esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessing the Security and Privacy of Baby Monitor Apps
## 3 Materials and Methods
### Reviewing the Confidentiality of Monitoring Video Streams  
Considering that the confidentiality of video stream monitoring is an integral part of privacy protection, we found that evaluating the use of appropriate security measures is of particular interest. By evaluating the utilized network security measures, we examined how each application protects the confidentiality of the transmitted baby monitoring video streams.
## 4 Adaptive Neuromorphic Joint FRESH Equalizer and Detector  
### Motivation  
Artificial neural networks (ANN) are highly interconnected networks equipped with massive parallel processing functionality, which are inspired by the biological nervous system. Due to the massive parallelism and robustness offered by these neural networks, they are highly suitable for solving complex non-linear problems. In wireless communications applications such as channel equalization and automatic modulation classification (AMC), ANN-based receivers have been proven to outperform their conventional counterparts [26]. Due to their immense benefits, ANN is selected to perform the role of FRESH equalizer in this work. Basically, the ANN performs as an adaptive prediction filter, which can be used to predict the PU signal by utilizing the known cyclostationary features of the PU signal. The prediction noise is a combination of extracted SU-FC-FB signal and AWGN [26]. This means that the residual error signal of the cyclostationary prediction filtering is non-Gaussian in nature. Hence, use of conventional BA-RLS-FRESH joint equalization and data detection method will lead to performance degradation. However, ANN can perform the same role with much better performance.  
The primary objective of neural network training is to adaptively update the weights and biases to cause the output of the neural network predictor to approach the desired output signal. Evidently, this involves a certain amount of training data, which requires knowledge of a specific number of input-output pairs. If the past samples of the PU-OFDM signal are available, then they can be utilized as the desired output of the ANN based predictor to train the network. But from a practical standpoint, the exact PU-OFDM signal samples are not known in advance. Hence, in the NOMCR scenario, it is assumed that no prior information on the PU-OFDM is available except its cyclic frequencies, owing to its pilot arrangement and specific modulation format. Therefore, as an alternative to conventional ANN training, the received NOMCR signal can be used as the desired output signal to train the ANN. Owing to higher power allocation to the PU-OFDM signal and perfect knowledge of its cyclic frequencies at the SU receiver, the ANN can predict the PU-OFDM from the received NOMCR signal.
## 6 Evaluation Metrics of the Network Performance  
Several parameters are considered to evaluate how UAV computing intelligence assists SAR and to examine the proposed network performance such as throughput, network load, delay, network traffic received and sent, and path losses. These parameters are considered for improving the system's quality of service [49; 50].
## 6 Proposed Migration Scheme
It also plays a critical role in integrating different network technologies and architectures, reducing service disruption during handovers, and enabling safety-critical applications. Therefore, developing effective and efficient migration mechanisms is essential for the success of VANET and realizing its full potential for improving road safety, traffic efficiency, and passenger comfort. Algorithm 2 introduces the proposed migration scheme for the developed VANET. The algorithm is built based on our previously developed handover scheme proposed in [28]. The power indicates the vehicle's position with the RSU. The signal power for the RSU is denoted as P, and the signal-to-noise ratio (SNR) is calculated by dividing the signal RSU power value (P) by the noise power of the channel. The main steps of the proposed migration algorithm are summarized below:  
* _Step 1:_ First, the algorithm initializes all the parameters needed, including the SNR and P;
* _Step 2:_ Then, it compares the P with the SNR1 (i.e., the first level of thresholds). If the P > SNR1, two actions will occur: The first is \"dedicate the parameter\", and the second is \"dedicate P RSU\". If not, it goes to step 3;
* _Step 3:_ We have two possibilities if P is not greater than the SNR1. These possibilities are (P < SNR1 or P > SNR2). In this case, if either of the possibilities is true, the action is to \"Start the inspection state phase\". If not, then it goes to step 4;
* _Step 4:_ If P is not smaller than SNR1 or greater than SNR2, then we have another two possibilities. These two possibilities are (P < SNR2 or P > SNR3). In this case, if either of the possibilities is true, the action is 4 steps. The first is \"send migration request to target MEC\", and second is \"Initialize migration process\". The third is \"Send task specification to the target MEC\", and fourth is \"Receive migration response and time stamps\". If not, it goes to step 5;
* _Step 5:_ If P is not smaller than SNR2 or greater than SNR3, we have any other special condition. In this case, the action will be four steps. The first is \"Start execution stage\", the second is \"Migrate computing task\", the third is \"Load target MEC\", and the fourth is \"Move serving vehicle parameters to RSU.\"  
The proposed algorithm covers all possibilities during the handover and migration processes. The migration process takes place at the start of the handover algorithm.
