esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Recent Trends, Observations, and Future Research Challenges
### Research Trends and Observations
This adaptability further mitigates latency issues by prioritizing critical information transmission for real-time applications. NOMA's inherent scalability ensures effective connectivity in dynamic environments, accommodating a growing number of devices and users. Moreover, its compatibility with emerging technologies like edge computing and machine learning further enhances performance and facilitates efficient resource management and optimization. * _Machine learning-enabled IoT: Optimizing Network Performance_ Machine learning is revolutionizing IoRT by unlocking the full potential of NTN systems. ML algorithms excel at optimizing resource allocation within the NTN network and for its users. They achieve this through efficient bandwidth distribution via techniques like predictive demand forecasting, dynamic traffic routing, and priority-based scheduling. Furthermore, reinforcement learning and deep RL can be effectively harnessed to mitigate the Doppler effect in satellite communication. These techniques accurately predict frequency changes and promptly adapt modulation techniques, leading to improved signal quality and data transmission. Beyond network optimization, ML plays a crucial role in data management. It helps identify anomalies, reduce data size, and extract relevant features, enabling efficient analysis and informed decision-making. This empowers the use of NTN for the IoRT, providing robust and effective connectivity solutions for diverse IoRT challenges. * _MEC-enabled IoRT Task Offloading_ Integrating MEC architecture with NTNs holds significant potential to enhance the performance of NTN-based IoRT systems. Depending on the NTN architecture, this integration can be achieved by incorporating computing capabilities into satellites or UAVs, essentially forming edge servers. These edge servers, positioned closer to IoT devices, can offload IoRT processing tasks, reducing reliance on distant cloud servers. Utilizing coordination techniques, often based on ML, enables the dynamic allocation of offloaded tasks to achieve various objectives, such as energy-efficient channel allocation, maximizing the number of offloaded tasks, or minimizing latency through delay-aware task offloading. Furthermore, real-time collaboration may enable edge nodes to exchange processed data and make collective decisions, further improving the effectiveness of MEC.
# Improving PM2.5 Concentration Forecast with the Identification of Temperature Inversion
## 1 Introduction
One class includes the days with temperature inversion, the other class includes the rest. The classification performances of the two classifiers are compared in terms of various metrics. (3) The meteorological features of the two training classes are contrasting and they have different relationships with the PM\({}_{2.5}\) concentrations. A separate regression model for PM\({}_{2.5}\) forecasting is trained for each group to enhance the overall prediction performance. (4) Our experimental results with the Puli PM\({}_{2.5}\) and meteorology datasets during the period from 2016 to 2019 show that the models considering the temperature inversion factor reduce the forecast errors by 4-9% and 2-7% in terms of RMSE and MAE, as compared to the original model. The remainder of this paper is organized as follows. Section 2 reviews the relevant researches on analyses of PM\({}_{2.5}\) concentrations in Taiwan, temperature inversion phenomenon, and existing PM\({}_{2.5}\) estimation approaches. Section 3 elucidates the proposed method for PM\({}_{2.5}\) forecast. Section 4 presents the experimental results and comparative performances. Finally, Section 5 concludes this work.
## 5 Proposed Framework
### Integration of ZTA and FL
#### 5.4.2 Security Performance  
The integrated framework demonstrates significant improvements over baseline approaches, as quantitatively verified in Table 11. Our solution reduces the poisoning success rate by 67.9% compared to standard FL implementations, achieving just 9% susceptibility to model poisoning attacks. The false acceptance rate shows even more dramatic improvement, dropping 78.1% to a mere 0.7%, indicating the highly reliable authentication of legitimate participants. Detection latency was slashed by 73.4% to 85 ms, enabling Near-Real-Time response to threats, while simultaneously improving model accuracy by 6.8 percentage points to 94%--a rare combination of enhanced security and improved utility.  
The framework's attack surface reduction capabilities are particularly noteworthy, achieving a 72% reduction in vulnerable endpoints through rigorous micro-segmentation and continuous authentication. Unauthorized API calls were reduced by 89% thanks to our behavioral analysis techniques that monitor call sequences and timing patterns. Despite these robust security measures, the system maintains excellent operational efficiency, adding only 3.5% CPU overhead for security operations. The end-to-end latency penalty is kept to just 18 ms through optimized cryptographic implementations, while maintaining a 92% valid update acceptance rate through our adaptive thresholding approach.
