esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine-Learning-Based Darknet Traffic Detection System for IoT Applications
## 2 Related Work
### VPN Traffic Classification  
Virtual private networks (VPNs) [32] are a type of technology that allows communicating securely over an unsafe network. VPN solutions are divided into multiple varieties, each with its own approach to security, benefits, drawbacks, and reliance on various protocols and standards. IPsec, PPTP, and TLS are the three main varieties. According to [33], the purpose of VPN traffic classification is to overcome the constraints and problems posed by their structure and the hostile environment in which they operate. This will enable the development of a better routing solution, one that avoids the flaws in standard routing protocols that control VPN operations. The authors of the work presented in [33] proposed a MATLAB implementation that uses artificial neural networks and time-related features on Apache Spark [34] to classify VPN network data flow. The results obtained in this work were as follows: the VPN identification accuracy was about 96.7%, and the non-VPN accuracy was about 92.5%. In [35], the dataset ISCXVPN2016 [36], as well as six machine learning models, were used to find the best supervised model for distinguishing VPN from non-VPN traffic. All other supervised models were shown to be inferior to gradient boosting tree (GBT) and random forest (RF). The authors of [36] used the same database to train multi-class classifiers that accurately classified VPN traffic into seven different categories using k-nearest neighbor (kNN) and the c4.5 decision tree technique. The classification accuracy obtained was around 80%. In order to develop a model capable of discriminating between VPN and non-VPN traffic in real time, the authors of [37] used machine-learning-based techniques on a multi-layer perceptron neural network model. To create a representative dataset of VPN and non-VPN values, real network data were collected using a variety of technologies. The packet capture for this dataset was performed with Wireshark [38]. The observed results suggest that the neural network's overall detection accuracy in the post-training test was around 94%.
## 3 Results
### Temporal Trends  
All models show a positive LAI trend in most of the Northern Hemisphere, which is consistent with the satellite observations (Figure 8). Nevertheless, there is little agreement on the spatial distribution of this phenomenon, with spatial correlation values between \(-0.05\) and \(0.12\). In the satellite observation most of the greening occurs over 55 -90 N in Eurasia, while in models it is homogeneously distributed. More puzzling is the reduction of LAI in LPJ-GUESS, OCN and VEGAS, which could be explained by a decrease in precipitation over this region (not shown). As all models are forced using the same climate, consistent regional patterns must be driven by temperature or precipitation. The greening over the high latitudes occurs in all models and is driven by an increased temperature.  
The models also show a general increase in the GSL albeit with a few areas where it decreases (Figure 9). However, similar to previous trends, there is little agreement over the spatial distribution. Clearly, models that perform better at calculating the GSL (both on the onset and dormancy) and average LAI more accurately reproduce observed linear trends (Figure 9). In most models, changes in the GSL match those of LAI. This is the case for CLM, OCN, ORCHIDEE, SDGVM and the satellite data, all of which use prescribed vegetation. LPJ, GUESS and VEGAS show an increased length over Eurasia and a decrease over North America, and their patterns resemble the precipitation trends for this  
Figure 8: LAI linear trends over the period 1986-2005 for 8 LSMs and the satellite observations. Spatial correlations between each model and the observations are given in the white boxes.  
period (not shown). This discrepancy between LAI and GSL changes is difficult to explain but could be driven by vegetation shifts from deciduous to evergreen forests. Changes in the GSL in TRIFFID, while only occurring over a small area, match the observations.
## 3 Results
### Application of Intelligent Discrimination Models
#### 3.4.2 Model Testing  
To avoid the randomness of test results, the support vector machine (SVM), random forest (RF), and gradient boosting decision tree (GBDT) models were tested on the test set for 10 runs each. The average Jaccard index and F1 score of the models were obtained (Figure 9). It can be observed that different methods of handling the datasets have varying effects on different models, showing a general trend that the more balanced the sample distribution of the dataset, the higher the model's lithology recognition scores.  
Figure 9: Model testing scores.  
Specifically, for the SVM model, the improvement in the balanced datasets is the most significant. However, the highest scores of both evaluation metrics for SVM were lower than those of RF and GBDT, indicating that the overall lithology recognition performance of the other two models is superior. RF is the highest-scoring model, and it performs better on more balanced datasets as well. The balanced datasets further unleash the potential of the RF model. In contrast, the performance of the GBDT model is not as outstanding as RF, but the performance of the GBDT model also improves on balanced datasets.
