esa-sceva/satcom-chunk-collection huggingface dataset:
# Provably Secure ECC-Based Anonymous Authentication and Key Agreement for IoT
## 4 The Proposed Protocol
### Authentication and Key Agreement Phase  
\(S_{s}\) and \(SP_{sp}\) will authenticate each other and negotiate a session key as shown in Figure 3.  
S1: \(S\) first picks \(x_{s}\in Z_{q}^{*}\) randomly and generates a timestamp \(TS_{s}\). Next, S calculates \(A_{s}=(x_{s}k_{s}\bmod q)\cdot P\) and \(B_{s}=x_{s}\cdot WS_{sp}\). Third, \(S\) encrypts \(ID_{s}\), \(EID_{s}=ID_{s}\oplus B_{s}\), and obtains a verifier \(V_{s}=h(WS_{sp}||TS_{s}||ID_{s}||B_{s})\). Finally, \(S\) transmits the authentication request \(M_{s}=\{A_{s},EID_{s},TS_{s},V_{s}\}\) to \(SP\).  
SP1: Upon receiving the request message, \(SP\) first examines its freshness against the timestamp \(TS_{s}\). Next, SP calculates \(B_{sp}=k_{sp}\cdot A_{s}\) to decrypt \(ID_{s}=EID_{s}\oplus B_{sp}\). Thus, SP gains the S verifier and validates the equation of \(V_{s}=?h(WS_{s}||TS_{s}||ID_{s}||B_{sp})\) to assure the integrity of the incoming message and the validity of S.  
SP2: Firstly, \(SP\) selects \(x_{sp}\) randomly and obtains a timestamp \(TS_{sp}\). Secondly, SP calculates \(A_{sp}=(x_{sp}k_{s}\bmod q)\cdot P\) and \(C_{sp}=x_{sp}\cdot B_{sp}\). SP obtains the session key as \(SSK_{sp}=h(ID_{s}||ID_{sp}||BS_{sp}||CS_{sp})\). Third, SP figures out a verifier: \(V_{sp}=h(WS_{s}||TS_{sp}||ID_{sp}||SSK_{sp})\), and transmits authentication reply \(M_{sp}=\{A_{sp},TS_{sp},V_{sp}\}\) to S.  
S2: On receiving the message, [PERSON] first examines its freshness against \(TS_{sp}\). Next, [PERSON] calculates \(C_{s}=(x_{s}k_{s}\bmod q)\cdot A_{sp}\) to obtain the session key \(SSK_{s}=h(ID_{s}||ID_{sp}||B_{s}||C_{s})\). Thus, [PERSON] gains the SP verifier and validates the equation of \(V_{sp}=?h(WS_{sp}||TS_{sp}||ID_{sp}||SSK_{s})\) to assure the integrity of the incoming message and the validity of SP.  
Figure 2: Registration processes of the proposed scheme.
# Continuous Mobile User Authentication Using Combined Biometric Traits
## 6 Evaluation and Results
### Training with Position Data Only  
Our first approach was based on training a GMM HMM classifier with just _x_position_ and _y_position_ data. For the training process, different numbers of hidden states and components for the Gaussian mixture distribution were used. For different parameter combinations, the score is displayed in log scale because otherwise the number would be too small. After training, the best parameters are chosen for the final model selection.  
After this step, we can use the trained model to make predictions on the test sets captured from the other users. We calculate the score for each individual gesture using the _score_ method of the model and normalize it by the length of the input. When we have calculated all scores for the positive validation set and negative test sets, we can calculate the mean score for a dataset.  
Apart from the ROC curve and the resulting AUC value, we are relying on the calculation of the EER for measuring the performance of our classifier, since this metric is used in a lot of papers focusing on continuous user authentication, for example, [26] or [28]. As already mentioned, the EER is the value, where the FPR is equal to the FNR under a certain threshold.  
When examining the AUC of the trained classifier, we can see that it reaches a value of 0.66 which maps to a performance of 66%. Our AUC of 66% indicates that the classifier does indeed tend to predict in favor of the authenticated user, but the overall performance is much too low to make a usable continuous authentication system out of it. This may have several reasons which are discussed in more detail later. Similar to the performance of the AUC, the EER is rather high with a value of 35.99%. Therefore, the model trained with those parameters and features returns worse results when only predicting on single gestures, than the proposed systems presented in the related research.
