esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conventional Methods in ITS Cybersecurity  
Although ITS are relatively new, many of the technologies they integrate have been tested in practice and the experience gained can be reused. In terms of security, some of the classic approaches will certainly play a key role. The effective approaches of defending support layer are: strong authentication, encrypted communication, key management, regular auditing and private network and secure routing [8; 12; 20; 21].  
**Cryptographic methods** are the heart of cybersecurity. The application of cryptographic techniques in the automotive industry has a history since 90s. Traditional algorithm and encryption standards are not completely suitable for ITS as they cannot meet the requirements of high throughput performance, low latency and reliability. Lightweight encryption has become a basic requirement in ITS [8; 12].  
**Network segmentation** is another classic approach that improves both network security and efficiency. When talking about ITS network segmentation, it should be taken into account that some of the nodes are mobile, dynamically joining and with anonymity requirements. In VANET, the separation of clusters from communicating cars would play an important role, thus building hierarchy in the network. Depending on the goals and the situation, different metrics can be taken into account--the behavioral characteristic, based on historical data, the resources, the location and so forth. [21].  
In Reference [21] authors describe IoT security segmentation pattern. They take into account security level, attack surface, heterogeneity, identity, compliance, threats and overhead.
## 5 Experimental Results and Discussions
### Scalability to Terabyte-Scale Images  
The end-to-end algorithm operates exclusively on JPEG payload bytes, with all other data access performed sequentially. This design yields a computational complexity of \(T(N)=\alpha N+\beta\), where \(N\) represents the number of JPEG images, and we measured \(\alpha\approx 9.2\) us per JPEG on a single 3.6 GHz core. Based on our 1124-JPEG contained disk image, we project that a 1 TB evidence image (containing approximately 2,084,000 medium-sized JPEGs) can be processed in under 35 seconds on a single thread or in 1.1 seconds on a 32-core forensic workstation. This processing rate meets the operational needs in digital forensics, where timely evidence handling is essential according to established guidelines [75]. Memory usage remains bounded below <120 MB due to the algorithm's streaming architecture.
# A Resilient LoRa-Based Solution to Support Pervasive Sensing
## 6 Conclusions  
LoRa is a very powerful LPWAN that is becoming increasingly used. It offers a long transmission range and low energy consumption, making it perfect for sensor networks and the IoT world. While LoRa is generally used along with LoRaWAN, a robust protocol that can often enhance the physical layer, it requires connections to be only between a node and a gateway, heavily limiting the possible uses of the underlying technology. Mesh networks can provide a solution with a low installation time and help deliver messages, even if one of the middle-node suddenly becomes unavailable.  
Combining LoRa and mesh networks in this project, we created a solution for exchanging data and commands that can be used for many purposes, including pervasive sensing  
Figure 16: Positioning of nodes in the park test.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Measurement** & **Value** \\ \hline PDR & 100\% \\ RSSI (db) & [\(-\)109; \(-\)77] \\ SNR & [3; 7] \\ \hline \hline \end{tabular}
\end{table}
Table 10: Results obtained from the indoor mesh test.  
supporting smart cities. In our solution, nodes can be easily added to the mesh network and connected to a sensor, the data gathered can be delivered to the destination through a multi-hop routing protocol, and the spreading factor can be adapted to reduce data loss while maintaining the throughput between the nodes as high as possible.  
A future expansion of this work could focus on improving the routing algorithm by exploiting the RSSI to calculate the better path rather than choosing the one with the least number of hops. Some work should be put into developing an algorithm to normalize RSSI values obtained from different devices. The nodes currently do not use any form of encryption to secure messages, which may be helpful for certain applications. While small, battery-powered devices typically have limited computing capabilities, ways to secure the connection between nodes exist, like certificates. A future iteration could work on implementing this feature.  
