esa-sceva/satcom-chunk-collection huggingface dataset:
# Leveraging Adversarial Samples for Enhanced Classification of Malicious and Evasive PDF Files
## 4 Results and Discussion
### Training and Validation Experiments
#### 4.1.1 Experiment 0: Illustrating the Problem and Establishing a Baseline  
To illustrate the main problem in question, we present this experiment where we train several malware classifiers on the non-evasive training set. We trained 8 different models, selected the one that performs best, and fine-tuned it to consider it as a baseline. The algorithms we tried are Logistic Regression, Support Vector Machine (with RBF and Polynomial kernels with degrees of 1 and 2), Naive Bayes, Random Forest Classifier, AdaBoost, and LightGBM. We first used these algorithms with their default hyperparameters as set by the sci-kit learn python's library. The performance of the resulting models on the non-evasive validation set is shown in Table 2. The results show an excellent performance of the different chosen models, with a slight improvement for the tree-based ensemble models (Random Forest, AdaBoost, and LightGBM). As such, we deferred the choice of the model to adopt. To evaluate the robustness of these models, we test each of them against the evasive validation set, and the results are reported in Table 3. We can see that the performance dropped significantly, and the classifiers that performed well on the non-evasive data, were \"fooled\" by the evasive data, and this can be seen from the high FNR. In the next three experiments, we implement the proposed approaches where we adopt LightGBM since it was the most robust model with an FNR of 41.14%, while the others had an FNR higher than 67.92%. We fine-tuned the hyperparameters of LightGBM using a 5-fold cross-validation method to obtain the best possible performance on the non-evasive validation data. The final hyperparameters are the ones mentioned in the Experimental Setup section. We will refer to this model as \"the baseline\", which we will use to compare against the outcomes of the other approaches. We evaluated this model (after fine-tuning) on the evasive and non-evasive validation sets, and the results are shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Logistic** & **SVM (RBF)** & **SVM (Poly 1)** & **SVM (Poly 2)** & **Naive Bayes** & **Random** & **AdaBoost** & **LightGBM** \\ \hline Accuracy & 94.43\% & 98.74\% & 92.32\% & 96.03\% & 82.81\% & 99.66\% & 99.32\% & 99.32\% \\ TNR & 91.97\% & 98.96\% & 88.30\% & 94.74\% & 97.14\% & 99.95\% & 99.55\% & 99.55\% \\ FPR & 8.03\% & 1.04\% & 11.07\% & 5.25\% & 20.86\% & 0.05\% & 0.45\% & 0.45\% \\ FNR & 2.75\% & 1.51\% & 3.06\% & 2.49\% & 12.99\% & 0.68\% & 0.94\% & 0.94\% \\ TPR & 97.25\% & 98.49\% & 96.94\% & 97.51\% & 87.01\% & 99.32\% & 99.06\% & 99.06\% \\ F1 Score & 94.21\% & 98.65\% & 91.71\% & 95.81\% & 82.51\% & 99.63\% & 99.27\% & 99.27\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of the tested models on the non-evasive validation data.
# Machine Learning Methods in Weather and Climate Applications: A Survey
## 7 Discussion  
Weather forecasting and climate prediction are closely related to people's lives and provide important information and support for social and economic activities. For example, governments and relief organizations rely on accurate weather forecasts to warn of and respond to natural disasters, thereby mitigating their impact on people's lives and property. At the same time, the energy industry also relies heavily on climate forecasts to predict energy demand and optimize energy distribution, thereby ensuring the stability and efficiency of energy supply. Our research purpose, the examination of machine learning in meteorological forecasting, is situated within a rich historical context, charting the evolution of weather prediction methodologies. Starting from simple statistical methods to complex deterministic modeling, the field has witnessed a paradigm shift with the advent of machine learning techniques.