# HDLNIDS: Hybrid Deep-Learning-Based Network Intrusion Detection System
## 6 Performance Analysis
### Results and Evaluation  
Experiments were conducted to evaluate the effectiveness of HDLNIDS in terms of accuracy, precision, recall, F1 measure, and the loss graph. We used 10-fold cross-validation to evaluate the ability of the model on new data as shown in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline
**Machine** & **Specification** \\ \hline Processor & Core i7 \\ Type & 64-Bit \\ RAM & 64-GB \\ Core & 64-core \\ Type & CPU \\ Graphic Card & NVIDIA GTX 1080 Ti \\ \hline \end{tabular}
\end{table}
Table 3: Hardware Specification.  
Figure 2 displays the results in terms of accuracy, which indicate that the proposed model's accuracy continues increasing with time. Initially, the accuracy of the model is observed as low, but it continues increasing over epochs. This is due to the learning ability of the proposed model that makes it stable and more competent to classify normal and malicious traffic.  
The results of the proposed model for the loss graph are shown in Figure 3. This indicates that the proposed model loss graph continues decreasing with time. Initially, the model's loss graph is observed to be high, but it steadily decreases over time. This is due to the proposed model's ability to learn, which makes it more stable and competent at classifying normal and malicious traffic.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Folds** & **Precision (\%)** & **Recall (\%)** & **F-Measure (\%)** & **Accuracy (\%)** \\ \hline Fold 1 & 98.33 & 98.91 & 98.81 & 98.95 \\ Fold 2 & 99.14 & 98.82 & 99.63 & 98.76 \\ Fold 3 & 99.45 & 99.55 & 98.64 & 98.56 \\ Fold 4 & 99.05 & 99.43 & 99.85 & 99.02 \\ Fold 5 & 98.85 & 99.16 & 99.12 & 99.23 \\ Fold 6 & 98.77 & 99.04 & 98.83 & 98.84 \\ Fold 7 & 98.83 & 98.92 & 98.87 & 98.67 \\ Fold 8 & 98.79 & 99.32 & 99.15 & 98.9 \\ Fold 9 & 98.82 & 99.18 & 98.71 & 99.18 \\ Fold 10 & 98.60 & 99.16 & 98.83 & 98.91 \\ Average & 98.63 & 99.14 & 99.03 & 98.90 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation and Characteristics of Architecture on each Fold.  
Figure 2: Accuracy of proposed deep learning model with respect to epochs.
# Small Infrared Target Detection via a Mexican-Hat Distribution
## 5 Conclusions  
In this paper, we have proposed a new method for detecting small IR targets. Our method is based on that the adjacent region of a small target in a DoG-filtered image roughly holds the Mexican-hat distribution. Our experimental results on both real-world and synthetic IR images show that our method is quite effective in enhancing small IR targets while suppressing background clutters. In terms of SCRG, BSF, \(P_{d}\), and \(P_{f}\), our method outperforms both the traditional and state-of-the-art methods. Moreover, it runs faster than the RLCM, the LIG, the Max-median, and the Max-mean.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Our Method** & **LoG** & **LIG** & **Max–Median** & **Max–Mean** & **Top-Hat** \\ \hline \(P_{d}\) & 0.6556 & **0.8030** & 0.7396 & 0.5729 & 0.6979 & 0.7369 \\ \(P_{f}\) & 0.3444 & **0.1969** & 0.2604 & 0.4271 & 0.3021 & 0.2604 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quantitative assessment of Seq.4.  
Figure 13: Detection results on the second image in Seq.4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Our Method** & **LoG** & **LIG** & **Max–Median** & **Max–Mean** & **Top-Hat** \\ \hline \(P_{d}\) & **0.9667** & 0.8000 & 0.8667 & 0.7111 & 0.7333 & 0.8444 \\ \(P_{f}\) & **0.0333** & 0.2000 & 0.1333 & 0.2889 & 0.2667 & 0.1556 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantitative assessment of Seq.3.  
Our experimental results show that our method performs rather well in single target detection and can also be directly used for detecting multi-targets in simple background. However, such direct use degrades the detection performance when the background is very complicated. Thus, our future work will focus on detecting multi-targets in complex background via such a method.  
Conceptualization, Y.Z. ([PERSON]) and L.Z.; Data curation, Y.Z. ([PERSON]) and L.Z.; Formal analysis, Y.Z. ([PERSON]) and L.Z.; Funding acquisition, L.Z.; Investigation, Y.Z. ([PERSON]); Methodology, Y.Z. ([PERSON]); Project administration, L.Z.; Resources, L.Z.; Software, Y.Z. ([PERSON]); Supervision, L.Z.; Visualization, Y.Z. ([PERSON]); Writing--original draft, Y.Z. ([PERSON]); Writing--review & editing, Y.Z. ([PERSON]), L.Z. and Y.Z. ([PERSON]).  