Conceptualization, P.M., S.E.M., C.E.P. and P.P.; Methodology, P.M., S.E.M., C.E.P. and P.P.; Software, P.M., S.E.M., C.E.P. and P.P.; Validation, P.M., S.E.M., C.E.P. and P.P.; Formal analysis, P.M., S.E.M., C.E.P. and P.P.; Investigation, P.M., S.E.M., C.E.P. and P.P.; Writing--original draft, P.M., S.E.M., C.E.P. and P.P.; Writing--review & editing, P.M., S.E.M., C.E.P. and P.P.; Supervision, P.M. and C.E.P.; Funding acquisition, P.M. and C.E.P. All authors have read and agreed to the published version of the manuscript.  
The authors of this paper acknowledge the support of MUR (Italian Ministry of University and Research) under the PON ex DM 1061 and the PNRR CN-MOST initiatives.  
Data Availability Statement: Data presented in this paper are available upon request to the authors.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# A Message Passing Approach to Biomedical Relation Classification for Drug-Drug Interactions
## 2 Materials and Methods
### Negative Instance Filtering  
The extraction of DDIs from biomedical text is a multi-class classification problem, with _Advice, Effect, Int_ and _Mechanism_ being the positive classes and _Negative_ being the negative class. The dataset statistics in Table 1 show the highly imbalanced nature of the corpus, in terms of both the positive and negative class distribution and within the four positive classes as well. In particular, the instances of the negative class exceed the positive classes with a ratio of 1:5.9 in the training set. It can be observed that only a part of the pairs labeled as negative explicitly express the knowledge that there is no interaction between the substances (drugs). Conversely, in the vast majority, the negative instances follow the same pattern where a number of drug-pair interactions were labeled in the same sentence, without clarifying the relationship between them. Consequently, the following set of rules was defined to detect them, as such cases can be dismissed.  
Figure 3: The schema of subgraph G which models the classification of an Interaction node.  
Figure 2: The DDI Knowledge Graph overview based on an example sentence. (**Left**) drug sub-graph; (**Right**) sentence sub-graph.  
Consistent with the assumption that a substance does not interact with itself, pairs involving the same or synonymous drugs are rejected;
* There are sentences in the form of \"\(drug_{1}\) [-]...\(drug_{1}\)...\(drug_{2}\)...\" which provide conflicting knowledge about the type of interaction between two drug entities. Therefore, any pair involving the first occurrence of \(drug_{1}\) will be removed;
* Regular expressions identify patterns, such as quoting a list of drugs or referring to a sub-case, broader category or abbreviation. Additionally, consecutive or overlapping matches are merged to combine regular expressions. Finally, any pairs found within a match are discarded.  
The above rule set leads to the rejection of 44.5% and 0.94% of the negative and positive examples in the training set, respectively, with the ratio changing to 1:3.3. Finally, as the corresponding Interaction nodes are not taken into account, they are excluded in the results during the evaluation of our proposed system.
## 6 Conclusions  
In this study, a new progressive waveform decomposition method based on the Gaussian mixture model is proposed to effectively decompose various ABL waveforms and improve the bathymetry performance. Experiments using the Seahawk waveform dataset acquired from different environments were conducted. The results confirmed that the proposed approach achieved superior performance in measuring bottom points compared with Seahawk's data processing software by effectively detecting weak or shallow bottom returns. The proposed approach is universally applicable regardless of water depth, presence of unpredictable underwater objects, or irregular topography. In addition, it does not require a data-specific process according to the environment, thus simplifying the entire waveform processing. Furthermore, the proposed Gaussian decomposition technique can potentially be applied to various ABL bathymetry studies by enabling the extraction of diverse waveform features of the water surface, seafloor, and underwater points. Future works can include the investigation of the methods for overcoming noise misdetection and achieving the accurate automatic labeling of generated points.  
Figure 12: Comparison of waveform decomposition results in terms of termination criterion of iteration: (**a**) R\({}^{2}\); (**b**) R\({}^{2}\) and time difference between OP and EP.  
