esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Network and cloud environments must be fortified against a dynamic array of threats, and intrusion detection systems (IDSs) are critical tools for identifying and thwarting hostile activities. IDSs, classified as anomaly-based or signature-based, have increasingly incorporated deep learning models into their framework. Recently, significant advancements have been made in anomaly-based IDSs, particularly those using machine learning, where attack detection accuracy has been notably high. Our proposed method demonstrates that deep learning models can achieve unprecedented success in identifying both known and unknown threats within cloud environments. However, existing benchmark datasets for intrusion detection typically contain more normal traffic samples than attack samples to reflect real-world network traffic. This imbalance in the training data makes it more challenging for IDSs to accurately detect specific types of attacks. Thus, our challenges arise from two key factors, unbalanced training data and the emergence of new, unidentified threats. To address these issues, we present a hybrid transformer-convolutional neural network (Transformer-CNN) deep learning model, which leverages data resampling techniques such as adaptive synthetic (ADASYN), synthetic minority oversampling technique (SMOTE), edited nearest neighbors (ENN), and class weights to overcome class imbalance. The transformer component of our model is employed for contextual feature extraction, enabling the system to analyze relationships and patterns in the data effectively. In contrast, the CNN is responsible for final classification, processing the extracted features to accurately identify specific attack types. The Transformer-CNN model focuses on three primary objectives to enhance detection accuracy and performance: (1) reducing false positives and false negatives, (2) enabling real-time intrusion detection in high-speed networks, and (3) detecting zero-day attacks. We evaluate our proposed model, Transformer-CNN, using the NF-UNSW-NB15-v2 and CICIDS2017 benchmark datasets, and assess its performance with metrics such as accuracy, precision, recall, and F1-score. The results demonstrate that our method achieves an impressive 99.71% accuracy in binary classification and 99.02% in multi-class classification on the NF-UNSW-NB15-v2 dataset, while for the CICIDS2017 dataset, it reaches 99.93% in binary classification and 99.13% in multi-class classification, significantly outperforming existing models. This proves the enhanced capability of our IDS in defending cloud environments against intrusions, including zero-day attacks.  
ADASYN; data resampling; deep learning; ENN; IDS; multi-class classification; Transformer-CNN
## 1 Introduction
In this paper, the PSO design of PID controllers based on an additive compromise cost function involving the Integral Absolute Error (IAE) and Total Variation (TV) is proposed. This technique is validated both with simulation and practical results obtained with the TCLab. The results obtained with the proposed technique are compared with the ones obtained with the GWO algorithm and classical tuning techniques. The novel contributions of this paper are highlighted in the following points:  
* New formulation for an additive compromise (or aggregated) cost function involving the Integral Absolute Error (IAE) and Total Variation (TV). Major control design criteria concern optimizing set-point tracking and disturbance rejection, while minimizing the control signal variation. This proposed technique significantly simplifies the PID controller design procedure combining these criteria into a single-objective optimization formulation. * PSO algorithm to design PID controllers that minimize a cost function weighting IAE and TV. A simple PSO algorithm constitutes an excellent design tool for the PID controller, with practical interest for control engineers. The proposed technique is compared with the original GWO algorithm, in a TCLab temperature control case study, providing a similar control performance. * Both the simulation and practical validation with TCLab tests, show an effectiveness to design PID controllers by softening the control signal. The paper remainder is organized as follows: Section 2 introduces the problem statement. In Section 3, the classical particle swarm optimization algorithm is presented. Sections 4 and 5 discuss the TCLab main features and results. Finally, Section 6 provides the conclusions and recommendations for future work.
# A Novel SDWSN-Based Testbed for IoT Smart Applications
## 3 System Model
### WSN Physical Network Setup for Node Distribution on the Ground
#### 3.3.1 Energy Consumption Model for Communication Network  
The energy consumption model for enabling communication among the ground network components is calculated based on the proposed model. Hence, the amount of each network components' power consumption for this purpose is as in Equation (1):  
\[P_{T}=P_{Tx}+P_{Rx}+P_{LPM}+P_{Idle} \tag{1}\]  
This means the average power consumption of each node is the summation of the average power consumption of a node in four modes: idle mode, low power mode (LPM), and receiving and transmitting modes. The idle mode (\(P_{Idle}\)) is activated whenever the node is listening (the time interval during which the CPU is inactive prior to the radio transmitter or receiver becoming active). LPM mode (\(P_{LPM}\)) is activated when the sensor node goes to low power mode. Rx mode (\(P_{Rx}\)) is activated in radio receive mode and finally Tx mode (\(P_{Tx}\)) is activated in transmission mode.  