# A Novel Framework Leveraging Large Language Models to Enhance Cold-Start Advertising Systems
###### Abstract  
The cold-start problem remains a critical challenge in personalized advertising, where users with limited or no interaction history often receive suboptimal recommendations. This study introduces a novel, three-stage framework that systematically integrates transformer architectures and large language models (LLMs) to improve recommendation accuracy, transparency, and user experience throughout the entire advertising pipeline. The proposed approach begins with transformer-enhanced feature extraction, leveraging self-attention and learned positional encodings to capture deep semantic relationships among users, ads, and context. It then employs an ensemble integration strategy combining enhanced state-of-the-art models with optimized aggregation for robust prediction. Finally, an LLM-driven enhancement module performs semantic reranking, personalized message refinement, and natural language explanation generation while also addressing cold-start scenarios through pre-trained knowledge. The LLM component further supports diversification, fairness-aware ranking, and sentiment sensitivity in order to ensure more relevant, diverse, and ethically grounded recommendations. Extensive experiments on DigiX and Avazu datasets demonstrate notable gains in click-through rate prediction (CTR), while an in-depth real user evaluation showcases improvements in perceived ad relevance, message quality, transparency, and trust. This work advances the state-of-the-art by combining CTR models with interpretability and contextual reasoning. The strengths of the proposed method, such as its innovative integration of components, empirical validation, multifaceted LLM application, and ethical alignment highlight its potential as a robust, future-ready solution for personalized advertising.  
artificial intelligence; machine learning; click-through rate (CTR); artificial neural networks; cold-start; large language models (LLMs)
# Towards a Scalable Software Defined Network-on-Chip for Next Generation Cloud
## 6 Evaluation  
We evaluated the effectiveness of the proposed NoC architecture regarding performance, scalability, area cost and power consumption. We performed our evaluation, by synthesising the proposed interconnection using an FPGA device. The analysis of the proposed programming interface (i.e., the instruction set extension and the high-level programming interface) along with the evaluation of its effectiveness has been evaluated in [30].  
All of the experiments were performed on a Xilinx Kintex-7 ultra-scale device and using the Xilinx Vivado 2017.4 design suite to simulate, synthesise, and perform the place-and-route operations. The entire synthesis and placement process was performed considering the instantiation of both a variable number of 2D-mesh routers (from 1 to 64) and ringlets (ranging from 1 to 256). A single FPGA device does not offer enough resources to accommodate the largest configurations. Therefore, the evaluation of scalability and performance of such complex configurations (up to 1024 PEs), was performed on multiple FPGA devices connected through dedicated external high-speed links. To this end, the latency of such links must be scaled to reflect the same latency of intra-chip interconnections. Correct management of the traffic required input ports in the router to have two VCs. Routers implement an RR strategy and employ the \(xy\)-DoR to reduce the latency for reaching the output port while keeping the complexity of the control logic low. The router operates with a four-stage pipeline, which can be optimised by integrating speculative logic to reduce the number of cycles required to forward flits.  
We evaluated the throughput and average packet latency for a configuration with multiple physical tiles by simulating the injection of uniform random traffic, since this traffic pattern allows for better capturing the average network behavior. Experimental results show the capability of the proposed design to scale very well: average latency increases from 90 cycles (16 PEs) to 220 cycles (1024 PEs), while the throughput increases from 12 packets/cycle (16 PEs) to 570 packets/cycle (1024 PEs).