# Context-Based Energy Disaggregation in Smart Homes
## 5 Experimental Results
### Usage Statistic Conditioning  
In Figure 6, an aggregate power consumption trace is shown; as it can be noticed in this temporal portion, a PC-Desktop is always ON just like the Refrigerator, an LCD-TV is turned ON a little after  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Mean** & **Variance** \\ \hline \multicolumn{3}{c}{_Refrigerator_} \\ \hline \multirow{3}{*}{k-means} & 66.09 & 7.42 \\  & 302.48 & 56597.83 \\  & 61.09 & 37.66 \\ \hline \multirow{3}{*}{GMM} & 64.13 & 8.46 \\  & 491.01 & 24501.37 \\  & 30.56 & 921.85 \\ \hline \multicolumn{3}{c}{_Washing Machine_} \\ \hline \multirow{3}{*}{k-means} & 6.72 & 205.39 \\  & 2100.47 & 9339.21 \\  & 167.65 & 6804.05 \\ \hline \multirow{3}{*}{GMM} & 2.00 & 0.001 \\  & 2100.47 & 9339.21 \\ \cline{1-1}  & 105.28 & 2349.91 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative results of \(k\)-means and Gaussian Mixture Model (GMM) clustering algorithm for extracting the power levels (mean and variance) of a refrigerator and a washing machine.  
8:00 am and left ON until the end of the examined temporal portion. Moreover, a Coffee Maker is used in the other two daily moments.  
Figure 7 graphically shows the results obtained by applying the disaggregation algorithm by [PERSON] [9].  
Figure 8 shows the graphical disaggregation results obtained by applying our NILM algorithm with _Usage Statistics Conditioning_ (_USC_).  
Figure 6: Aggregate power consumption trace—test case 1.  
Figure 7: Power consumption trace disaggregated with the basic [PERSON] [9]’s Additive Factorial Approximate MAP (AFAMAP) algorithm - test case 1.  
An improvement can be observed; this is plausible, especially for devices that are typically switched ON and OFF in a portion of a specific time such as the coffee maker. As expected, a typical \"Always ON\" device such as the Refrigerator does not benefit from the effects of this type of conditioning.  
Table 6 shows the precision obtained with the timing usage statistics conditioning compared with those obtained by using the AFAMAP algorithm [9].
## 4 Conclusions
** & **F1** \\ \hline bamital & 100.00\% & 100.00\% & 100.00\% & padcrypt & 97.23\% & 98.79\% & 98.01\% \\ banjori & 99.55\% & 99.91\% & 99.73\% & phorpiex & 85.19\% & 90.93\% & 87.96\% \\ bazzloader & 99.86\% & 100.00\% & 99.93\% & pitou & 92.14\% & 98.37\% & 95.15\% \\ chinad & 97.74\% & 98.96\% & 98.35\% & proslikefan & 48.65\% & 26.05\% & 33.93\% \\ conficker & 47.18\% & 37.17\% & 41.59\% & pseudoman & 53.31\% & 96.60\% & 68.70\% \\ corebot & 99.66\% & 99.83\% & 99.75\% & pushdo & 94.22\% & 94.49\% & 94.35\% \\ cryptblocker & 42.32\% & 61.33\% & 50.08\% & pushdotid & 90.11\% & 97.59\% & 93.70\% \\ darkwatchman & 99.52\% & 99.87\% & 99.70\% & pykspa2s & 97.86\% & 97.58\% & 97.72\% \\ dircrypt & 5.25\% & 41.74\% & 9.32\% & qadars & 94.50\% & 98.84\% & 96.62\% \\ dyre & 99.91\% & 100.00\% & 99.96\% & qakbot & 77.69\% & 63.97\% & 70.16\% \\ febury & 90.80\% & 99.83\% & 95.10\% & qsnatch & 99.62\% & 96.60\% & 98.09\% \\ emotet & 71.77\% & 100.00\% & 83.57\% & ramdo & 98.03\% & 99.94\% & 99.98\% \\ flubot & 89.73\% & 94.14\% & 91.89\% & ramnit & 53.96\% & 49.16\% & 51.45\% \\ fobber & 32.91\% & 40.81\% & 