**Author Contributions:** Conceptualization, H.K., M.J. and J.L.; methodology, H.K.; validation, H.K. and J.L.; formal analysis, H.K.; investigation, H.K. and M.J.; resources, G.W.; data curation, H.K. and G.W.; writing--original draft preparation, H.K. and M.J.; writing--review and editing, all authors; visualization, H.K.; supervision, J.L.; project administration, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by Korea Institute of Marine Science & Technology (KIMST) funded by the Ministry of Oceans and Fisheries (RS-2023-00254717).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results
### Data Preprocessing  
Data preprocessing is used to identify excessive mistakes and cycle lapses in GNSS measurements. The Geometry-Free (GF) [44] and Melbourne-Wubbena (MW) [45] combinations are popular methods for detecting cycle slip, and they are frequently employed jointly. [PERSON] (1990) [46] proposed the TurboEdit approach, which detects gross errors and cycle slippage using dual-frequency GF and MW combinations. Its usefulness in GNSS-based positioning and orbit determination has been well tested ([PERSON] 2011) [41]. As a result, this work uses the TurboEdit approach to preprocess dual-frequency GNSS data and then finds gross mistakes using dual-frequency pseudorange observations. Figure 2 depicts the whole data preprocessing method.
# Secure Inter-Frame Space Communications for Wireless LANs
## 5 Conclusions  
In this paper, a secure inter-frame space communication system was designed and its simulation model was implemented. To design the secure inter-frame space communication algorithm, the characteristics of periodic interference signals were investigated. Although most periodic interference signals have similar properties such as pulse width, pulse period, and burst period, some military radios, such as radars, do not reveal their parameters. To implement the special-purpose periodic interference signals, all the parameters should be set as adjustable parameters. The periodic interference signal generators have been implemented in the WLAN C models and a pulse train is available to test the proposed algorithm. Since a periodic interference signal is generated independently of WLAN communication packets, the secure inter-frame space communication algorithms could be tested either alone or together with WLAN packets. One of major security requirements of wireless networks is availability issue. The availability refers to ensuring that the authorized users are indeed capable of accessing a wireless network when needed. The violation of availability, caused by interferences, will results in the authorized users to become unable to access the wireless network. The proposed secure inter-frame space communications scheme improves the availability under the interfered environments. As future work, the secure inter-frame space communication system will be implemented and demonstrated in a real testbed, and optimization issues for more than one radar will be investigated in the real testbed and the performance analyzed theoretically including mathematical models.  
This work was supported by the Sungshin University Research Grant of 2017-1-11-055/1.
###### Abstract
We see a strong dichotomy between two concepts and the question of attaining a unified vision system that can perform segmentation that is coherent to meet the needs of real-world applications is still an important concern. Panoptic segmentation (PS) is the first framework proposed in 2019 by [10] that unifies both semantic and instance segmentation. This unified segmentation has immense potential to open doors for researchers to come up with novel algorithmic solutions. When performing image segmentation, each pixel is provided with a semantic label along with an instance id. This unified approach, to combine scene-level and subject-level understanding, is driving the design and development of panoptic segmentation models. Industry leaders such as Apple, Facebook, Tesla, and Uber have been in the forefront to come up with pioneering vision systems that could provide a comprehensive view of the broader panoptic segmentation landscape. In a similar vein, this article aims to present an overview of various PS methods for scene classification and object detection and the potential challenges that revolve around it till date. The evaluation metrics applied in this context and some of the prominent PS models such as the Panoptic Feature Pyramid Network (FPN), Attention-guided unified network for PS, Seamless scene segmentation, Panoptic Deep lab, Unified PS network, and Efficient PS are presented. Finally, the paper outlines certain key future directions in deep learning-based segmentation research.
# Research on Network Attack Traffic Detection HybridAlgorithm Based on UMAP-RF
## 4 Experiment and Analysis  
### UNSW-NB15 Dataset and Experimental Environment  
The UNSW-NB15 dataset, created by the Australian Centre for Cyber Security (ACCS) Network Scope Lab, is the closest simulation of the current network traffic environment, covering one regular class of data and nine attack classes of data. Denial of Service (Dos), Exploits (Attack), Generic (General), Reconnaissance, Shellcode and Worms. As shown in Table 1, features 1 to 5 are stream features collected from the dataset, 6 to 18 are based features, 19 to 26 are content features, 27 to 35 are temporal features, 36 to 40 are generic features, 41 to 47 are general features and 48 to 49 are label features. In this paper, we use the training set of UNSW-NB15 to train the model and use its test set to evaluate the model performance.  