## 5 Implementation and Experiment Results  
This section presents the experiment setup and the analysis of results. The team conducted a set of experiments in order to validate the effectiveness of machine learning and deep learning techniques (decision tree (DT), \(k\)-nearest neighbor (KNN), random forest (RF), and convolutional neural network (CNN) classifiers) using the 87 features extracted from the raw mouse data. To avoid classifier biases, we trained all the experiments by using balanced training sets and evaluation sets. Scikit-learn software tools were used for the analysis of user behavior data [36]. The evaluations were measured using classifier accuracy (ACC), recall, precision, and F1-score. The experiments were conducted on the basis of two phases: (1) continuous authentication phase, and (2) anomaly detection phase. For each phase, each of the classifiers was trained and tested separately using each of these scenarios: a single mouse movement action (Scenario A), a single point-and-click action (Scenario B), and a set of mouse movement and point-and-click actions (Scenario C). We report evaluation results in terms of classification accuracy (ACC), area under the curve (AUC), false negative rates (FNRs), false positive rates (FPRs), and equal error rates (EERs). Besides these values, we report the performance of our detection system by receiver operating characteristic (ROC) curves.
## 4 Comparative Results
### Computational Complexity  
Regarding the computational complexity, the proposed embedding scheme includes two key loops. The aim of the first for-loop is to determine the total number of insignificant coefficients to determine the hiding capacity ratio. The second loop embeds the secret image in the DCT cubes of the cover signal.  
If it is presumed that \(T_{1}\) is the execution time for every iteration in the first for-loop, \(T_{2}\) is the execution time for every iteration in the second for-loop, and \(N\) is the total number of cubes, the computational complexity of the embedding process is:  
\[O([T_{1}+T_{2}]\times N)=O(N) \tag{10}\]  
The execution time and the total number of regions created for every cover image are proportional as can be seen in Equation (10). Therefore, the execution time increases linearly with the total number of segmented regions.  
However, compared to the technique proposed in [32], which had the same Big-O complexity as shown in Equation (10), the proposed Octree technique demonstrated faster execution speed. For a \(256\times 256\times 256\) video segment processed using an Intel Xeon 2.9 GHz processor with 32 GB of RAM, the Octree technique achieved an average speed of 16.7 s, while the directional scheme required 272.9 s. This improvement is due to Octree's use of cubes, as opposed to the pixel-by-pixel processing approach of the directional scheme.  
The execution speed of the proposed Octree technique can be further improved through parallel processing. By dividing the video segment into smaller regions and processing these regions simultaneously, the computational workload can be distributed across multiple cores or processors. This approach is particularly well-suited for the Octree technique, as its hierarchical structure and cube-based processing naturally lend themselves to parallelization.  
For instance, each level of the Octree or each sub-cube can be processed independently, significantly reducing the overall execution time compared to sequential processing. With modern hardware, such as multi-core CPUs or GPUs, parallel implementation could drastically enhance the performance, potentially bringing the execution time well below the 16.7 s achieved on a single-threaded Intel Xeon 2.9 GHz processor. This scalability highlights the potential of the Octree technique to handle larger video segments or real-time applications without sacrificing efficiency.
## 2 Dataset and Methodology
### Methodology
#### 2.2.3 Loss Formulation  
1. Adversarial Learning Objective  
The adversarial loss establishes a competitive learning paradigm between generators \((G,F)\) and discriminators \((D_{X},D_{Y})\). For the generator \(G\) mapping domain \(X\) (mining VHR imagery) to \(Y\) (road masks), the adversarial loss is defined as:  
\[\mathcal{L}_{\text{adv}}(G,D_{Y})=\mathbb{E}_{y\sim p_{\text{data}}(Y)}[\log D _{Y}(y)]+\mathbb{E}_{x\sim p_{\text{data}}(X)}[\log(1-D_{Y}(G(x)))]. \tag{7}\]  
Here, \(\mathbb{E}\) denotes the expectation operator, \(p_{data}(X)\) and \(p_{data}(Y)\) represent the empirical distributions of the source and target domains, \(G(x)\) generates pseudo-labels from input x, and \(D_{Y}(\cdot)\) evaluates the authenticity of samples in domain \(Y\). Symmetrically, the adversarial loss for generator \(F\) and discriminator \(D_{X}\) is formulated by swapping \(X\) and \(Y\).  