36.44\% & ranbyus & 77.39\% & 89.90\% & 83.18\% \\ gameover\_p2p & 99.98\% & 99.91\% & 99.94\% & rovnix & 95.13\% & 98.61\% & 96.84\% \\ gameover\_p2p & 73.18\% & 58.53\% & 65.04\% & shifu & 72.61\% & 96.35\% & 82.81\% \\ gozi & 76.08\% & 81.98\% & 78.92\% & simda & 98.65\% & 99.66\% & 99.15\% \\ granderoire & 92.98\% & 100.00\% & 96.36\% & sphinx & 32.51\% & 80.82\% & 46.37\% \\ locky & 64.63\% & 47.69\% & 54.88\% & suppobox & 94.75\% & 99.12\% & 96.89\% \\ m0 ytdd & 43.70\% & 98.45\% & 60.53\% & symmi & 98.77\% & 99.00\% & 98.99\% \\ metastealer & 99.99\% & 100.00\% & 100.00\% & tinba & 95.03\% & 96.67\% & 95.84\% \\ monerominer & 99.89\% & 100.00\% & 99.94\% & tinylfluff & 99.66\% & 99.78\% & 99.72\% \\ murofet & 80.27\% & 47.94\% & 60.03\% & tinynuke & 99.30\% & 100.00\% & 99.65\% \\ murofetweeky & 99.55\% & 99.55\% & tufik & 11.27\% & 44.85\% & 18.02\% \\ mydoom & 63.41\% & 99.45\% & 77.45\% & urlzone & 99.54\% & 90.51\% & 94.81\% \\ necro & 99.64\% & 98.02\% & 98.82\% & vavtrak & 70.56\% & 77.63\% & 73.92\% \\ necurs & 97.22\% & 82.26\% & 89.12\% & virut & 99.70\% & 99.96\% & 99.83\% \\ ngioweb & 92.73\% & 90.07\% & 91.38\% & xdhex & 98.90\% & 100.00\% & 99.45\% \\ nymaim & 42.37\% & 23.66\% & 30.36\% & zloader & 92.12\% & 100.00\% & 9
## 4 Approach
### Problem Definition  
**Definition 1**.: _Knowledge Graph._  
A knowledge graph is defined as \(\mathcal{G}=(\mathcal{E},\mathcal{R},\mathcal{T})\), where \(\mathcal{E}=\{e_{1},e_{2},\ldots,e_{n}\}\) is the set of entities, \(\mathcal{R}=\{r_{1},r_{2},\ldots,r_{n}\}\) is the set of relations, and \(\mathcal{T}\subseteq\mathcal{E}\times\mathcal{R}\times\mathcal{E}\) is the set of labeled edges. Each triple \((e_{h},r,e_{t})\in T\) represents a directed relationship \(r\) from head entity \(e_{h}\) to tail entity \(e_{t}\), inducing a heterogeneous graph structure where entities are interconnected through relational paths and the graph topology emerges through the closure of triples under path composition. The graph structure enables two fundamental operations: forward and backward traversal. In the former, given \(e_{h}\in\mathcal{E}\) and \(r\in\mathcal{R}\)\(\{e_{t}|(e_{h},r,e_{t})\in\mathcal{T}\}\) is returned; in the latter, given \(e_{t}\in\mathcal{E}\) and \(r\in\mathcal{R}\), \(\{e_{h}|(e_{h},r,e_{t})\in\mathcal{T}\}\) is returned.  
**Definition 2**.: _Natural Language Question._  
A natural language question \(\mathcal{Q}\) is a semantic query expressed through a sequence of tokens \(\{q_{1},q_{2}\ldots q_{n}\}\), which implicitly or explicitly contain the following: a topic entity and a set of reasoning objectives. The topic entity is defined as \(e_{topic}\in\mathcal{E}\), which explicitly refers to an entity in the knowledge graph. A set of reasoning objectives can be represented as a multi-step reasoning intention, where each step of the intention implicitly corresponds to a relation in the knowledge graph. The goal of the multi-hop knowledge question answering is as follows: First, identify the topic entity in the natural language question. Subsequently, starting from the topic entity, traverse and retrieve from the graph by filtering relationships that match the query intention according to the natural language. Finally, find the answer entities.  