# Recent Advancement of Data-Driven Models in Wireless Sensor Networks: A Survey
## 5 Analysis of Data-Driven Models for WSNs
### Query-Driven Model  
In WSNs, a query-driven model is used when the user needs data regarding their requirement. According to the user's need, the user sends the request to the sensor node in the interested region. Interested regions have various types of nature such as environmental monitoring, agriculture, healthcare, military, forest monitoring, etc. Hence, there is a huge problem regarding energy in WSNs due to the harsh environment. The energy problem in this model does not affect the whole network; it only affects the nodes of a specific region, which is consumed by data transmission. For reducing energy consumption, [PERSON] et al. (2018) propose a data protocol based on multi-tree and multiple-tree algorithms. In tree-based architecture, the parent node has more load as compared to the child node, as the child node forwards data as well. The parent node receives data from the child node as it might have redundant data, which can cause high transmission. Mostly, data load occurs due to congestion, packet loss, collusion, chances of data being delayed, network control overheads with quality of service (QoS), and loss of reliability. The query-driven model is usually used for fast communication; most of the applications include health monitoring applications, post-emergency management systems (rack any object), and fire temperature. [PERSON] et al. (2019) propose a QWRP: a query-driven virtual wheel-based routing protocol for location-based data collection. The data collection is by a mobile sink node that is placed in an interesting location-based region. To improve data delivery performance, QWRP is used. In the network, special nodes are known as wheel nodes, which have the information of mobile sink nodes. When the mobile sink wants to get the data from the interested region, wheel nodes send the query to the interest region of the mobile sink node
# Fair Task Allocation When Cost of Task Is Multidimensional
## 5 Related Work
The authors of [1; 23] researched on fair allocation of of indivisible tasks and the authors of [21] presented an improved approximation algorithm for MMS allocation of tasks. The authors of [18; 23] showed that fair allocations of goods and tasks have some fundamental connections but differences as well. The problem of combinations of goods and tasks has received attention as well [25]. Agents are said to have additive valuations when for any subset of goods \(S\subseteq M\), the valuation of agent \(i\) satisfies \(v_{i}(S):=\sum_{j\in S}v_{i}(j)\). Agents are said to have submodular valuations when for any \(A\subseteq B\subset M\) and \(g\in M\setminus B\), the valuation of agent i satisfies \(v_{i}(B\cup\{g\})-v_{i}(B)\leq v_{i}(A\cup\{g\})-v_{i}(A)\). The authors of [21] have shown that under additive valuations a \(\frac{1}{3}\)-approximate MMS allocation can be achieved and under submodular valuations a \(\frac{1}{10}\)-approximate MMS allocation can be guaranteed. The authors of [1] consider MMS fairness in 3 preference models elicited from the agents: the cardinal model in which agents are allowed to report their utilities over the items, the ordinal model in which agents are only allowed to express their ordinal rankings over the items, and the public ranking model in which the ordinal preferences are publicly known. The authors of [1] present an MMS approximation can be guaranteed while the algorithm is strategy proof in 3 preference models. However, in all the work above, utilities of goods or the cost of tasks limited in single dimension and the situation that cost comes from multiple dimensions has not been taken into account. There are some optimization algorithms that can be used to solve multidimensional cost problem through multi-objective optimization models. The authors of [26] used multi-objective Genetic Algorithms (GA) to solve multi-UAV mission planning. The authors of [27] proposed a methodology with heuristic based on Earliest Available Time algorithm to solve the UAV scheduling problem in an indoor environment incorporated Particle Swarm Optimization (PSO) algorithm, with an objective of minimizing the makespan. However, as the space for the solutions of our problem increases exponentially with the number of tasks and these optimizations usually requires thousands of iterations to converge to the optimal solution when dealing with NP-Hard problems. Therefore, this type of algorithm takes a long time and is not suitable for embedding into agents with low hardware facilities. In this work, we consider a model that has costs of tasks from multiple dimensions, and in each dimension, agents are only allowed to express their ordinal rankings over tasks. In addition, the cost of tasks under additive valuations. For this model, we proposed a task allocation algorithm that guarantees low time complexity while still achieving high fairness allocation.