The network attack traffic detection model experiments and comparison experiments presented in this paper were conducted on a 64-bit Windows Intel(R) Core (TM) i7-11700K CPU (3.60 GHz) with 32 GB RAM and a Python-based Nvidia GeForce GTX 3070 GPU (8 GB), using Python's TensorFlow library to write the UMAP-RF model for this paper.
# Facial Expression Recognition Based on Discrete Separable Shearlet Transform and Feature Selection
## 3 The Proposed Algorithm
### Facial Expression Recognition and Classification Using Support Vector Machine  
In machine learning, SVM [22] uses a kernel function to map the data in an input space to a high-dimensional feature space in which we can process a problem in linear form. The steps of facial expression recognition based on SVM are as follows:  
1. Selection of the kernel function: in this method, we choose radial basis function (RBF) as the kernel function, as shown in (22). The reasons for choosing RBF are that RBF can realize nonlinear mapping and solve nonlinear separable problems, and, compared with other kernel functions, RBF has only one parameter \(\sigma\), so its model complexity is lower than that of others. \[K(x,x_{\text{c}})=\exp\left(-\frac{\lVert x-x_{\text{c}}\rVert^{2}}{\sigma^{ 2}}\right)\] (22)
2. Selection of RBF parameter \(\sigma\) and penalty coefficient \(E\): when using the RBF kernel function, the values of two parameters, \(\sigma\) and \(E\), are considered. However, there is no theoretical guidance on how to select the two parameters' values, therefore, we use [PERSON]'s tool grid.py in LibSVM to select the optimal values of \(\sigma\) and \(E\), \(E=128\) and \(\sigma=0.0078125\).
3. Construction of the multiclass SVM classifier: we adopt the one-against-one voting strategy of SVM. In the training stage, we use 6 categories of samples to construct \(6\times(6-1)/2=15\) SVM binary classifiers. We save the results of each SVM binary classifier into an array of structural cells, and hence save all the information needed for multiclass SVM classification into the array of structural cells. In the multiclass SVM classification stage, the training samples are successively passed through the 15 SVM binary classifiers, and the category of data is determined by one-against-one voting strategy.
## 3 Results
### Analysis of the VOCs Under Different Storage Temperatures by GC-IMS
#### 3.4.2 Analysis of Fingerprint Spectrum  
The fingerprints in Figure 3C were sorted from top to bottom as follows: 0 d, IT stored for 42 d, IT stored for 84 d, LT stored for 42 d, LT stored for 84 d, FT stored for 42 d, and FT stored for 84 d, with three determinations for each treatment. The detected substances were arranged from left to right for clearer presentation. Regions I, II, and III in Figure 3C include the substances that represent the flavor of fresh blue honeysuckle. All substances in region I (Figure 3C) were present at high levels in all samples, including ethyl butanoate, acetic acid ethyl ester, (E)-2-Hexen-1-ol, and ethyl trans-2-butenoate. The volatiles in regions II and III (Figure 3C) were substances that exhibited differences among the treatment groups. Region II (Figure 3C) contained 1-Hexanal, 1,2-Dimethoxyethane, and 1-Penten-3-one. Region III (Figure 3C) comprise methyl heptanoate, and 1-(2-Furanyl)-ethanoneone-M. Furans, including 2-Butylfuran in region II (Figure 3C) and 1-(2-Furanyl)-ethanone-M (D) in region III(Figure 3C), are important products of the Maillard reactions in foods and are of interest in many applications, such as coffee and baking flavorings [22; 41]. After 42 d of storage, IT demonstrated the best maintenance of the 10 volatiles in region II, while FT was not as effective as IT due to the reduction in volatiles, and LT was the least effective, having lost most of the volatiles. After 