**Definition 3**.: _Reasoning Plan._  
Natural language questions often do not explicitly show the relationships required during traversal and retrieval. Instead, these relationships need to be parsed by understanding the semantic meaning of the context. Additionally, multiple retrieval relationships also have a strict sequential order. Before retrieving the answer, generating a concise and clear reasoning plan can therefore guide the model to perform retrieval accurately and in sequence. The reasoning plan is defined as \(\mathcal{P}=\{p_{1},p_{2},\ldots,p_{n}\}\). Each \(\mathcal{P}\) corresponds to a unique retrieval relation \(r\) in the knowledge graph.
## 1 Introduction
[PERSON] et al. [35] examined the shear behavior of calcareous sand using drained and undrained triaxial tests, revealing the effect of particle breakage on specimen response. [PERSON] et al. [36] performed a series of bender element and cyclic triaxial tests to study the low-strain and high-strain characteristics of calcareous sand and terrigenous sand. Although numerous researchers have studied the dynamic shear modulus and damping ratio at low strains on natural and cement-stabilized soil [30; 34; 36; 37; 38; 39; 40; 41], considerably less information is known about the dynamic response of marine soil, such as calcareous sand at different MICP-treatment levels. Understanding how the shear modulus and damping ratio of MICP-treated calcareous sand varies with shear strain and other factors for engineering applications is necessary. Many studies have shown that the dynamic properties of cemented soils are affected by the cementation level [20; 42] and confining stress [43; 44; 45; 46; 47]. Accordingly, the main objective of this investigation is to evaluate the dynamic behavior of MICP-treatment calcareous sands at a low strain amplitude, focusing on the effect of confining stress and treatment time on the precipitated calcite contents. A series of resonant column tests are conducted to determine calcareous sand's dynamic shear modulus and damping ratio at a low MICP-treated level. Initially, this paper describes the test materials and implementation methods. The effect of MICP treatment on the dynamic response of saturated calcareous sand at a low shear strain, including the responses of the dynamic shear modulus, degradation characteristics of a normalized dynamic shear modulus, and the damping ratio, is then discussed. In addition, the correlations of the initial dynamic shear modulus with an unconfined compression strength, confining stress, and treatment time are addressed. Finally, the relationship between the reference shear strain and the treatment duration or confining stress are proposed. The study's results provide a quantification of dynamic parameters of MICP-treated calcareous sand and relevant support for the development of constitutive models.
## 5 Discussion
As the detection distance increases, the energy received by the radar system will inevitably be attenuated. As shown in Figures 15 and 19, when multiple targets are distributed at different distances in the imaging scene, the relatively weak energy of distant targets may be concealed in clutter. Therefore, we still need to study an effective way to solve this problem, such as adding amplifiers or shielding devices to the radar system to improve energy in the next step. In future research, the application of MIMO through-wall radar to extract vital signs (such as breathing, heartbeat, arm swing) or human micro-Doppler [39; 40] will be explored. In addition,compressive sensing technology can also be applied to through-wall detection of sparse arrays to reduce the sampling time and improve the resolution [41]. Moreover, it is worth mentioning that the existence of multiple walls, the detection of moving targets, real-time positioning imaging, and even three-dimensional imaging will also be challenging research tasks in our future work.
###### Abstract  
The relevance of effective and efficient solutions for vehicle traffic surveillance is widely recognized in order to enable advanced strategies for traffic management, e.g., based on dynamically adaptive and decentralized traffic light management. However, most related solutions in the literature, based on the powerful enabler of cooperative vehicular communications, assume the complete penetration rate of connectivity/communication technologies (and willingness to participate in the collaborative surveillance service) over the targeted vehicle population, thus making them not applicable nowadays. The paper originally proposes an innovative solution for cooperative traffic surveillance based on vehicular communications capable of: (i) working with low penetration rates of the proposed technology and (ii) of collecting a large set of monitoring data about vehicle mobility in targeted areas of interest. The paper presents insights and lessons learnt from the design and implementation work of the proposed solution. Moreover, it reports extensive performance evaluation results collected on realistic simulation scenarios based on the usage of iTETRIS with real traces of vehicular traffic of the city of Bologna. The reported results show the capability of our proposal to consistently estimate the real vehicular traffic even with low penetration rates of our solution (only 10%).  
