esa-sceva/satcom-chunk-collection huggingface dataset:
# Geographic Routing Decision Method for Flying Ad Hoc Networks Based on Mobile Prediction
## 2 Related Work
### Proactive Routing  
The proactive routing protocol [23] maintains the global topology through regular information exchange between nodes. Nodes not only discover neighbors by sending Hello packets but also exchange routing information to perceive real-time changes in network topology. The advantage of this method is that it can reduce the delay during route discovery.  
[PERSON] et al. [24] proposed using a combination of graph neural networks and software defined networks to optimize routing selection in the network through the learning ability of graph neural networks in order to meet the transmission needs of different types of data packets. However, since FANET has higher mobility than ordinary ad hoc networks, [PERSON] et al. [25] proposed an improved OLSR (optimized link state routing) protocol based on UAV residual energy and node degree, which addresses frequent topology changes by predicting link stability, thereby reducing routing overhead and improving network stability. However, the fixed time slot Hello packet mechanism may not be able to update routing information in a rapidly changing network environment, resulting in significant overhead. [PERSON] et al. [26] proposed a dynamic perception of changes in the number of neighbors in the environment and dynamically adjusted the time interval for broadcasting Hello packets using an adaptive link maintenance method based on deep reinforcement learning. The IEM protocol [27] dynamically adjusts the Hello message interval through DQL. Another improvement based on fuzzy logic is OLSR+ [28], which combines link quality prediction technology to optimize the link state routing protocol, making it more suitable for the FANET environment.  
However, OLSR+ still faces issues of high update latency and routing overhead when facing high dynamic networks. In order to reduce the problem of update delay, [PERSON] et al. [6] introduced an active ant colony update mechanism called proactive ant colony routing, which can quickly respond to network topology changes through more frequent pheromone transmission. However, frequent routing updates still incur additional computational and communication overhead.
# Achieving Excellence in Cyber Fraud Detection: A Hybrid ML+DL Ensemble Approach for Credit Cards
## 4 Method  
This section describes the successful development of a hybrid ML+DL approach that integrates the ML techniques: DT, RF, SVM, XGBoost, CatBoost, and LR with the DL techniques CNN and BiLSTM. Figure 2 illustrates a block diagram of the proposed modelling framework that was developed in this study.  
The heart of the model lies in its architecture, which includes both ML classifiers and a DL model. The classical classifiers include RF, SVM, LR, DT, XGBoost, and CatBoost. These classifiers are trained individually on the training data and evaluated using standard metrics. Additionally, we introduce a CNN-BiLSTM with an attention mechanism. The CNN architecture consists of convolutional layers followed by batch normalisation, max-pooling layers, Bidirectional LSTM layers, Dropout layers for regularisation, and a custom AttentionLayer. This AttentionLayer helps the model focus on important features. The CNN is trained alongside the classical classifiers and is evaluated similarly. Moreover, we employ various callbacks during training, such as F1 ScoreCallback, ModelCheckpoint, ReduceLROnPlateau, and EarlyStopping, to monitor the model's performance and prevent overfitting. Finally, we build a StackingClassifier that combines the predictions of all classifiers ML and DL, using an RF classifier as the final estimator. This StackingClassifier is trained on the training data and evaluated on the test set. The performance metrics, including accuracy, precision, recall, F1-score, ROC AUC score, and confusion matrix, are computed and visualised for comprehensive analysis. In summary, the novel hybrid stacking ML+DL model is a hybrid approach that combines the strengths of classical ML algorithms with the representation learning capabilities of a CNN-BiLSTM with an attention  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metrics** & **Description** & **Equation** & **Range** \\ \hline Accuracy & Assess the number of TPs & \(A=\frac{TN+TP}{TN+TN+TP+FP}\) & [0–1] \\ \hline Recall & The ratio of TP to a TP and FN & \(R=\frac{TP}{TP+FN}\) & [0–1] \\ \hline Precision & The ratio of TP to a TP and FP & \(P=\frac{TP}{TP+FP}\) & [0–1] \\ \hline F1-Score & Combines precision and recall & \(F_{1}=2\frac{P*R}{P+R}\) & [0–1] \\ \hline AUC & The area between two points bounded by the function and the x axis. & \(AUC=\int_{a}^{b}f(x)dx\) & [0–1] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance indicators.  
Figure 2: Hybrid ML+DL model.  
mechanism, resulting in a robust and effective framework for detecting fraudulent transactions.