Sensor nodes operate in either active mode or sleep mode. The ratio of the time spent in active mode to a total data period is defined as the duty cycle. In general, sensors mainly consume energy during data receiving and transmitting, and idle listening when they are in active mode. The proposed energy model uses the Contiki power tracker to measure the time intervals that each node spends in these four modes [32]. Hence, the overall energy consumption per node can be calculated considering the equivalent consumed energy for these intervals as in Equation (2):  
\[Cost=\sum_{i=1}^{4}P_{i}\times T_{i} \tag{2}\]  
where \(P_{i}\) represents the value of consumed power within each power mode, \(T_{i}\) is the time spent during a specific mode \(i\).  
Figure 16: Proposed network architecture for physical network implementation.  
Power calculation analysis was conducted based on the information explored from the CC2538 datasheet for values of current usages, once the module is in active receiving, transmitting, idle, and low power modes, as shown in Table 3.
# Identification of Damage in Beams by Modal Curvatures Using Acoustic Beamformers
## 4 Inverse Problem
### Experimental Tests  
In this section, we compare the performance of the beamformers and of the traditional minimization procedure using experimental data; that is, the vectors \(\mathbf{d}_{i}\) are determined by the set of experiments described in Section 3.2 with the damage scenarios in Tables 3 and 4. In all cases, the first three modal curvatures are included in the summation of the estimators. As pointed out in Section 3.2, for each damage scenario, the dynamic response was measured ten times. The estimates of curvatures of the \(m\)-th test are \(\mathbf{d}_{i}^{m}\), and the values of estimators are calculated by summing the estimates from single tests taken individually:  
\[\begin{array}{l}H(\mathbf{x})=\sum_{m=1}^{10}\sum_{i=1}^{3}||\bar{\mathbf{x} }_{i}(\mathbf{x})-\mathbf{d}_{i}^{m}||\\ B_{Bartletlet}(\mathbf{x})=\sum_{m=1}^{10}\sum_{i=1}^{3}B_{Bart,i}^{m}(\mathbf{ x})\\ B_{MVDR}(\mathbf{x})=\sum_{m=1}^{10}\sum_{i=1}^{3}B_{MVDR,i}^{m}(\mathbf{x}). \end{array} \tag{12}\]  
Figure 11: Contour plots of the MVDR beamformer including one ((**a**) \(i=1\), (**b**) \(i=2\), (**c**) \(i=3\)) and three numerical modal curvatures ((**d**) \(i=1\), \(2\), \(3\)) (circle: correct damage parameters).  
Figure 10: Contour plots of the Bartlett beamformer including one ((**a**) \(i=1\), (**b**) \(i=2\), (**c**) \(i=3\)) and three numerical modal curvatures ((**d**) \(i=1\), \(2\), \(3\)) (circle: correct damage parameters).
## 5 Experiment
### Experimental Components  
The experiments were run on an i7 machine with 16 GB RAM. Each experiment was repeated 40 times to record the average and specific performances. The maximum simulation time of each trial was 30 min. The rate parameters in the DD_Model were initialized based on the [PERSON] et al. [13] study, where \(\alpha=0.5\), \(\beta=0.33\), \(\gamma=0.17\), and \(\varepsilon=30\). It was assumed in the experiments that any resource could have a deadline and expire if not collected before its expiration time. Thus, the mortality rate was initialized as \(\eta=1-\gamma\).  
To study how ED_Foraging can manage online deadlines in an unknown environment, the proposed foraging approach was tested with different DTA methods, including hotspot DTA. The first three methods are self-organized DTA, and the fourth method is centralized DTA.  
* Hotspot--The proposed DTA method discussed in Section 4.
* ACO--Adopted from the MPFA, which is ACO-based DTA with multiple collection zones, where ACO parameters are initialized as in [25].
* Blind--Implemented to allocate robots randomly and search blindly in multiple collection zones. No information is reported on the zones or exchanged between the robots.
* Centralized--A traditional DTA for swarm foraging, where there is only one collection zone (central) and the search is blind and random.  