84 d of storage, IT still retained (E)-2-Pentenal-M and 3-Pentanone-D, whereas LT and FT lost almost all the volatiles in region II (Figure 3C). Unexpectedly, the FT treatment retained the volatiles in region III (Figure 3C) well. Volatiles in region IV were produced during storage. After 42 d of storage, IT had the least amount of newly produced volatiles, while both LT and FT produced volatiles not found in fresh fruit. After 84 days of storage, eight volatiles were elevated in IT compared to 0 d in region IV (Figure 3C), whereas almost all the volatiles detected in LT and FT were substantially elevated. The production of new odors did not necessarily mean that the flavor may be related to deterioration; on the contrary, the newly produced odors may also be pleasant, such as iso-Propyl acetate, 1-Butyl acetate, and 1-Hexanol, which contribute a fruit flavor, and 3-Hydroxy-2-butanone, which contributes a milk flavor in region IV. Most of the alcohols detected in the experiment were in region IV, which composed significant component of the flavor, giving the fruit an elegant aroma [42]. However, the generation of fewer new odors during storage indicated that the original flavor of the fresh fruit was better preserved. Combined with the results from the previous analyses, it can be concluded that IT has the best impact on maintaining the original VOCs in blue honeysuckles.
## 2 Materials and Methods
### Statistical Analysis
#### 2.4.2 Partial Least Squares Regression (PLS-R) Modeling
In order to avoid 'over-fitting' and find the optimal number of LV, we applied 10-fold cross validation over the calibration sets by minimizing the corresponding mean squared error over 15 LV. The validation sets were used to estimate generalizability and evaluate the predictive power of the final model. Model performance was assessed using the coefficient of determination (R\({}^{2}\)), the normalized root mean squared error (NRMSE) (Equation (4)), and the model bias (Equation (5)) of the calibrated and validated datasets, after pooling replicates and the 500 iterations together while separating between crops, dates, and soil water groups. \[NRMSE\ =\ \frac{\sqrt{\sum_{i\ =\ 1}^{S}(y_{i}-\ \overline{y}_{i})^{2}/S}}{y _{max}-\ y_{min}} \tag{4}\]  
\[bias\ =\ \frac{1}{S}\sum_{i\ =\ 1}^{S}\frac{(\overline{y}_{i}-\ y_{i})}{y_{i}} \tag{5}\]  
where \(y_{i}\) and \(\overline{y_{i}}\) are the known and predicted values, respectively. \(S\) is the total number of samples corresponding to calibration or validation sets. \(y_{max}\) and \(y_{min}\) represent the maximum and minimum known values. From PLS-R, we assessed the contribution of each predictor to the response using the variable importance of projection (VIP). VIP explains the contribution of individual wavelengths to the overall fitted PLS-R model across components. VIP scores lower than the unit indicate that the contribution of that variable can be neglected. VIP scores were calculated according to [PERSON] (2010) [85] as in Equation (6)  
\[ \begin{split} VIP_{j}&=\sqrt{\frac{\sum_{f\ =\ 1}^{F}w_{jf}^{2}\cdot SSY_{f^{ \prime}}\cdot J}{SSY_{total}\cdot F}}\end{split}  \tag{6}\]  
where \(J\) represents the number of variables, \(F\) is the total number of components, \(w_{jf}\) is the weight of component \(f\) and variable \(j\), \(SSY_{f}\) is the sum of squares of explained variance for the specific component \(f\) and \(SSY_{total}\) outlines the total sum of squares explained by the response. To identify wavelengths correlated to \(\mathrm{T_{L,Rad}}\) and \(\mathrm{h_{c}}\) in the latent variable space, we displayed the loadings of the first and second individual variables in that subspace. The goal was to detect which of the 138 VNIR bands provided similar information as \(\mathrm{T_{L,Rad}}\) and as \(\mathrm{h_{c}}\).