## 2 Literature Review
### Theoretical Framework and Development Contexts  
Recent scholars in IT for development have emphasised the need to contextualise appropriate theoretical frameworks when examining technology adoption in developing nations [51; 52]. As [PERSON], [PERSON] [53] and [PERSON] [54] posited, no single theory or framework can accurately explain technology adoption without considering the specific development context. This observation is particularly relevant for government cloud adoption in Nigeria, where institutional, infrastructural, and socioeconomic factors create a unique environment for technological implementation.  
The UTAUT provides a solid foundation for examining technology adoption factors but requires adaptation to address the specific development challenges faced by governments in emerging economies. Building on [PERSON], [PERSON] [55] and [PERSON], [PERSON]'s [56] call for contextually sensitive theoretical approaches in IT for development research, we adapt the UTAUT to incorporate development-specific constructs, including governance frameworks, information security, privacy, and perceived risk, all factors that take a heightened significance in a context where institutional capacity and trust may be limited.  
[PERSON], [PERSON] [57] surveyed cloud computing providers' concerns about information security and privacy and identified security and privacy as the significant barriers to cloud computing adoption. [PERSON], [PERSON] [58] found security and privacy as significant factors that influenced the adoption of cloud computing for e-government implementation, while other notable researchers identified information security and privacy as key barriers and challenges to the government's intention to adopt cloud services [16; 31; 59; 60; 61; 62; 63].  
Furthermore, the ITU [42] paper on privacy for cloud computing highlighted the difficulties faced by CSPs in ensuring data-level compliance with geographic restrictions on data transfers. This bolsters the need for cloud consumers to maintain compliance controls that expressly meet privacy- and security-related requirements. [PERSON], [PERSON] [16] and [PERSON] and [PERSON] [61] believe that technology could be adopted to mitigate the issues. Still, other researchers [64; 65; 66] suggest that developing a process, such as a governance framework, would better address the challenges and encourage adoption. Therefore, assessing these challenging factors influencing the government's intention to adopt cloud services and to enhance socioeconomic development outcomes is paramount.
## 2 Multiprecision Hardware
### Multiprecision Emulators  
The straightforward execution of a piece of code on low-precision hardware likely will produce unacceptable results due to the quality loss or iteration divergence. Therefore, it is useful to experiment with different precision levels to understand the performance of a numerical model prior the porting it onto specialized hardware. To address this goal, a variety of programming tools have been developed to be able to propagate the round-off errors, tune the floating-point numbers' precision and produce a mixed-precision version of a specific piece of code. One can find a comprehensive review on multiprecision analysis in [33].  
A reduced-precision emulator (RPE) [34] provides the capability to emulate calculations in arbitrary reduced floating-point precision. The software program is implemented as a Fortran module that can be included into existing projects. The module defines a new derived type \"pre-var\" that emulates a reduced-precision variable and can replace Fortran's built-in real type in the user's code. The module provides an overloaded assignment, arithmetic and logic operators that accept \"pre-var\" instances as inputs, along with overloaded versions of many of Fortran's intrinsic functions. The reduction of the precision is applied after each operator or intrinsic function call, while the operations are performed using single-precision arithmetic.
# A Linear Memory CTC-Based Algorithm for Text-to-Voice Alignment of Very Long Audio Recordings
## 3 Method
### The Textbook DTW Alignment Algorithm
#### 3.1.2 \(\boldsymbol{\pi}^{*}\)  
Computation via Dynamic Programming  
Solving Equation (4) directly is usually intractable, as there are too many possible paths between the two sequences. It is, however, possible to compute it indirectly, noticing that the optimal similarity measure between two sequences can be obtained recursively, considering the similarity measure between the two sequence _prefixes_.  
As depicted on Figure 3, if the optimal path between two sequences passes through the point \((i,j)\), it necessarily passes through one of the points \((i-1,j-1)\), \((i,j-1)\), or \((i-1,j)\). It is thus enough to know the optimal similarity measure on these 3 predecessors to deduce the optimal similarity measure on the point \((i,j)\).  