1. Cycle-Consistency Constraint  
To enforce bidirectional consistency between domains, the cycle-consistency loss ensures that translating a sample to the target domain and back reconstructs the original input [38]:  
\[\mathcal{L}_{\text{cyc}}(G,F)=\mathbb{E}_{x\sim X}[\lVert F(G(x))-x\rVert_{1 }]+\mathbb{E}_{y\sim Y}[\lVert G(F(y))-y\rVert_{1}]. \tag{8}\]  
The \(L_{1}\)-norm \(\lVert\cdot\rVert_{1}\) penalizes pixel-wise discrepancies between reconstructed \(F(G(x))\) and original \(x\), and similarly for \(G(F(y))\) and \(y\), ensuring structural coherence across translation cycles.  
1. Identity Preservation Mechanism  
An identity loss regularizes generators to preserve input content when already residing in the target domain:  
\[\mathcal{L}_{\text{id}}\ (G,F)=\mathbb{E}_{y\sim Y}[\lVert G(y)-y\rVert_{1}]+ \mathbb{E}_{x\sim X}[\lVert F(x)-x\rVert_{1}]. \tag{9}\]  
Here, \(G(y)\) denotes mapping a road mask \(y\) through generator \(G\), which should ideally output an unaltered mask to retain geometric integrity, while \(F(x)\) preserves spectral characteristics of mining images.  
1. Multi-Objective Optimization  
The total training objective combines adversarial, cyclic, and identity terms with balancing coefficients:  
\[\mathcal{L}_{\text{total}}\ =\mathcal{L}_{\text{adv}}+\lambda_{\text{cyc}} \mathcal{L}_{\text{cyc}}+\lambda_{\text{id}}\mathcal{L}_{\text{id}}. \tag{10}\]  
Here, \(\lambda_{\text{cyc}}\) and \(\lambda_{\text{id}}\) are hyperparameters controlling the relative importance of cycle consistency and identity preservation, and based on experience, \(\lambda_{\text{cyc}}=1\) and \(\lambda_{\text{id}}=0.5\). The generators G and F are trained to minimize \(\mathcal{L}_{\text{total}}\), while discriminators \(D_{X}\) and \(D_{Y}\) aim to maximize the adversarial component. Optimization employs the Adam algorithm with separate learning rates for generators and discriminators.
# High-Precision Combined Tidal Forecasting Model
## 2 Related Concepts  
### SVM and SVR  
Pioneered by [PERSON] in 1995, SVM is a nonlinear learning method with a solid theoretical foundation. Unlike other machine learning methods, such as neural networks, SVM implements the principle of Structural Risk Minimization (SRM) [15]. In convex quadratic programming problems, SVM seeks the best generalization performance by balancing the learning ability of the finite sample [16] against the complexity of the model. It has two variants: support vector classification (SVC) and support vector regression (SVR), which solve data classification and regression prediction problems, respectively. SVM architecture is shown in Figure 1, where \(x(n)\) is the input independent eigenvalue and \(K(X,X_{i})\)\((i=1,2,\ldots,n)\) is the kernel function. The independent variable \(x(n)\) is mapped to the high-dimensional feature space by the kernel function to realize linear regression in the feature space, and multiple linear regression is then performed in the high-dimensional feature space [17], and the output characteristic \(Y\) is obtained.  