## 2 Materials and Methods
### Detection of Biofilm Formation  
A modified colorimetric method, according to [PERSON] et al. [26], was utilized to assess the biofilm formation of _E. coli_ strains. A pure 24 h bacterial culture in 2 mL of sterile saline was used to prepare a suspension with a turbidity of 1.0 on the McFarland turbidity scale. Into each well of a 96-well microtiter plate were added 100 \(\mu\)L of modified Brain Heart Infusion broth (HiMedia Laboratories; Mumbai, India) and an equal volume of the prepared suspension. As positive and negative controls, the biofilm-forming _Staphylococcus aureus_ CCM 4223 and the non-biofilm-forming _Staphylococcus epidermidis_ CCM 4418 (both from the Czech Collection of Microorganisms) were chosen.  
After a 24 h incubation period at 37 \({}^{\circ}\)C, the planktonic cell medium was removed, and distilled water was used to wash the wells. The biofilms were stained using a 0.1% crystal violet solution (Merck; Darmstadt, Germany). The dye, after incubation for 30 min at room temperature, was carefully removed from the wells. The wells were repeatedly washed with distilled water and then allowed to dry at room temperature. The residual dye was eliminated using 30% acetic acid. Finally, the optical density (OD) was determined using a Synergy reader (Merck; Darmstadt, Germany) at a wavelength of 550 nm.  
For the interpretation of the results obtained, the strains were classified as non-biofilm producers (OD \(\leq\) 0.150), weak biofilm producers (0.151 \(\leq\) OD \(\leq\) 0.300), moderate biofilm producers (0.301 \(\leq\) OD \(\leq\) 0.600) or strong biofilm producers (OD \(\geq\) 0.601) according to the criteria described by [PERSON] et al. [27] with modifications according to [PERSON] et al. [28].
# A Review of Requirements for Gamma Radiation Detection in Space Using CubeSats
## 3 Gamma Radiation Detection Strategy and Reference Detector
### Photosensors
In order to operate PMTs in space environment, safety actions to prevent and mitigate electric discharges must be implemented. Electrical arcing might be facilitated by the satellite components outgassing in presence of high electric fields or by high ionization due to exceptional radiation intensity typical of specific regions of the orbit such as South Atlantic Anomaly. Nowadays, off-the-shelf products suitable for aerospace applications are largely available; however, the choice needs to reflect the specific requirements of the payload. The Mo \begin{table}
\begin{tabular}{c c c} \hline \hline
**Characteristics** & **R11265-200 PMT** & **S13361-6050 NE-04 MPPC** \\ \hline Dimensions & & \\ (L \(\times\) D \(\times\) H [mm \(\times\) mm \(\times\) mm]) & 26 \(\times\) 26 \(\times\) 19 & 25 \(\times\) 25 \(\times\) 1.4 \\ \hline Weight [g] & 24 & 2 \\ \hline Peak Sensitivity [mm] & 400 & 450 \\ \hline Quantum Efficiency at Peak [\%] & 43 & - \\ \hline Photo Detection Efficiency at Peak & - & 40 \\ [\%] & 900 & 55-58 \\ \hline Typical Gain at working point & \textgreater{}10^{6}\) & \textgreater{}10^{6}\) \\ \hline Dark Count at working point, room & & \\ temperature [Hz] & few & \textgreater{}10^{7}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Characteristics of the two devices (PMT and MPPC) considered in the paper. mentive RTV615 is a clear silicon rubber compound that offers high dielectric strength (19.7 kV/mm), low viscosity that allows for an easy pouring and low density (1.01 g/cm\({}^{3}\)) for easing the constraints on the mass budget, and it is vibration-tolerant and flexible at the lowest temperature (down to \(-115\)\({}^{\circ}\)C). While the Momentive RTV615 can be a good encapsulating option for most of the applications, it is possible that the heat generated in the payload board needs to be dissipated or radiated outside. The Epoxies 50-3185 NC (cured with Catalyst 30), a filled epoxy composite, is an excellent option when the application requires an encapsulant that offers high dielectric strength (14.5 kV/mm), optimal mechanical properties and vibration tolerance, and high thermal conductivity (1.31 W/m\({}^{\circ}\)K). However, its fairly high density (2.3 g/cm\({}^{3}\)) can pose restrictions in its use when mass budget is concerned. The use of encapsulants is generally also recommended for improving the mechanical stability of the payload subjected to the vibration stress typical of the launch phase.