Let \(\alpha(i,j)\) denote the optimal alignment similarity between the prefix \(\mathbf{a}[1:i]\) and the prefix \(\mathbf{b}[1:j]\), and assume that \(\alpha(i-1,j-1)\), \(\alpha(i-1,j)\), and \(\alpha(i,j-1)\) are already known. Given the permitted path transitions depicted on Figure 3, it is straightforward to see that:  
\[\alpha(i,j)=\mathcal{S}(a_{i},b_{j})+\max\begin{cases}\alpha(i-1,j-1)\\ \alpha(i,j-1)\\ \alpha(i-1,j)\end{cases}\quad\forall(i,j)\in\{2,\ldots,|\mathbf{a}|\}\times\{ 2,\ldots,|\mathbf{b}|\} \tag{6}\]  
The initialisation step is given by:  
\[\begin{cases}\alpha(i,1)=\sum_{k=1}^{i}\mathcal{S}(a_{1},b_{k})\\ \alpha(1,j)=\sum_{k=1}^{i}\mathcal{S}(a_{k},b_{1})\end{cases}\quad\forall(i,j )\in\{1,\ldots,|\mathbf{a}|\}\times\{1,\ldots,|\mathbf{b}|\} \tag{7}\]  
The optimal global alignment similarity \(\mathrm{A}^{*}=\alpha(|\mathbf{a}|,|\mathbf{b}|)\) can then be computed recursively using Equations (6) and (7), accumulating at each step the similarity measure of the best predecessor optimal path (forward pass). The optimal path \(\boldsymbol{\pi}^{*}\) can then be traced back using the sequence of optimal transitions retained in the forward pass (backward pass).  
The computation of the matrix \(\boldsymbol{\alpha}\) during the forward pass has a computational and memory complexity that scales quadratically in \(\mathcal{O}(|\mathbf{a}||\mathbf{b}|)\). This rapidly becomes problematic for long sequences.  
Figure 3: Textbook DTW alignment path-permitted transitions
###### Abstract  
(1) Background: the objective of this study was to examine the impact of defatted mealworm hydrolysate (DMH), formulated through protein hydrolysis, on muscle protein synthesis in C2C12 cells and rats; (2) Methods: C2C12 cells were treated with dexamethasone and DMH, and cell viability was quantified using the MTT assay. Twenty-four Sprague-Dawley rats were divided into three groups (control, DEX, and DEX + DMH) and treated for 8 weeks. The DEX and DEX + DMH groups were administered intraperitoneal injections of DEX at a concentration of 2.25 mg/kg over a 3-d period. The control and DEX groups were fed a control diet, whereas the DMH group had part of the protein composition of the control diet replaced with 3.5% of DMH. The impact of DMH on muscle protein synthesis was evaluated through the measurement of grip strength, gastrocnemius and tibialis anterior muscle weights, and the investigation of muscle protein synthesis and degradation factor mRNA expression utilising the real-time PCR method; (3) Results: in vitro experiments demonstrated that treatment with DMH at concentrations greater than 5 mg/mL markedly alleviated DEX-induced injury in C2C12 cells. In vivo experiments demonstrated that the mRNA expression levels of myogenin and myoblast determination proteins, which promote muscle protein synthesis, were significantly increased. Furthermore, rats fed DMH exhibited significantly enhanced grip strength and tibialis anterior weight; (4) Conclusions: these findings indicate that DMH may serve as a functional material capable of promoting muscle protein synthesis and that the utilization of proteolytic enzymes is advantageous for the effective utilization of mealworms.  
defatted mealworm hydrolysate; dexamethasone; MyoD; myogenin; muscle protein synthesis Article
# Training Spiking Neural Networks with Metaheuristic Algorithms
## 3 SNN Model and Learning Method  
### Spiking Neuron Model  
There are several spiking neuron models, such as Leaky Integrate-and-Fire (LIF) [54], Izhikevich [55], the Morris-Lecar model [56], the FitzHugh-Nagumo model [57], and Hodgkin-Huxley (HH) [58], each having its advantages and disadvantages. Some of them are simple but not biologically accurate, whereas others are biologically plausible but costly to simulate.  
The neuron model used in this paper is the Integrate-and-Fire (IF) model because of the limited resources needed to implement it and the speed of simulation. The IF is a low-computing-cost and one-dimensional neuron model. The membrane potential of the \(j^{th}\) neuron in the \(l^{th}\) layer is determined by:  
\[\mathrm{V}_{j}^{l}(\mathrm{t})=\sum_{i}\omega_{ij}^{l}\sum_{\tau=1}^{t}S_{i}^ {l-1}(\tau) \tag{1}\]  
where \(S_{i}^{l-1}\) denotes the spike train and \(\omega_{ij}^{l}\) is the input synaptic weight. An output spike occurs when the membrane voltage surpasses the firing threshold \(\mathrm{V}_{j}^{l}(\mathrm{t})\geq V_{th}\). Figure 2a depicts an overview of an IF neuron.  