Figure 5: Screenshot of multi-place foraging in the ARGoS simulator: Bird-eye vs. Zoomed screenshot.  
Throughout the experiments, different measures were collected. The first measure was the foraging rate, which represents the number of resources delivered to the central collection zone (a higher foraging rate corresponds to a better performance). The second measure was the expiry rate, which represents the percentage of resources with expired deadlines. A lower expiry rate corresponds to a better performance. The expiry rate was computed by dividing the number of resources that died (expired deadlines) by the total number of resources scheduled (expected) to die.
## 2 Related Work
### Semantic Extraction  
#### 2.1.1 Image Classification and Localization  
Image classification and object recognition tasks have been investigated for a long time. Yet, for much of this period, there were no suitable general solutions available. This was mainly attributed to the quality of training data and accessible computational hardware. Moreover, the classification accuracy when using a smaller, rather than a larger, number of classes was observed to be greater [11]. However, performance in image-classification tasks has been exponentially improved in open competitions, such as the Large Scale Visual Recognition Challenge (ILSVRC) and MIT-Places-365. These competitions encouraged the development of region proposal network (RPN)-based deep neural networks, including AlexNet, GoogleNet and Vision Geometry Group (VGG). These networks have revolutionised image classification and have opened doors, in all directions, for classification and annotation. We use the VGG-16 network trained on MIT-Places-365 for obtaining the place/location of a frame, because it is very generalised and the architecture could be reused for further tasks, including the Dense Captioning of a frame that also has VGG-16 as its base architecture.  
In addition to classification tasks, the success of the above-mentioned challenges has also fuelled research on localisation and detection tasks. Speed and accuracy have been the major areas of focus and, based on these, there are two major types of object detection models: (1) region-based convolution models, such as R-CNN and Faster RCNN, that split the image into a number of sub-images, and (2) convolution models, such as Single Shot Detector (SSD) and You Only Look Once (YOLO), that detect objects in a single run [12]. Even though the Faster RCNN have slightly higher accuracy, the latest version of YOLO (YOLOv3 [12]) detects objects up to 20 times faster while retaining similar/acceptable accuracy. Thus, our pipeline has a pre-trained YOLOv3 model that has been used for detecting objects and persons in a frame.
# Hybrid Techniques for the Diagnosis of Acute Lymphoblastic Leukemia Based on Fusion of CNN Features
## 4 Results of System Evaluation
### Results of Strategy of Machine-Learning with Features CNN  
This section presents the performance of hybrid systems between deep and machine-learning for analyzing microscopic blood images for diagnosing two acute leukemia datasets. The mechanism of action of this technique is to segment the WBC cell region and isolate it from the rest of the basic blood components. The DenseNet121, ResNet50, and MobileNet models received the region of interest, extracted the deep feature maps, reduced the dimensions, and saved the high representation features by PCA. RF and XGBoost classifiers received highly representative features and classified them with high accuracy. The RF and XGBoost classifiers with features DenseNet121, ResNet50, and MobileNet yielded good results for classifying acute leukemia's C-NMC 2019 and ALL-IDB2 datasets. First, for the C-NMC 2019 dataset, as shown in Table 6 and Figure 9, the RF classifier with DenseNet121 features achieved an AUC of 98.15%, accuracy of 95%, sensitivity of 94.8%, precision of 94.15%, and specificity of 94.3%. In contrast, RF achieved with ResNet50 features achieved an AUC of 97.25%, accuracy of 95.5%, sensitivity of 95.35%, precision of 94.8%, and specificity of 94.7%. RF with MobileNet features achieved an AUC of 96.75%, accuracy of 96.2%, sensitivity of 95.75%, precision of 95.55%, and specificity of 95.65%. The XGBoost classifier with DenseNet121 features achieved an AUC of 95.5%, accuracy of 94.7%, sensitivity of 96.1%, precision of 93.7%, and specificity of 93.8%. In contrast, XGBoost achieved with ResNet50 features achieved an AUC of 97.65%, accuracy of 95.4%, sensitivity of 94.15%, precision of 95.25%, and specificity of 94.3%. XGBoost with MobileNet features achieved an AUC of 96%, accuracy of 95.3%, sensitivity of 94.35%, precision of 94.45%, and specificity of 94.75%. Figure 8: Display of results of CNN models based on WBC segmentation of the C-NMC 2019 and ALL-IDB2 datasets for acute leukemia diagnosis. Second, for the ALL-IDB2 dataset, RF and XGBoost classifiers with CNN-fused features achieved promising results. The RF classifier with features of DenseNet121, ResNet50, and MobileNet achieved accuracy, AUC, sensitivity, precision, and specificity of 100%. In contrast, the XGBoost classifier with features of DenseNet121, ResNet50, and MobileNet achieved accuracy, AUC, sensitivity, precision, and specificity of 100%. When evaluating the systems for the dataset C-NMC 2019: The RF classifier with CNN features produces a confusion matrix, as shown in Figure 10. The hybrid systems DenseNet121-RF, ResNet50-RF, and MobileNet-RF achieved good results for each class. First, DenseNet121-RF attained an accuracy of 96.1% and 92.6 for ALL and normal classes. Second, ResNet50-RF attained an accuracy of 96.6% and 93.4% for ALL and normal classes. Third, MobileNet-RF attained an accuracy of 97.1% and 94.2% for ALL and normal classes.