In the present paper, the kernel is the popularly used radial basis function (RBF):  
\[K(x,x_{i})=\exp\left(\frac{-\|x-x_{i}\|^{2}}{2\times\delta^{2}}\right)=\exp\Bigl{(} -g\|x-x_{i}\|^{2}\Bigr{)} \tag{1}\]  
where \([x,x_{i}]\) is the initial low-dimensional feature space vector, \(\delta\) is the inner bandwidth of the kernel function, \(g\) is the parameter of the kernel function, and \(\|\|\) is the 2-norm operator. By mapping the RBF kernel function, each sample \((x_{i},y_{i})\) is maximally fitted to the following linear SVR model:  
\[Y_{i}=\left\langle\omega,x_{i}\right\rangle+b\ (\omega\in R^{n},b\in R) \tag{2}\]  
where \(\left\langle\right\rangle\) is the inner product notation and incorporates the kernel function; the weight vector \(\omega\) and bias \(b\) are obtained by solving the following optimization problems:  
\[\min_{\omega,b,\xi_{i}^{*}}\frac{1}{2}\|\omega\|+C\sum_{i=1}^{n}(\xi_{i}+\xi_ {i}^{*})\,, \tag{3}\]  
\[\text{s.t.}\left\{\begin{array}{l}y_{i}-(\omega x_{i}+b)\leq\varepsilon+\xi _{i}\\ (\omega x_{i}+b)-y_{i}\leq\varepsilon+\xi_{i}^{*}\\ \xi_{i},\xi_{i}^{*}\geq 0\end{array}\right. \tag{4}\]  
The factor \(C\) is the penalty factor, which compromises between the generalization performance and the training error, and \(\varepsilon\) is the maximum tolerance beyond which the optimization fails. \(\xi_{i}\) and \(\xi_{i}^{*}\) are relaxation variables that avoid over-fitting during data training, ensuring a certain fault tolerance of the model.
## 5 Results and Discussion
### Scenario 1
For stations LE05 and ZA02, the RF model outperforms the LR model, with the opposite being observed for the rest of the stations. ST_CNN_v2 is also capable of outperforming the rest of the baseline results, although the performance is always below that of the ST_Dilated_CNN model, except for the abovementioned case (ZA02 station), where it equals it.
# Security Risk Analysis of LoRaWAN and Future Directions
## Security Challenges of LoRaWAN v1.1
### Network Entities Related  
(a) Gateway related  
To our understanding, _G_Ws constitute the weakest link of the LoRaWAN. In most of the implementation scenarios, they are deployed few in number (sometimes even 1 or 2). Any kind of capture attack or physical attack would destroy the communication in between the _EDs_ and the rest of the network, i.e., servers.  
(b) Servers related  
Because of roaming, several _NS_'s are required, at least three as shown in Figure 2: _home_, _serving_, and _forwarding_. Besides, one _Join Server_ and one _Application Server_ are also required. All of these bring the following challenges:* These several network entities complicate network management. Orchestration of all these \(NS\)'s and _Join Server_, along with the _Application Server_ (see Figure 2 for the architecture of LoRaWAN v1.1) is non-trivial for the service provider.
* Hence all the keys are embedded (installed) upfront to the end-devices; ABP of LoRaWAN v1.1 does not support re-keying of the session keys. In LoRaWAN v1.1, re-keying of the session keys are only possible with _Join-initiation_ and _Re-Join_ commands during OTAA session. However, there is no apparent mechanism to perform re-keying of the root keys on demand or periodically. A solution needs to be devised so that re-keying would be possible for the root keys as well. A candidate solution can be implementing a mechanism to acquire usage of smart-phones or Bluetooth enabled platforms such as tablets to initiate and perform re-keying of the root keys on-site by using explicit certificates.
* There is a lack of the definition on the session (e.g., an OTAA session) durations of LoRaWAN v1.1: It is ambiguous that how long a session lasts. The dwell time of the signals on air is very well defined in Physical Layer specification, however the session times for the higher protocol layers are not indicated in the specification document.  
(c) End-device related  
Physical security of end-devices is an important task. To protect the network from physical attacks, especially from device capture attacks, tamper-resistant hardware should be used.
# Devising Mobile Sensing and Actuation Infrastructure with Drones
## 2 Designing DaaG
### Structure of DaaG
#### 2.2.3 Communication Modules  
In the new network paradigm, sensors and actuators can have different communication interfaces. Therefore, DaaG needs to support the various communication interfaces, such as Wi-Fi, Bluetooth, ZigBee, and so on. However, since beaconing for all communication interfaces results in the overhead of the battery, a communication scheduler uses the backoff-based beaconing algorithm for energy efficiency. Moreover, DaaG supports both single-hop and multi-hop communications. Drones can communicate with each other over other drones as long as there is a connected path. This component guarantees the connectivity of the features.