Figure 2: (**a**) An overview of an IF neuron. Whenever the membrane voltage exceeds the firing threshold, an output spike occurs. (**b**) Schematic representation of rate and temporal coding. In a temporal-based encoding, information is encoded through specific spike times, whereas the rate encoding is based on a spiking feature during a time interval.
## 3 Results and Discussion
### Metabarcoding vs. Culture-Dependent Diversity and Biodeterioration-Related Organisms
Moreover, _Alternaria infectoria_ and black fungi are also known to contribute to stone biopitting through physical action, triggered by disaggregation of rock grains determined by the internal pressure caused by hyphae penetrating stone [26; 123]. Among biochemical mechanisms induced by fungal metabolism, the production of inorganic acids (e.g., nitric, sulfuric, and carbonic) and organic acids (e.g., oxalic, citric, gluconic, malic, etc.) represents a significant part in biodeterioration processes [24]. _Aspergillus fumigatus, Epicoccum nigrum, Penicillin brevicompactum_ and _Penicillin corylophilum_ are known producers of oxalic acid. Oxalate is a key fungal metabolite and considered one of the most important in terms of its role in biodeterioration, biocorrosion and bioweathering, often associated with fungal-mediated rock and mineral transformations, that seems ubiquitous in the context of biodeterioration [124]. Other acid-producing species isolated in this study include _Alternaria alternata_ (tenuazonic acid), _Cladosporium tenuissimum_ (formic, fumaric, gluconic and lactic acids), _Epicoccum nigrum_ (acetic, butyric, citric, fumaric, malic and succinic acids) and _Penicillin brevicompactum_ (citric, fumaric and gluconic acids). Due to the acid-producing nature of the above species, they likely contribute to the acidification and dissolution of limestone minerals [14; 125]. Additionally, fungal species with known CaCO\({}_{3}\) dissolution abilities, such as _Accmonium charticola, Cladosporium spharospernum_, _Cyphellophora olivacea, Parengyodontium album_ and _Penicillin brevicompactum_; and mineralization or crystallization abilities, such as _Aeminium ludgeri_, _Alternaria alternata_, _Alternaria infectoria_, _Cladosporium cladosporoides_, _Cladosporium spharospernum_, _Epicoccum nigrum_ and _Penicillin brevicompactum_[62], have also been retrieved in this study. Despite bacteria not being an object of this study, the frequent isolation of a filamentous _Actinobacteria_ was an interesting result. Some organisms from this phylum (e.g., _Streptomyces_) have been associated with damaged limestone, frequently promoting a decrease in pH, therefore working as biodegradation phenomena indicators [24; 126].
# Evaluation of the Digital Data Performance Transmission on a Fiber-Radio System
## 2 Optical and Wireless Transmission Schemes
_\(\mu\)_ is the propagation loss exponent with values between 2.5 and 6, or even higher, depending on the type of environment. As an urban environment is being considered, then \(\mu\) = 4 [15]. \(h\) is a Rayleigh random variable whose models caused fading by multiple signal paths (fast fading) with standard deviation \(\Delta\)[4]. _\(\ell\)_ is a log-normal random variable (\(10^{\frac{\zeta}{10}}\)) that represents shadowing losses (slow fading). It is defined as a function of the Gaussian random variable \(\zeta\), with mean 0 and standard deviation \(\sigma\)[13; 14]. The performance of the forward link of the RF system is evaluated in terms of the SINR [13; 14]  
\[SINR=\frac{Prx}{z_{i}+CCI} \tag{2}\]  
where \(z_{i}\) is defined as the AWGN signal [4] with a standard deviation \(\sigma\), and \(CCI\) is the additive co-channel interference signal [5]. Thus, \(CCI\) is modeled as  
\[CCI=\sum_{j=1}^{6}Prx_{j}=\sum_{j=1}^{6}\frac{y_{j}\ h_{j}\ell_{j}}{d_{j}^{\mu} }=\sum_{j=1}^{6}\frac{y_{j}\ h_{j}(10^{\frac{\zeta_{j}}{10}})}{d_{j}^{\mu}} \tag{3}\]  
where \(j\) = 1,..., 6 is the number of each neighbor base station. Figure 4: Layout of the coverage area of a mobile user in a hexagonal cell distribution. Thus, the total signal x received by U1 is given by [4; 5]  
\[x=Prx+(z_{i})+CCI \tag{4}\]  
The _SINR_, expressed in decibels, is given by [13; 14]  
\[SINR\ [dB]=10 log(SINR) \tag{5}\]  
The parameters considered for the performance evaluation in terms of the _SINR_ using the Monte Carlo simulation are listed in Figure 3:0
