moderate.\" In contrast, the federated version had learned to recognize this formulation from regional data and responded with the correct \"activate stroke protocol\" intervention. Quantitatively, the non-federated model achieved a Slot F1 of 0.88 and a Frame F1 of 0.83, compared to 0.91 and 0.89 in the full model. The Macro F1 for intervention classification dropped to 0.79 (from 0.88), and accuracy fell to 0.86 (from 0.92). These drops demonstrate the contribution of federated learning in improving generalizability while preserving local relevance, especially in geographically or linguistically diverse deployments. Summary of Ablation Results. The ablation studies demonstrate the necessity of each architectural module in achieving TriagE-NLU's full performance. Removing patient metadata significantly impairs semantic understanding, highlighting the value of contextual enrichment. Disabling the se mantic parsing head or classification head individually reveals how each contributes distinct yet complementary capabilities, interpretability, and clinical decision mapping. The knowledge constraint module helps reduce noisy outputs, while federated updates boost performance in multilingual and heterogeneous deployment settings. Together, these findings confirm that TriagE-NLU's architecture relies on the synergistic contribution of its components to enable accurate, structured, and context-aware emergency triage. To visually summarize these findings, in Figure 9 we present a heatmap showing the performance of each model configuration across four key metrics: Slot F1, Frame F1, Macro F1 (intervention), and accuracy (intervention). The heatmap makes clear that the full model consistently outperforms its ablated counterparts, underscoring the cumulative importance of the integrated components.
# Detecting Malicious Devices in IPSEC Traffic with IPv4 Steganography
## 1 Introduction
Moreover, steganography's utility extends to practical scenarios such as secure data transmission, hiding encryption keys, watermarking sensitive information, and detecting malicious activities such as covert channels or stealthy communication, enhancing network systems' overall security posture [13; 14; 15; 16]. The aim of this paper is to develop a steganographic procedure that enhances network security by monitoring traffic and identifying malicious devices in order to prevent flooding and DDoS attacks. Our approach also describes the interception process of MAC addresses associated with these devices. Furthermore, we introduce a service that enables efficient monitoring of network packets within a network using the visualization platform Grafana. The paper is organized as follows: Section 2 contains a description and overview of the essential concepts in network steganography and existing methods. Section 3 presents a novel concept of enhancing network security with steganography used in IPv4 over IPv5c. Section 4 delves into the architecture employed in the study, while Section 5 presents the results and their analysis. Section 6 encompasses the conclusions drawn from the study's findings.
# Node Depth Adjustment Based Target Tracking in UWSNs Using Improved Harmony Search
## 3 Problem Formulation
### Network Model  
In real situations, the sensors cannot be always placed at will, either due to physical or mission constraints. Thus, we refer to the scenario in Ref. [25], and we tackle the case where the sensors are floating in the sea surface at first. If a moving target is within the sensing range, the fusion center will wake up nodes to participate in the tracking task, and send commands to activated nodes to make them move vertically to the optimal depth. Then, the nodes will sense the target and send measurements back to the fusion center. As shown in Figure 1, the black triangle is a moving target; the red square is fusion center; the blue oval is a sleeping node, which is static at the water surface; the green ovals are activated nodes, which move vertically to the optimal depth for sensing the target. It should be noted that the moving range of nodes can be modified according to target' position. Otherwise, it remains dormant for energy efficiency. After receiving measurements from all selected nodes, the fusion center will estimate the target state via a tracking algorithm and communicate with the base station by radio signal.
## 2 Theoretical Background
### Comb Filter  
To remove the stationary frequency components from a transient signal, a comb filter can be used. A feed-forward comb filter (FFCF) involves the addition or subtraction of a delayed version of the original signal. The discrete output, \(y\), of an FFCF when \(x\) is used as the input signal is defined as follows [34]:  
\[y[k]=(x[k]+\alpha x[k-N])/2 \tag{8}\]  
where \(N\) is the number of delay samples of the input signal and \(\alpha\) is a scaling factor that satisfies \(|\alpha|\leq 1\). The frequency response of the FFCF exhibits a periodic sequence of notches and peaks. The peaks reach their maximum amplitude when \(\alpha\pm 1\). The notches are centered at frequencies \(f_{c}\) defined as follows [34]:  
\[f_{c}(r)=(F_{s}r)/N \tag{9}\]  
where \(F_{s}\) is the sampling frequency of the signal and \(r\) takes the values 0, 1, 2, \(\ldots\), \(N/2\). The frequency response of an FFCF can be observed in Figure 1.  
Figure 1: Frequency response of the comb filter at 60 Hz component and its harmonics.
# On the Optimal Calculation of the Rice Coding Parameter
## 2 Related Work
### Adaptive Rice Coding  
[PERSON] in [6] and later improved in [7] proposed the Run-Length/Golomb-Rice (RLGR) code. RLGR was designed to work best when the input sequence follows a TSGD. RLGR uses both run-length and Rice coding for encoding an input sequence of measurements. Run-length coding is used for compressing sequences of measurements with a value of zero.  
The Rice coding parameter is automatically adapted after a sample is coded: if the encoding process using the previous parameter yields zero on the value of \(q_{i}\), the Rice coding parameter is increased for coding the next symbol; if the previous parameter yields a value higher than one on \(q_{i}\), the Rice coding parameter is decreased proportionally. In our opinion, this idea works best if the consecutive values to be encoded are close to each other, but may yield sub-optimal Rice codes in the case that the consecutive values to be encoded are considerably different.  