## 6 Conclusions  
This paper addresses the challenges of packet reordering in the IP-ICN hybrid multipath transmission system by providing a best-effort reordering service through a dedicated module at the gateway. A Red-Black Tree structure is used in the receiver's buffer to reorder packets based on flow EID and sequence number. Disordered packets from flows with different EIDs are organized sequentially within their respective Red-Black Trees and are delivered when the smallest sequence number in each Red-Black Tree matches the expected sequence number. We propose a Reordering Buffer Management Method (RBMM) to enhance service quality. First, in order to optimize buffer resource utilization, the improved DT scheme allocates reserved buffer space dynamically in response to congestion. This allocation takes into account factors such as the number of flows in the buffer, the proportion of persistent high-resource-demanding (PHRD) flows, and the business category of the flows. Second, to ensure fair resource allocation, a flow-priority-based buffer replacement strategy is employed. This strategy evicts packets that are more difficult to deliver during congestion, prioritizing flows that can make better use of the available resources. Experimental results demonstrate that RBMM achieves better transmission performance with lower resource consumption compared to environments without buffer management. Moreover, compared to conventional ST, DT and DT+RED methods, RBMM significantly reduces queuing delay and flow completion time, demonstrating its ability to reduce resource overhead and improve user experience. Additionally, comparative experiments with the RCBRB method show that RBMM exhibits significant advantages in controlling network overhead and conserving buffer resources under large-scale data conditions.  
Future work will focus on two main aspects. First, we plan to deploy this method in real-world networks, with further adjustments and optimizations based on practical conditions. Additionally, we aim to explore opportunities for extending the functionality and enhancing the performance of gateway nodes. For example, integrating AI techniques at the ingress gateway to predict, identify, and classify traffic could improve management efficiency and strengthen system security. At the egress gateway, optimizing traffic shaping will be a key focus. Developing a well-designed rate-limiting mechanism is essential to meet the transmission needs of IP terminals while minimizing excessive pressure on the buffer.  
Conceptualization, Y.L. and R.H.; methodology, Y.L. and R.H.; software,Y.L.; writing--original draft preparation, Y.L.; writing--review and editing, R.H. and X.W.; supervision, R.H.; project administration, R.H.; funding acquisition, R.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China: Application Demonstration of Polymorphic Network Environment for computing from the eastern areas to the western. (Project No. 2023 YFB2906404).  
**Data Availability Statement:** All of the necessary data are included in the article.
# A Multi-Domain Model for Variable Gap Iron-Cored Wireless Power Transmission System
## 6 Conclusions  
WPT devices represent a promising solution for data and power transfer applications, both for devices that dispose of a discrete quantity of energy, but also for devices that are applied in behavior where the total amount of energy is strictly reduced. The proposed paper analyses a new type of device: a ferrite-cored, variable gap, high-frequency power and data transfer system.  
The standard circuit-based model proposed in the literature can be considered reliable for NFC application where the devices present two air core coils that communicate through light coupling. In the case of \(n\)-cored coils coupled through a fluctuating and uncontrollable air gap, the previously cited model can no longer be considered accurate. A complete multi-scale, multi-domain model is proposed herein, which is based on the integrated co-simulation of the Spice-based equivalent circuit modeling analysis and the ESRF Radia-based 3D finite volume methodology, placing particular emphasis on the intrinsic sensitivity with respect to unwanted mechanical and geometric variations.  
Furthermore, a detailed experimental methodology is outlined for both the estimation of the main electro-magneto-mechanical static and dynamic parameters, and for the validation of the proposed co-simulation architecture.  
The numerical simulation results and measures obtained from a real device are compared, showing that the co-simulation model correctly predicts the dynamic behavior in both the time and frequency domain analysis.  
In conclusion, this research work aims not only to introduce an improvement in the modeling of wireless communication applications, but also to provide a guideline for all those who are interested in the numerical characterization and experimental identification of the constitutive parameters useful for understanding the dynamics of a ferrite-cored variable gap WPT device.  
Conceptualization, F.M.R., C.R., S.C. and J.M.; methodology, F.M.R. and C.R.; software, F.M.R.; validation, M.C. and J.M.; formal analysis, F.M.R.; investigation, F.M.R. and C.R.; resources, M.C. and J.M.; data curation, F.M.R. and S.C.; writing--original draft preparation, F.M.R.; writing--review and editing, F.M.R. and S.C.; visualization, F.M.R.; supervision, C.R. and S.C.; project administration, C.R. and S.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are unavailable due to privacy.  
The authors declare no conflict of interest.