## 2 Case Study Site  
The Kuznetsky coal basin (Kuzbass) consists of several exploration districts, as illustrated in Figure 1. Herein, we focus on the Kondomsky district. Borehole studies carried out in the study cite have revealed the features of geologic bedding (on the right side of Figure 1). It was found that high beds of sandstone or siltstone typically form the main roof of several coal seams. The latter reduces the roof cavability, which increases the rock burst potential of coal. Seam 3 in this study has an immediate roof of \(\approx\)10 m of siltstone and the main roof composed of sandstone and siltstone of \(\approx\)20-25 m. For more details, the borehole log is given in Figure 2b.  
A three-dimensional geomechanical model (a cross-section of the model is given in Figure 2a) has been built on the basis of a simplified stratigraphy of the Kondomsky deposit (Figure 2b). The panels are located at the depths of \(\approx\)540-650 m; the inclined seam 3 in the study site has a dip angle of 16\({}^{\circ}\). The panel width is 200 m, the mining height is 3.6 m, the pillar width is \(\approx\)35 m, and the roadway width is 5 m. The roadways are indicated by Roman numerals. The stratigraphy above seam 1 is disregarded in the model; for simplicity, it is replaced by a single rock layer of a finite height to limit the influence of the boundary conditions. Other boundary conditions are discussed in the Section 3.  
The problem of maintaining the stability of the protective pillars in the area under consideration is also accounted for by significant technogenic seismicity. During the extraction of the first panel, a strong rock burst was recorded in the air roadway. As a result of the rock burst, the floor cracked, followed by a collapse of the roadway wall from the side of the stope spanning more than 40 m. Over the ten-month period preceding the rock burst, about 5500 events of various classes were recorded. More than 95% of them were classified as dynamic phenomena associated with the stope's advancement. Figure 3
## 1 Introduction  
Additive manufacturing (AM) is a layer-by-layer fabrication process that builds objects directly from digital models [1]. Among the different AM techniques developed over the past decades, Fused Deposition Modeling (FDM) and Vat Photopolymerization (such as Stereolithography, SLA, which uses a laser, or Digital Light Processing, DLP, which uses a projector) have become two commonly employed technologies for polymer parts. In FDM, thermoplastic filaments (e.g., Polylactic Acid (PLA) or Acrylonitrile Butadiene Styrene (ABS)) are heated and extruded, whereas SLA employs a laser to photopolymerize liquid resin [2,3]. These distinct mechanisms lead to trade-offs in surface quality, dimensional accuracy, part strength, cost, and post-processing requirements [4,5].  
Existing studies frequently compare AM technologies but often focus on only one or two performance attributes (e.g., mechanical properties or cost). FDM, for example, has been investigated extensively for its anisotropic tensile behavior and relatively rough surface finishes [4, 6], while SLA is acknowledged for delivering superior detail resolution but is criticized for higher material expenses and more complex post-processing [5, 7]. However, few publications provide a unified, multi-metric evaluation of FDM and SLA using carefully designed, identical geometries under standardized conditions. Consequently, a broader assessment is needed to understand how these methods perform with respect to accuracy, strength, cost, energy efficiency, and environmental impact.  
In an effort to address this gap, the present study exclusively compares FDM and SLA by manufacturing the same test geometries via each process and rigorously measuring multiple performance indices. Specifically, surface finish is quantified via profilometry, dimensional accuracy by means of standardized reference cubes, and mechanical properties using ASTM-based tensile testing. In parallel, production costs, energy consumption, and waste output are evaluated. By integrating these findings into a single, data-driven framework, this work aims to provide engineers, practitioners, and educators with a clear basis for selecting the most suitable AM technology for diverse project requirements.  
This paper is organized as follows: Section 2 critically reviews prior FDM vs. SLA studies and highlights unresolved issues in existing comparisons. Section 3 then describes the experimental design, including chosen geometries, printing parameters, and testing procedures. Section 4 presents a multi-faceted comparison of FDM and SLA, emphasizing key differences and trade-offs. Finally, Section 5 summarizes the results, offers practical guidance on technology selection, and proposes future directions to advance knowledge in this field.