This work has been supported by the National Natural Science Foundation of China (No. 61771155).  
The authors declare no conflict of interest and there is no copyright issue in all of figures.
## 3 Hardware Development
### Wi-Fi  
Although nowadays, there are several wireless devices for sending data through low-power wide area networks (LPWAN), in Latin America, not all wireless technologies have coverage due to a lack of infrastructure, as is the case for Sigfox [29; 30], or wide-range communication, such as LoraWAN, which in urban areas has a maximum range of 500 m [31]. It is also worth noting that during conceptualization, the gas company proposed to use Wi-Fi communication for the reasons mentioned above, among others.  
Then, according to the gas company's requirements, an ESP-12E module [32] is selected for wireless data communication. It presents the following features: Wi-Fi 2.4 GHz, support WPA/WPA2, 802.11b protocol, Wi-Fi Direct (P2P), soft-AP, Integrated TCP/IP protocol stack, +19.5 dBm output power in 802.11b mode, and integrated low power 32-bit MCU.  
The ESP-12E module and its GPIOs operate at 3.3 V. This consumes an operating current of 80 mA, in light sleep mode 0.20 mA, and in deep sleep mode 10 \(\upmu\)A. Without data transmission, the Wi-Fi Modern circuit can be turned off and CPU is suspended to save power according to the 802.11 standard [33]. The schematic connection diagram of the Wi-Fi device is shown in Figure 6b [34].
## 4 Discussion
### Limitations and Future Directions  
One possible limitation of this study is that it only considered MEMS/IMU on mobile devices, and we did not address this technology present in wearable sensors such as smartwatches and commercial IMUs. In this sense, it is worth mentioning that the MEMS/IMU signal can suffer interference from several factors, as discussed earlier. With that in mind, when considering other technologies and comparing with mobile devices, the weight of the device and the sampling rate, as well as the device positioning, may differ from one device to another and so the signal may change. Therefore, carrying out a study comparing the differences between devices and verifying how this interferes with the signal would be interesting to enrich the discussions on the subject. In addition, based on the results found here, we believe that carrying out studies that compare signals using different filters and different cutoff frequencies can be the first step towards a standardization of the analysis of signals collected through sensors.
## 4 Machine Learning Algorithms
### Classification Results  
Figure 9 shows the confusion matrix of the SVM model with \"mixing\" validation dataset. The cumulative explained variance \(E\) was set at 99% with respect to 53 PCs. The results show a good classification with 96.33%, 99.29%, 98.57%, 94.43%, and 100% for no corrosion, backward, forward, left, and right corrosion. The lowest accuracy is for the left corrosion (94.43%), but the wrong prediction is mostly backward corrosion, which is also corrosion near a location. The average accuracy of the model is 97.17%. Figure 10 shows the probability of the prediction for each type of corrosion of the validation dataset. The probability increases as the corrosion volume increase. The average probability is over 90%, with the volume over 25 mm\({}^{3}\).  
Figure 10: Probability of classification by SVM model with “mixing” dataset.  
Figure 9: Confusion matrix of the SVM model for five classes with “mixing” dataset: no corrosion (N), backward (B), forward (F), left (L), and right (R).  
The SVM model provides good classification accuracy with a large training dataset ratio (80% of the entire dataset). Thus, we will evaluate the model with a smaller training dataset. Datasets \"increasing\" and \"shifting\" are used. The \"increasing\" dataset provides the increasing number of samples in the training dataset from 10% to 80% of the entire dataset, and the \"shifting\" dataset provides a fixed ratio of 20% of the entire dataset but with a different range of volume from small to large, as described in Table 2. We performed several other machine-learning models, which are Naive Bayes (NB), K-nearest Neighbor (KNN), Random Forest (RF), and Logistic Regression (LG) classifiers. Figure 11 shows the classification accuracy of the five machine-learning models with the two training datasets. With the increasing number of training datasets from 10% to 40%, the model accuracy quickly increased from 46% to 95%, as shown in Figure 11a. Then, the accuracy slowly increases as the training dataset is larger. The maximum accuracy was about 99%, with a 60-80% ratio of the training dataset using the SVM method. The LG method provides slightly smaller accuracy than the SVM method. The KNN and RF methods provide similar accuracy. The NB method provides the worst performance. While keeping the same ratio of 20% and shifting the volume ranges from small to large in the \"shifting\" dataset, the accuracy of the machine-learning model increased, peaked at range P\({}_{2}\) (volume in from 15.0 to 25.4 mm\({}^{3}\)), and decreased as the volume range increased.