# Deep Learning and Embedding Based Latent Factor Model for Collaborative Recommender Systems
## 5 Experiments
### Baseline Methods  
To evaluate the performance of the proposed model, DELCR, we compared it with the state-of-the-art models for MAE and RMSE evaluation metrics provided in [29].  
* For collaborative filtering, MDA employs an autoencoder architecture with MF for model prediction [35].
* CDL provides a hierarchical Bayesian model by combining two methods. It enables the acquisition of an appropriate latent feature to address the sparsity problem [12].
* RMF model uses Manhattan distance to minimize the distances within matrices. It successfully solves the problem of data sparsity [36].
* DMF is a Deep MF model that uses MLP to transform the representation of the user and item [8].
* HLFM is one of the MF-based algorithms that investigates the underlying hierarchical characteristics of users and items. To increase the effectiveness of RSs, the approach integrates the hierarchy of items as well as user preferences [37].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **Users** & **Items** & **Ratings** & **Rating Density** \\ \hline ML-100K & 944 & 1683 & 100,000 & 6.3\% \\ ML-1M & 6040 & 3706 & 1,000,208 & 4.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of the datasets used.  
* DLFCF is a CF model based on latent factors. The model uses deep factorization on users and items to determine their latent representation vectors [29].
* DELCR is the model presented in this paper.
## 3 Results
### Pilot Adjustment of Early Unified Control Points (2008-2010) for Strategy Validation
These vector-based comparisons provide a compelling visual corroboration of the statistical results presented in Table 6, highlighting the extent to which the integrated adjustment strategy mitigates spatial discrepancies. The consistency in vector directions and magnitudes across the network reveals that the unified adjustment not only enhances horizontal positional coherence but also significantly reduces vertical deviations. This integrated approach ensures spatial continuity and eliminates localized distortions introduced by regionally constrained adjustments, thereby reinforcing the robustness and reliability of the national geodetic framework. Prior to the nationwide adjustment of over 17,000 RINEX datasets, a preliminary test adjustment was conducted using data from the initial three years to evaluate software performance. For this purpose, both GAMIT and Bernese were employed to compare the suitability of each processing strategy. As shown in Figures 8 and 9, notable differences between the two solutions are observed. In the horizontal component, the GAMIT results exhibit systematic residual trends when compared to KGD2002, with deviations largely oriented in a uniform direction. In contrast, the Bernese solution yields smaller residual magnitudes but presents a more scattered directional distribution. Figure 9: Vertical coordinate differences between previous KGD-based adjustments and the integrated adjustment of UCPs from 2008 to 2010. For the vertical component, the GAMIT-derived solution shows minimal differences from KGD2002 in the 2008 dataset, particularly in metropolitan regions such as Seoul, Gyeonggi, and Daejeon. However, larger discrepancies appear in the 2010 dataset, especially in mountainous regions like Gangwon Province. The Bernese results exhibit generally larger vertical discrepancies than GAMIT across all regions and epochs. Based on these comparative findings, GAMIT was selected for the full-scale network adjustment due to its systematic behavior and relatively consistent vertical accuracy. Further comparative research between GAMIT and Bernese using the full set of 17,000 RINEX files is planned as part of ongoing efforts to assess the long-term performance and consistency of both software platforms in national geodetic applications.