# Inland Water Level Monitoring from Satellite Observations:
## 3 Review Results
### Waveform Retracking
#### 3.2.2 Official Retracking Algorithms
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Publication** & **Satellite Mission** & **Data** & **Retracker** & **Validation Data** & **Accuracy Metrics** \\ \hline \multirow{4}{*}{[134]} & \multirow{2}{*}{Sentinel-3} & \multirow{2}{*}{Level-2 (L-2)} & \multirow{2}{*}{Ocean, OCOG} & \multirow{2}{*}{In-situ gauge} & Ocean: & \multirow{2}{*}{Ocean: \(\text{R}=0.25\)-0.30 m, \(\text{R}=0.86\) and for OCOG: \(\text{R}=0.93\). Percentage of outliers} \\  & & & & & \\ \hline \multirow{4}{*}{[135]} & \multirow{2}{*}{Sentinel-3} & \multirow{2}{*}{Acquired by G-POD} & \multirow{2}{*}{OCOG} & \multirow{2}{*}{In-situ gauge} & R = 0.937-0.941, \(\text{RMSD}=0.43\)-0.45 m, \(\text{ubRMSE}=0.37\)-0.42 m \\ \cline{1-1} \cline{2-5} \cline{6-6} \cline{
## 4 Case Study: Analysis of a Device
### Application of the Methodology
#### 4.2.1 Phase 1: Preliminary Check of the Device's Security Mechanisms  
In the case study, the device had an activated lock screen (alphanumeric characters), bootloader blocked, USB debugging mode disabled, and FRP active. Figure 9 displays information about the devices firmware version, OS version, device model, and binary firmware version.  
Figure 8: Flowchart of the LLDA-ISPCF methodology applied in the case study.
# Dynamic Response Analysis of Mega-Sub Isolated Structures under Multiaxial Earthquakes
## 5 Parametric Analysis of Main and Sub\(-\)Frame Collisions
### Influence of Subframe Height
#### 5.5.1 Influence on Collision Forces  
Figure 21 shows the change of the peak collision force of the main and substructure with the change of substructure height under the action of three seismic waves earthquakes. It can be seen that the changing pattern of the peak collision force under the action of three seismic waves is quite different. Under the action of the El Centro wave, the peak collision force exhibits a trend of initially decreasing, then increasing, followed by another decrease, and finally continuing to increase. The maximum collision force occurs when the substructure has 6 stories. For the Taft wave, the peak collision force generally follows a trend of initially decreasing, then increasing, and then decreasing again. The maximum peak collision force occurs when the total number of substructure stories is 10. As for the artificial wave, the variation pattern of the peak collision force is similar to that of the El Centro wave. The maximum collision force occurs when the total number of substructure stories is 12.  
For the sub-structure, the impact of its height on the main and sub-structure under different earthquake actions need to be more consistent. Therefore, when designing the height of the sub-structure, specific analysis, and reasonable design should be carried out according to different load conditions.
# A Comprehensive Review of In-Body Biomedical Antennas: Design, Challenges and Applications
## 3 Antenna Design, Manufacture and Testing
### Testing of In Vivo Antenna  
In vitro study is commonly carried out in an artificial biological environment, which cannot confirm the stability of the implanted antenna system because of the lack of dynamic illustration of a real biological environment in the in vitro study [51]. Therefore, the testing of in-body antenna in a real biological environment (in vivo) is commonly suggested after in vitro testing. Before implantation of the in-body antenna prototype inside the biological body, it is necessary to ensure that the temperature of the testing environment is below 100 \({}^{\circ}\)C. Generally, the in-body antenna itself generates heat up to 60 \({}^{\circ}\)C because of the battery and other internal system devices. Furthermore, the in-body antenna must be insulated by using biocompatible material to protect the antenna system from coupling loss, as described in Section 2.5. Figures 7 and 8 [23; 77] illustrate the in vivo testing of the glucose monitoring implantable antenna in a rat and monitoring of blood pressure of the left ventricle, respectively.  
Figure 6: (**a**) Phantom setup. (**b**) Indentation of capsule in phantom [13]. Reproduced with permission from an open-access article from _IEEE_ (CC-BY).  
Figure 7: Multi-layer implantable antenna measurement for continuous glucose monitoring. (**a**) A sensor implanted in a rat, (**b**) Experimental setup [77]. Reproduced by courtesy of the Electromagnetics Academy.