## 3 Materials and Methods
### Training Dataset for IDS  
#### 3.2.1 TON_IoT Dataset  
This is a heterogeneous dataset specifically captured for intrusion detection in IoT applications totaling 461,043 instances. The edge layer of the testbed comprises sensors, weather stations, smart TVs, gateways, and Virtual Machines (VMs) managing the network. The fog layer includes client and vulnerable VMs, along with offensive Kali systems. Additionally, the fog layer hosts a middleware server that manages seven IoT sensors and their message queuing telemetry transport (MQTT) services using the Node-RED tool. The cloud layer encompasses servers simulating communication with the cloud, a vulnerable website for attack simulations, and an MQTT broker. This testbed covers various attack categories, including port scanning, DoS/DDoS, ransomware, backdoor, data injection, cross-site scripting, brute force, and man-in-the-middle attacks. A total of 45 features are extracted from this testbed.  
The main points considered for selection of the dataset are as follows:  
* Testbed architecture;
* Number of features;
* Number of attacks simulated.  
The MQTT IoT dataset simulates only data of sensors that are communicating over MQTT and there are only four attack categories covered [38]. The Bot-IoT dataset focuses more on the network and there are a fewer number of attacks covered [39]. In the TON_IoT dataset, the testbed is heterogeneous and mimics the behavior of the edge, fog, and cloud side very well. Moreover, this testbed includes seven IoT sensors, and it covers a wide range of attacks that proved helpful while doing load balancing for the distributed approach for the IDS.
## 4 Experiments
### Ablation Experiments and Result Analysis
#### 4.4.5 Cam  
The CAM achieves targeted optimization of network representation by focusing on channel features that play a key role in change analysis. To verify its necessity, ablation experiments were conducted by removing this module. The data in Row 7 of Table 1 show that although removing the CAM yields performance close to the complete model on LEVIR-CD and WHU-CD, the F1-score on the CLCD dataset decreases from 78.68 to 78.23, representing a 0.45% reduction. This indicates that it is more important for feature selection in low-contrast scenarios. The visualization results in Column g of Figure 6. show that the model without the CAM misdetects some shadow areas in the CLCD scene, with red false detection spots appearing. This confirms that the channel attention mechanism effectively enhances the robustness of feature expression by suppressing redundant channel information. The above experiments confirm the positive impact of the channel attention mechanism on fine-grained change analysis from both quantitative indicator differences and qualitative visual representations.
###### Abstract  
An all-optical comparator is desirable to realize large-capacity, fully-transparent, and energy-efficient communication systems, as it is considered to be a fundamental component to perform most of the operations, including packet switching, label recognition, error detection and correction, and so on. However, most of the previous studies have been confined to the on-off keying (OOK) modulation format, not phase-shift keying (PSK) modulation. In this paper, the author provides a novel optical comparator designed for quadrature PSK (QPSK)-modulated signal, which comprises a code word with 8-bit length, using a serially-cascaded delay line interferometer. The proposed comparator yields constellations having the information of a Hamming distance based on the designed code, when several patterns of QPSK signal are injected into the comparator. The paper experimentally demonstrates the feasibility of the optical comparison operation for 8-bit QPSK-modulated return-to-zero (RZ) signal at 10 Gbaud.  