## 3 UAV Computing  
Each UAV is equipped with a microcontroller AI-capable processor board and wireless transceivers as a minimum design requirement [40]. UAVs are fitted with AI-capable processor boards with controllers that process commands and allow them to be controlled remotely using a remote control if desired. In addition, UAVs are equipped with wireless transceivers that allow them to communicate with one another and other objects on the ground, such as the center of the disaster and the command and control center. An ad-hoc UAV network is formed when two or more UAVs communicate to accomplish a single goal. The advancements in the ad-hoc network among UAVs meet the two primary criteria of UAV systems: (1) mobility and (2) adaptable topology of the network.  
The ad-hoc network allows packets of data to be transferred instantly (in real-time) across networked UAVs [40; 41; 42; 43]. As a result, the shared packets of data are routed across the networked UAVs along a path determined by the routing protocol. According to [40], there are two basic routing protocols: proactive routing protocols and reactive routing protocols. The total available network bandwidth will be reduced due to the large number of packets that are frequently transferred across the network to maintain connectivity. Furthermore, as packets need to be transferred between UAVs, the shortest pathways need to be known in reactive routing. As a result, reactive routing takes longer than proactive routing, as it requires knowing pathways ahead of time and has a shorter End-to-End (E2E) delay when transferring packets. Therefore, the trade-off between bandwidth and delay should be considered when selecting routing protocol hierarchies [44]. One sub-group of UAVs may be constructed just for long-range communication services to interface with external networks. In contrast, another sub-group of UAVs may be developed solely for sensingand monitoring [45], and may carry specialized sensors, actuators, and cameras. All nodes will exchange packets with UAVs and center units if necessary [44].
# Development of Fire Consequence Prediction Model
## 3 Configuration of the Prediction Model  
### Configuration of the Neural Network of the Prediction Model  
\"Deep\" implies that there are many layers in a neural network and many variables to be considered for each layer. To differentiate neural networks by the depth of the layers, a neural network with a depth of two-to-three layers is known as a shallow network, whereas a neural network with layers of more than two-to-three is known as a deep network. In deep learning, the number of layers that represents the depth refers to the number of hidden layers between the input and output layers. Therefore, deep learning involves a deep neural network (DNN), which is represented as shown in Figure 4 [13].  
Adam optimizer, which is used for optimization, is a training algorithm provided by TensorFlow for calculating the error function from the given training-set data and updating parameters in the direction opposite to its gradient vector. Furthermore, it is one of the widely used algorithms in deep learning because its structure automatically adjusts the learning-rate parameter, it yields a relatively good performance, and there is no need to manually adjust the learning rate. Moreover, the data are normalized to a value between 0 and 1, where the activation function is operated to effectively perform the training process [14,15]. The normalization equation is as follows:  
\[\overline{d}=\frac{d-\text{min}v}{\text{max}v-\text{min}v}, \tag{1}\]  
where \(\overline{d}\) is the normalization data, \(d\) is the data before normalization, \(\text{min}v\) is the minimum value of the feature, and \(\text{max}v\) is the maximum value of the feature.  
The main role of the activation function used in the hidden layer is to convert signals gathered by neurons to a state that has a slightly higher discriminative power and to filter meaningless data in advance by applying a threshold. The appropriate weight that acts during the process of information transfer between neurons must be known to obtain the desired result from a feedforward neural network. In other words, if a specific pattern  
Figure 3: Simplified process flow diagram of the ship-to-ship LNG bunkering [12].  
recognition mission is assumed to be a function, the process of finding this function by adjusting the weight can be considered to be training [15].  
Unlike the sigmoid function with values ranging from 0 to 1, the rectified linear unit (ReLU) function processes all values below 0 as 0. ReLU can be implemented with a simple formula, and it also resolves the vanishing gradient problem, which was considered a problem in the deep learning model. He initialization, which is well suited for ReLU, was used to initialize the weights [14]. Therefore, when the DNN is configured, the sigmoid activation function is used only in the places that show the final value, and the ReLU function is used as the activation function between layers so that the weight update can be evenly spread. Figure 5 displays a comparison of the sigmoid and ReLU functions.