## 3 Numerical Examples  
In order to provide a numerical and graphical illustration of the analytical results reported in the preceding section, we present here a few examples of the behavior of the proposed model under various combinations of system parameters. For the sake of brevity, all results are expressed in terms of \(Z^{t}\) only, since the addition of \(F^{t}\) as a descriptor of the temporal evolution of the network state leads to qualitatively similar results. The cases of linear and fourth-power link cost functions are addressed separately in the next two subsections.
## 2 Materials and Methods
### Immunohistochemistry Analysis  
Immunoreactivities for the cell proliferation marker Bromodeoxyuridine (BrdU) (ab8152, Abcam, Cambridge, UK; dilution 1:100) were assessed using a purified primary antibody in conjunction with an avidin-biotin-peroxidase complex and a peroxidase substrate kit (Vector Labs, Burlingame, CA, USA) on femoral and tibial articular cartilage and synovial membrane tissues. Furthermore, immunoreactivity for the apoptotic marker cleaved Poly (ADP-ribose) polymerase (PARP) (9545, Cell Signaling Technology Inc., Danvers, MA, USA; dilution 1:100) and the proinflammatory cytokines Tumor Necrosis Factor-alpha (TNF-\(\alpha\)) (sc-52746, Santa Cruz Biotechnology, Santa Cruz, CA, USA; dilution 1:200) and Cyclooxygenase-2 (COX-2) (160126, Cayman; dilution 1:200) was evaluated. The procedure began with the quenching of endogenous peroxidase activity by incubating sections in methanol containing 0.3% H2O2 for 30 min. To prevent non-specific antibody binding, sections were treated with normal horse serum as a blocking solution for 1 h in a humidified chamber. Epitope retrieval was achieved through pretreatment with trypsin (Sigma-Aldrich, St. Louis, MO, USA) and 2N hydrochloric acid (HCI). Primary antibodies were applied overnight at 4 \({}^{\circ}\)C in a humidified chamber, followed by incubation with a biotinylated universal secondary antibody and avidin-biotin-peroxidase complex reagents for 1 h at room temperature. Development of the signal was conducted using the peroxidase substrate kit for 3 min at room temperature. Between each step, sections were washed thrice with 0.01 M phosphate-buffered saline. This analysis was performed blindly by a histopathological. Positive immunoreactivity, defined as over 20% staining for each antibody (BrdU, PARP, TNF-\(\alpha\), and COX-2), was quantified in cells/mm\({}^{2}\) in both femoral and tibial articular cartilage along with synovial membrane tissues.
## 3 Experimental Plan
### Asphal Mixture Performance Tests  
#### 3.4.1 Water Stability Performance  
The Marshall immersion or immersion compression test was conducted to estimate the water damage resistance of the bituminous mixes by computing the residual Marshall stability ratio (_MSR_) [47]. Two duplicate groups of specimens, each consisting of three specimens, were set up; the diameter and the height of each specimen were 101.6 mm and 63.5 \(\pm\) 1.3 mm, respectively. The first group was immersed in water at 60 \({}^{\circ}\)C for 30 min while the other was immersed in water at 60 \({}^{\circ}\)C for 48 h. Marshall stabilities were determined based on the standard requirements [48]. Thus, the _MSR_ was computed using the following equation [49]:  
\[MSR=\frac{MS_{2}}{MS_{1}}\times 100\% \tag{1}\]  
where _MSR_ is the ratio of residual stability, _MS_2 is the Marshall stability after 48 h of water immersion, and _MS_1 is the Marshall stability of the fresh mixture after 30 min of water immersion.  