# Algorithmic Techniques for GPU Scheduling: A Comprehensive Survey
## 1 Introduction  
Over the past decade, graphics processing units (GPUs) have matured from niche graphics accelerators into general-purpose, high-throughput computing engines, fundamentally altering how we approach data- and compute-intensive tasks. As their use hasproliferated beyond rendering--spanning scientific simulation, deep learning, and real-time analytics--traditional CPU-focused schedulers have struggled to balance the massive parallelism and unique memory hierarchies of modern GPU workloads.  
This survey brings together findings from across the GPU-scheduling literature to map out both established methods and the latest innovations. We review a spectrum of algorithmic strategies starting with classic formulations such as bin-packing and priority-queue approaches, and moving toward contemporary, data-driven schemes that employ machine learning for adaptive decision making. For each class of algorithms, we discuss its impact on key performance metrics (e.g., throughput, latency, and utilization) and identify the workload or cluster environments in which it excels. Our goal is twofold: to provide researchers with a clear picture of the current state-of-the-art and emerging directions in GPU scheduling, and to provide practitioners with practical guidance for tailoring schedulers to their specific data center architectures and workload profiles.
## Abbreviations  
The following abbreviations are used in this manuscript:  
4G Fourth generation  
5G Fifth generation  
5G NR & 5G New Radio  
5G-RANGE Remote Area Access Network for the Fifth Generation  
ABNT Associacao Brasileira de Normas Tecnicas  
ACLR Adjacent Channel Leakage-Power Ratio  
ACPEs Adjacent Channel Power Emissions  
ACPR Adjacent Channel Power Ratio  
Anatel Agencia Nacional de Telecomunicacoes  
ANE Agencia Nacional del Espectro  
BER Bit Error Rate  
BS Base Station  
BTS Broadcast Transport Stream  
BW Bandwidth  
DPD Digital Pre-Distortion  
DTV Digital Television  
DVRG Digital Video Recorder Generator  
EIRP Effective Isotropic Radiated Power  
ETSI European Telecommunications Standards Institute  
FCC Federal Communications Commission  
FEC Forward Error Correction  
F-OFDM Filtered Orthogonal Frequency Division Multiplexing  
GFDM Generalized Frequency Division Multiplexing
# Engineering Supply Chain Transportation Indexes through Big Data Analytics and Deep Learning
## 1 Introduction
### Website Big Data Analytics and Deep Learning  
The popularity of social media generates a tsunami of digital data, which is perceived as a challenging process to analyze and manage [20]. Even though big data offers an ocean of opportunities due to its transformational impact on various sectors, it further brings challenges on how to harness the available data, especially regarding data mining and information processing [21]. To mitigate the uncertainties generated by big data analytics, artificial intelligence techniques, such as evolutionary algorithms (EAs) and artificial neural networks (ANNs), have emerged in an attempt to provide accurate, factual, and scalable results [22].  
[PERSON] & [PERSON] [23], acknowledge the potential of leveraging deep learning techniques, such as variational autoencoders (VAEs), for hedging against uncertainty in data-driven optimization. Deep learning plays a key role in providing big data predictive analytics solutions, as shown with Restricted Boltzmann Machines (RBMs) exploitation [24]. Deep learning is used to analyze both structured and unstructured data by identifying new patterns and developing knowledge [25]. Especially for the supply chain industry, as data continues to grow exponentially, it will continue to be exploited to unearth new patterns, understand potential causes, and interpret the available data.  
Organizations within the supply chain industry have to invest in innovative approaches that will prevent operational inefficiencies, poor customer satisfaction, and revenue loss. Technology is one of the key trends that impacts the sustainability of the supply chain. As such, artificial intelligence and big data have the potential to become a powerful analytics pairing [26], a tool for companies that aspire to obtain the most out of their data analytics.  
In summary, big data analytics provide the necessary infrastructure and scalability, while deep learning leverages the power of big data to train models and perform advanced analytics tasks. Therefore, overcoming supply chain disruptions and ensuring the sustainability of the sector lie in the proper management of data growth into organized intelligence [27; 28]. Until now, even if big data offers great potential for revolutionizing all aspects of supply chain firms and immense value to different industries, there is still uncertainty over its use [29]. To maximize resilience, secure agility, and achieve the optimization of the supply chain transportation industry, companies need to invest in emerging technologies to develop innovative, data-driven applications based on both big data and deep learning techniques.