Keywords:vehicular traffic estimation and management; vehicular communications; low penetration rate; cooperative vehicles; iTETRIS; performance evaluation +
Footnote †: journal: Sensors
## 4 Experimental Design of the Control System
### Hardware Scheme of the Control System  
In this Section, some important parts will be introduced to prove the authenticity and repeatability of the experiment in this work.  
For the control of multiple motors, a microprocessor must control at least two motors at the same time, and it must have strong real-time data processing capability. Because of the closed-loop control, it must be able to receive signals from both encoders measuring motor velocity. To satisfy the above requirements, the microprocessor TMS320F28335 made by the TI corporation is selected in this work. It has 32 bytes size and clock frequency of 150 MHz, and high-precision PWM output, and floating-point computing capability.  
In the vibration machine, the physical unit of the phase is usually expressed as the degree. Therefore, a full rotation of the motor has 360 degrees. Hence, the accuracy of the feedback sensor (such as encoders) must be smaller than one degree. Considering comprehensively the above requirements, the incremental encoder E682-CWZ6C made by OMROM is selected, which has 1000 resolution.  
This work adopts microprocessor TMS320F28335 to assemble a digital signal control system (DSC), which consists of the power circuit, the driven circuit, the sampling circuit, and so on.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Code** & **Designation** & **Application** \\ \hline DSC & Control platform & Velocity and phase control of motor \\ PD & Power-driven circuit & Supplying power for direct current motor \\ LS & Signal processing circuit & Processing signal of the encoder \\ CK & Keyboard & Receiving order \\ PE & Photoelectric sensor & Measuring motor speed and the phase difference \\ E1 & Encoder & Providing motor phase signal to control board \\ AS1 & Acceleration sensor(accelerometer) & Measuring the acceleration amplitude of the body \\ M1 & DC vibration motor & Driven ER \\ VB & Vibration frame & Fixed motor \\ SP & Spring & Connecting vibration frame and fixed base \\ FB & Fixed base & Supporting vibration frame and spring \\ \hline \hline \end{tabular}
\end{table}
Table 1: Code table.  
Figure 4: Control schematic diagram of the hardware circuit.
# Serial Decoders-Based Auto-Encoders for Image Reconstruction
## 4 Experiments  
### Experimental Data Sets  
The purpose of the simulation experiments is to compare the data reconstruction performance of the proposed cascade decoders-based auto-encoders and the classical auto-encoders.  
Four data sets are utilized to evaluate algorithm performance [29; 30; 31; 32]. The mixed national institute of standards and technology (MNIST) data set has 10 classes of handwritten digit images [29]; the extending MNIST (EMNIST) data set holds 6 subcategories of handwritten digit and letter images [30]; the fashion-MNIST (FMNIST) data set possesses 10 classes of fashion product images [31]; and the medical MNIST (MMNIST) data set owns 10 subcategories of medical images [32]. The image size is 28 \(\times\) 28. All color images are converted into gray images. In order to reduce the computational load, small resolution images and gray images are chosen. Certainly, if the computational capability is ensured, the proposed methods can be easily and directly utilized on large resolution images, the components of color images or their sub-patches. A large image can be divided into small patches. In traditional image compression methods, the size of image patch for compression is 8 \(\times\) 8. Therefore, the proposed methods can be used for each image block. In brief, large image size will not degrade the performance of the proposed methods from the viewpoint of small image patches. For the convenience of training and testing deep neural networks, each pixel value is normalized from range [0, 255] to range [\(-\)1, +1] in the phase of pre-processing, and is re-scaled back to range [0, 255] in the phase of post-processing. The numbers of classes and samples in the four data sets are enumerated in Table 3. The sample images of the four data sets are illustrated in Figure 11. From top to bottom, there are images of MNIST digits, EMINIST digits, EMNIST letters, FMNIST goods, MMNIST breast, chest, derma, optical coherence tomography (OCT), axial organ, coronal organ, sagittal organ, pathology, pneumonia, and retina.