In Section 5, we propose a way for efficiently using Rice codes in situations where the input sequence of values changes abruptly.
# Anti-Jamming Method and Implementation for GNSS Receiver Based on Array Antenna Rotation
## 4 The Variable-Step Iteration Algorithm for Efficient Determination of Optimal Rotation Angle
### Evaluation of Iteration Efficiency  
It is mentioned in Section 4.1 that there are two ways to get \(\Delta\varphi_{opt}\). One is to traverse all possible rotation angles (denoted as the \"traversal algorithm\"). Another one is the proposed variable-step iterative algorithm. In order to evaluate the efficiency of the proposed algorithm, the index of efficiency improvement is given below compared to the traversal algorithm.  
Suppose the number of sufficient traversals is \(N_{\mathrm{s}}\) and the iteration number of the proposed algorithm is \(N_{i}\). Taking \(N_{\mathrm{s}}\) as a benchmark, the efficiency improvement of the proposed algorithm is defined as  
\[\eta=\frac{N_{\mathrm{s}}-N_{i}}{N_{\mathrm{s}}}\times 100\% \tag{30}\]  
Although a fine angle resolution in traversal is always expected, the search time \(N_{\mathrm{s}}\) cannot be infinite. So, \(N_{\mathrm{s}}\) is considered sufficient while the _ICR_ resolution meets the demand.  
Because the anti-jamming algorithms usually contain eigen-decomposition or matrix inversion operations, the calculation amount of Formula (15) (first equation) is the largest in the iteration. Moreover, those operations are carried out twice in each iteration since the partial derivative of Formula (15) (third equation) is approximate by the numerical method. In order to more accurately evaluate the efficiency of the proposed algorithm, the equivalent iteration number is defined as twice the actual iterations, and Equation (30) is updated to  
\[\eta=\frac{N_{\mathrm{s}}-2N_{i}}{N_{\mathrm{s}}}\times 100\% \tag{31}\]
## 4 Discussion
Regarding optimization, addressing exponential node growth necessitates a hierarchical genetic algorithm, decomposing global optimization into regional sub-problems and enabling parallel computation via SOP-exchanged boundary constraints. Simultaneously, a graph neural network-based population initialization strategy leverages topology features to generate high-quality solutions, accelerating convergence. For coordination, a dual-layer control architecture is essential: The lower layer utilizes local RBF models for real-time inertia boundary assessment, while the upper layer employs collaborative controllers to exchange frequency dynamics, minimizing the communication load. Additionally, dynamic partitioning rules must automatically define coordination areas based on the electrical coupling strength and spatiotemporal inertia correlation, while virtual inertia sensitivity analysis identifies key control nodes to enhance efficiency in complex networks. The Jianshan Demonstration Zone in Haining City, Zhejiang Province, China, is implementing a virtual inertia enhancement demonstration project. This project plans to deploy low-voltage active power control equipment in 100 substations within the zone, utilizing grid-forming inverter technology to actively support the terminal low-voltage distribution network. This integration aims to improve voltage/frequency stability, enhance the terminal system strength and photovoltaic hosting capacity, and reduce line losses. As shown in Figure 10, the method proposed in this article can be embedded within a multifunctional new distribution terminal featuring synchronous broadband measurement, online virtual inertia perception, and active virtual inertia adjustment capabilities. This new terminal deploys virtual inertia control software, leverages existing medium-voltage interconnected converter stations, and fully mobilizes flexible resources within the region (including energy storage stations--both grid-forming and conventional PQ-controlled types--and controllable loads). Collectively, this addresses key challenges: the insufficient flexible control capability of distributed resources, inadequate virtual inertia support, and unreliable regional grid operation, thereby improving the photovoltaic integration capacity and enhancing regional grid stability. Regarding the two-region virtual inertia allocation method proposed in this article, one set of fast coordination controllers can be configured in the Wujiang-Jiashan medium-voltage flexible interconnection device. Leveraging the robust attributes of the Wujiang power grid, this setup transiently responds to virtual inertia enhancement commands from the new distribution terminal. It utilizes the energy reserves of the strong Wujiang grid to provide virtual inertia support to the Jiashan grid, rapidly adjusting the transient power output at the medium-voltage AC port. Figure 10: Application of virtual inertia allocation function device.
# A Chest Strap-Based System for Electrocardiogram Monitoring
## 3 Experimental Section
### The Composition of the CEMS  
The CEMS is powered by a 3.7 V lithium battery. Voltage regulation and charge management are handled by the LGS5500 EP (Legend-Si, Shenzhen, China) chip, which boosts and stabilizes the battery's 3.7 V output to 5 V, ensuring reliable operation of the filtering circuitry and enabling system reusability. A ME6206 (Microne Semiconductor Co., Ltd, Nanjing, China) low-dropout linear regulator (LDO) then delivers a stable 3.3 V rail to the STM32F407 VET6 (STMicroelectronics, Geneva, Switzerland) microcontroller, the ECB02 Bluetooth module, and the W25Q64 (Winbond Electronics Corporation, Taiwan, China) serial flash memory. The filter stage employs the GS8552-SR (Gainsil Semiconductor Technology Co., Ltd., Shanghai, China) precision operational amplifier, whose ultra-low quiescent current of just 180 \(\upmu\)A satisfies the system's stringent low-power and miniaturization requirements.