# Modeling and Simulation of Sky Survey
## 3 Proposed Methods
### Objective Function  
In this paper, we consider the objective function from two main aspects. The first one is to introduce the fitness function for evaluating the importance and observation value of the observed sky area, which is described in detail in [PERSON] et al. (2018). The problem of this method is that the observation value needs to be set in advance for the sky area, and the setting of the observation area has different opinions from different scientists, making the setting of the observation value often biased. Another way is to target the observation time and the number of altitude adjustments (energy consumption) for the sky survey. This approach minimizes the survey observation time and energy output from the actual situation of the satellite and builds a multi-objective optimization model. The problem is that the altitude adjustment of the satellite does not need to be minimized, but only the energy requirement of the observation needs to be satisfied. There is a detailed description in [PERSON] et al. (2018). Combining the objective functions of the two papers, this paper takes maximizing the observation benefit and minimizing the angle of CMG rotations as the objective functions.  
Observation Benefit: Maximizing the observational benefit means that high-value sky areas are given the highest possible priority for observation and setting the observational priority of sky areas.  
\[O_{1}= Max\sum_{i=1}^{N}\sum_{j=1}^{M}v_{j}\cdot x_{ijk},\hskip 28.452756 pt \forall t_{i}\in\mathbf{T},\forall\tau_{k}\in\mathbf{R}. \tag{3}\]  
CMG Rotations: Since the CMG rotation process not only needs to consume energy but also needs to consume the observation time, in order to complete the target cruise faster and better, the objective function is set to the angle and minimum of CMG rotations.  
\[O_{2}=\min\sum_{i=2}^{N}\sum_{j=1}^{M}\sum_{h\in\mathcal{S}kh\
eq j}^{M}\theta \left(s_{j}x_{ijk},s_{h}x_{(i-1)hk}\right),\hskip 28.452756 pt\forall t_{i} \in\mathbf{T},\forall\tau_{k}\in\mathbf{R}. \tag{4}\]  
where \(\theta(S,O)\) is the formula for calculating the rotation angle between the position coordinates of the satellite at any moment \(S(Lat_{1},\,Lon_{1})\) and the position coordinates of the observation point \(O(Lat_{2},\,Lon_{2})\) according to ([PERSON] et al., 2018; [PERSON] et al., 2018).  
\[f(S,O)=\arcsin\left(\sqrt{\sin\left(\frac{Lat_{2}-Lat_{1}}{2}\right)^{2}+\cos (Lat_{1})\cdot\cos(Lat_{2})\cdot\sin\left(\frac{\text{Lon}_{2}-\text{Lon}_{1} }{2}\right)^{2}}\right), \tag{5}\]  
\[\theta(S,O)=2\cdot r\cdot f(S,O)\times 180^{\circ}/\pi, \tag{6}\]where \(f(S,O)\) stands for the function of calculating the angle between \(S\) and \(O\); \(r\) in the formula represents the unit radius, as \(r=1\); and the unit of position coordinates \((Lat_{1},Lon_{1})\) and \((Lat_{2},Lon_{2})\) is the unit radius.
## 3 Results and Discussion
### Scanning Speed  
The SEY curves of the laser engineered Mo samples #1 (light blue curve), #2 (orange curve), #3 (crimson curve), #5 (purple curve), #6 (dark blue curve) and #7 (fluorescent green curve) are shown in Figure 2. Samples #1 and #2 were processed by laser with the same laser parameters except for laser scanning speed as the cross hatched pattern.  
The \(\delta_{\rm max}\) of sample #1 (scanning speed of 100 mm s\({}^{-1}\)) was higher than that of sample #2 (scanning speed of 1000 mm s\({}^{-1}\)) at \(E_{\rm P}\leq 1400\) eV and lower than that of sample #2 at 1400 eV \(\leq E_{\rm P}\leq 3000\) eV. However, the \(\delta_{\rm max}\) of sample #5 (scanning speed of 1000 mm s\({}^{-1}\)) was above that of sample #3 (scanning speed of 100 mm s\({}^{-1}\)) at \(100\leq E_{\rm P}\leq 3000\) eV for the line hatched pattern. The situation was similar for samples #6 and #7. It was concluded that low scanning speed contributed to low SEY surface with the line hatched pattern. It was estimated that the SEY curves flattened off for samples #2, #3 and #6 and decreased gradually for samples #1, #5 and #7 at \(E_{\mathrm{P}}\geq 3000\) eV.  
The surface topography images of samples #1, #2, #3, #5, #6 and #7 are shown in Figure 3. It can be seen that the surface topographies of laser processed Mo samples #1, #3 and #6 were like the flower of allium giganteum regel with the view field of \(5.54\pm 0.01\) nm. The aggregates composed of micro- and nano-particles contributed to the multimodal roughness of the laser processing Mo surface. The Mo metal underwent various stages of melting and solidifying with oxide layers formation with nanoparticles covering it. The oxide states of laser processed Mo samples was analyzed in the following section.