The freeze-thaw splitting test was also applied to estimate the water stability performance of bituminous mixes by evaluating the freeze-thaw strength ratio (_TSR_) specified by Chinese standard JTG E20-2011 [48]. Two identical groups, each group with three identicalcylindrical samples were equipped. The diameter and the height of each sample were 101.6 mm and 63.5 mm, respectively. The first group was put under pressure (98.3-98.7 kPa) for 15 min, then cured in water at 25 \({}^{\circ}\)C for 20 min. After this, the specimens were wrapped with 10 mL of water in a plastic refill and refrigerated at \(-18\)\({}^{\circ}\)C for 16 h. They were then put in a water bath at 60 \({}^{\circ}\)C for 24 h. Next, this group of frozen-thawed samples and the second group of fresh samples were placed in a water bath at 25 \({}^{\circ}\)C for 2 h to reach a constant degree of temperature, and a split tensile strength test was conducted in the two groups with a loading rate of 50 mm/min, based on the requirement AASHTO T283 [50]. The \(TSR\) can be obtained as follows [49]:  
\[TSR=\frac{R_{T2}}{R_{T1}}\times 100 \tag{2}\]  
\[R_{T}=\frac{0.006287P_{T}}{h} \tag{3}\]  
where \(TSR\) is the tensile strength ratio (%), \(R_{T2}\) is the splitting strength of frozen-thawed samples (MPa), and \(R_{T1}\) is the splitting strength of fresh samples (MPa). \(P_{T}\) is the indirect tensile loading at failure (N); \(h\) is the sample height (mm).
# Multiscale Superpixelwise Locality Preserving Projection for Hyperspectral Image Classification
###### Abstract  
Manifold learning is a powerful dimensionality reduction tool for a hyperspectral image (HSI) classification to relieve the curse of dimensionality and to reveal the intrinsic low-dimensional manifold. However, a specific characteristic of HSIs, i.e., irregular spatial dependency, is not taken into consideration in the method design, which can yield many spatially homogenous subregions in an HSI scene. Conventional manifold learning methods, such as a locality preserving projection (LPP), pursue a unified projection on the entire HSI, while neglecting the local homogeneities on the HSI manifold caused by those spatially homogenous subregions. In this work, we propose a novel multiscale superpixelwise LPP (MSuperLPP) for HSI classification to overcome the challenge. First, we partition an HSI into homogeneous subregions with a multiscale superpixel segmentation. Then, on each scale, subregion specific LPPs and the associated preliminary classifications are performed. Finally, we aggregate the classification results from all scales using a decision fusion strategy to achieve the final result. Experimental results on three real hyperspectral data sets validate the effectiveness of our method.  
hyperspectral image manifold learning; dimensionality reduction; local homogeneity; irregular spatial dependency; multiscale superpixel segmentation; covariance feature; classification Article
## 4 Stability Analysis
### Simulation Results  
We present the simulation results of the proposed attitude and position following SMC control in Figures 3-8. Figures 3 and 4 show the attitude and position trajectories of the quadrotor with variable changes (weights increased to 125%). The simulation results in Figures 3 and 4 show that the proposed SMC can successfully drive the quadrotor from the initial position through the desired path to the final destination while maintaining the desired attitude. Figures 5 and 6 show the lift and steering torque produced by the four tilt-rotors of the TRQ. Figure 7 shows the path of the tilt angle with parameter deviation. The corresponding quadrotor speeds are shown in Figure 8.  
Because the stability analysis in the previous section demonstrated robustness with respect to parameter uncertainty and external perturbations, we then further evaluated the impact of external perturbations on the TRQ. We used perturbation force [\(\sin(4t)-\sin(4t)\ 2\sin(4t)\)] N and perturbation torque [0.1 sin(2t) 0.1 sin(2t) 0.1 sin(2t)] Nm applied to TRQ for simulated motion. As shown in Figures 9-14, we can see that the perturbation has little effect on the capability of the quadcopter because the proposed control and control assignment can reject the perturbation and return the state variables to the sliding surface.  
Figure 3: The attitude path with variable deviations.  
Figure 4: The position path with variable deviations.  
Figure 5: The propelling force with variable deviations.  
Figure 6: The turning torque with variable deviations.  
Figure 8: The rotor velocity with variable deviations.  
Figure 7: The tilt angle path with variable deviations.  
Figure 9: The attitude path with variable deviations and perturbations.  
Figure 11: The propelling force with variable deviations and perturbations.  
Figure 12: The turning torque with variable deviations and perturbations.  
Figure 10: The position path with variable deviations and perturbations.  
In summary, the numerical simulation results clearly show that the proposed SMC schemes can accomplish the goal of trajectory tracking and counter the parametric variation and external disturbances in the rotation and translation of TRQ via control allocation.