optical communication; optical signal processing; comparison operation +
Footnote †: journal: Applied Signal Processing
###### Abstract  
This paper presents a low-voltage, low-power current differencing transconductance amplifier (CDTA) utilizing the bulk-driven MOS transistor technique in the subthreshold region for reduced voltage and power consumption. The proposed CDTA includes a z-copy terminal, which enhances its functionality in current-mode circuit applications. Designed in the Cadence Virtuoso environment using 0.18 \(\upmu\)m CMOS technology from Taiwan Semiconductor Manufacturing Company (TSMC), the amplifier operates with a supply voltage of 0.45 V and consumes 328 nW of power, with a bias current set to 10 nA. The current bandwidth and offset of the CDTA are 35 kHz and 0.3 nA, respectively. To demonstrate its performance, the CDTA is applied in a current-mode universal filter, which can realize low-pass, band-pass, high-pass, band-stop, and all-pass responses within a single topology. This design eliminates issues related to inverting input signals, input signal matching, or the need for multiple input signals. Additionally, the natural frequency of these filtering functions can be electronically controlled. The low-pass filter achieves a dynamic range of 61 dB, with a total harmonic distortion of 0.8%.  
current differencing transconductance amplifier; current-mode circuit; active filter; bulk-driven MOS transistor Article  
1  
Academic Editors: [PERSON] and [PERSON] Received: 11 February 2025  
Revised: 13 March 2025  
Accepted: 19 March 2025  
Published: 21 March 2025  
**Citation**: [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] A 328 nW, 0.45 V Current Differencing Transconductance Amplifier and Its Application in a Current-Mode Universal Filter. _Appl. Sci._**2025**, 15, 3071. [[https://doi.org/10.3390/app15073471](https://doi.org/10.3390/app15073471)]([https://doi.org/10.3390/app15073471](https://doi.org/10.3390/app15073471))  
**Copyright**: 6 2025 by the authors.  
Liconse MDPI, Basel, Switzerland.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license  
([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Examining Pipe-Borehole Wall Contact and Pullback Loads for Horizontal Directional Drilling
## 3 Analytical Model for Calculating Pipe Pullback Loads  
### Pipe-Borehole Wall Friction Force  
According to the adhesion theory developed by [PERSON], friction is primarily caused by adhesive interactions in the actual contact area [40]. The pipe-borehole wall friction force per unit length of pipe \(T_{s}\) can be expressed as follows [19]:  
\[T_{s}=\tau_{s}A_{r}, \tag{23}\]  
where \(\tau_{s}\) is the shear (or friction) stress to break the junction (or adhesion), and \(A_{r}\) is the real contact area.  
The shear stress \(\tau_{s}\) can be expressed as [41]  
\[\tau_{s}=\tau_{f}, \tag{24}\]  
where \(\tau_{f}\) is the shear strength of the borehole wall.  
Figure 8: Relationship between semi-chord length and compressive displacement.  
The real contact area ratio \(\alpha\) can be defined as [25]  
\[\alpha=\frac{A_{r}}{A_{p}}=\frac{l}{R_{1}}=\frac{k\sqrt{R\delta}}{R_{1}}, \tag{25}\]  
where \(A_{p}\) is the projected area of the pipe-borehole wall contact area.  
Correspondingly, the real contact area per unit length of the pipe is  
\[A_{r}=2\alpha R_{1}=2k\sqrt{R\delta}. \tag{26}\]  
According to the Mohr-Coulomb criterion, the shear strength of the borehole wall is defined as  
\[\tau_{f}=c+\sigma_{a}\tan\varphi, \tag{27}\]  
where \(c\) denotes the cohesion; \(\varphi\) represents the internal friction angle; \(\sigma_{a}\) signifies the normal stress, and \(\sigma_{a}=P/A_{r}\). It should be noted that the drilling fluid will generate a bentonite mud film on the borehole wall. As the part comes into contact with the pipeline, the cohesion and internal friction angle of the bentonite mud film are used when calculating the pipe-borehole wall friction force.  
By substituting Equations (22), (24)-(27) into Equation (23), the formula for the pipe-borehole wall friction force per unit pipe length \(T_{s}\) can be obtained as follows:  
\[T_{s}=2 ck\sqrt{R\delta}+P\tan\varphi. \tag{28}\]  
It can be seen that the pipe-borehole wall friction force calculated through Equation (28) does not require an empirical friction coefficient.