esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Algorithm
More specifically, in sparse datasets, the CF\({}_{\text{DR}}\) algorithm outperforms other algorithms in the prediction coverage by a margin ranging from 1.7% to 6.6% in absolute numbers (or 4.9% to 21.7% as a relative increase), observed for the CF\({}_{\text{FOAF}}\)[29] and CF\({}_{\text{VNN}}\)[27] algorithms, respectively. The corresponding accuracy improvement, as quantified by the MAE metric, varies from 0.71% to 0.98% in absolute numbers (or 45.5% to 80% as a relative increase), observed for the CF\({}_{\text{VNN}}\)[27] and CF\({}_{\text{FOAF}}\)[29] algorithms, respectively. It is worth noting that the papers cited in Table 5 employ different techniques to quantify coverage increase and rating prediction accuracy: [33] and [45] use a five-fold cross-validation, while [27] and [29] employ the hide-one technique. For comparison fairness purposes, we ran additional experiments to compute the coverage increase and the error metrics using the five-fold cross-validation and the hide-one techniques, and when comparing the CF\({}_{\text{DR}}\) with another algorithm, we used the results obtained using the same technique employed in the

\[\text{HM(s)}=2*\frac{normCovInc(s)\times normAccInc(s)}{normCovInc(s)+normAccInc (s)} \tag{6}\]7 Rating prediction RMSE for the datasets summarized in Table 4, when the CS user similarity metric is used. original algorithm publication. In all additional experiments, the CF\({}_{\text{DR}}\) hyperparameters were set to the best performing composition, i.e., (thr\({}_{\text{NN}}\) = 3, thr\({}_{\text{sim}}\) = 0.0, w\({}_{\text{dr}}\) = 1.0). In dense datasets, the CF\({}_{\text{DR}}\) algorithm outperforms other algorithms in coverage increase by a margin ranging from 0.59% to 0.64% in absolute numbers (or 28.5% to 33.7% as a relative increase), as observed for the HyCov [33] and CF\({}_{\text{FOAF}}\)[29] algorithms, respectively. The corresponding accuracy improvement, as quantified by the MAE metric, varies from 0.15% to 0.54% in absolute numbers (or 32.6% to 450% as a relative increase), observed for the CF\({}_{\text{VNN}}\)[27] and the HyCov [33] algorithms, respectively. ## 6 Conclusions and Future Work

In this paper, we presented the CF\({}_{\text{DR}}\) algorithm, which is a novel CF algorithm for enhancing the density of sparse CF datasets. The presented algorithm introduced the concept of derived ratings, which are ratings formulated by robust predictions. The experimental results indicated that the optimal setting for considering a prediction as robust (and hence create a derived rating) is when at least three
# Dynamic Simulation Model and Performance Optimization of a Pressurized Pulsed Water Jet Device
## 3 Simulation of Dynamic Characteristics of the PPWJ Device
### Governing Equations
#### 3.2.3 The Flow Rate Continuity Equation  
Taking the oil compression and leakage into consideration, the flow continuity equation can be expressed as  
\[\left\{\begin{array}{l}\frac{V_{2}+\frac{\pi}{4}(d_{\rm v3}^{2}-d_{\rm v2}^{ 2})x_{\rm v}}{K_{\rm e}}\frac{\mathrm{d}p_{2}}{\mathrm{d}t}=q_{\rm p1}-\frac{ \pi}{4}(d_{\rm p3}^{2}-d_{\rm p2}^{2})\dot{x}_{\rm p}-Cq_{\rm p2}-q_{1}-q_{2} \\ \frac{V_{5}-\frac{\pi}{4}d_{\rm p1}^{2}x_{\rm v}}{K_{\rm e}}\frac{\mathrm{d}p_ {5}}{\mathrm{d}t}=\frac{\pi}{4}d_{\rm p1}^{2}\dot{x}_{\rm p}-(q_{\rm p3}-q_{3} )\\ \frac{V_{6}+\frac{\pi}{4}(d_{\rm v3}^{2}-d_{\rm v1}^{2})x_{\rm v}}{K_{\rm e}} \frac{\mathrm{d}p_{6}}{\mathrm{d}t}=q_{\rm v1}-\frac{\pi}{4}(d_{\rm v4}^{2}-d_{ \rm v1}^{2})\dot{x}_{\rm v}-q_{4}-q_{7}\\ \frac{V_{9}-\frac{\pi}{4}(d_{\rm v3}^{2}-d_{\rm v2}^{2})x_{\rm v}}{K_{\rm e}} \frac{\mathrm{d}p_{9}}{\mathrm{d}t}=\frac{\pi}{4}(d_{\rm v3}^{2}-d_{\rm v2}^{2} )\dot{x}_{\rm v}-(q_{\rm v2}-q_{5}-q_{6})\end{array}\right. \tag{18}\]  
Figure 4: The motion state of the valve core.  
\[\left\{\begin{array}{l}q_{1}=\frac{\pi d_{\rm p2}\delta_{\rm p1}^{2}}{12\mu_{ \rm p1}}(p_{2}-p_{\rm out})\\ q_{2}=\frac{\pi d_{\rm p3}\delta_{\rm p2}^{3}}{12\mu_{\rm p2}}(p_{2}-p_{\rm out })\\ q_{3}=\frac{\pi d_{\rm p4}\delta_{\rm p3}^{3}}{12\mu_{\rm p3}}(p_{5}-p_{\rm out })\\ q_{4}=\frac{\pi d_{\rm v1}\delta_{\rm v1}^{3}}{12\mu_{\rm v1}}(p_{6}-p_{\rm out })\\ q_{5}=\frac{\pi d_{\rm v2}\delta_{\rm v2}^{3}}{12\mu_{\rm v2}}(p_{9}-p_{\rm out })\\ q_{6}=\frac{\pi d_{\rm v3}\delta_{\rm v3}^{3}}{12\mu_{\rm v3}}(p_{9}-p_{\rm out })\\ q_{7}=\frac{\pi d_{\rm v4}\delta_{\rm v4}^{3}}{12\mu_{\rm v4}}(p_{10}-p_{\rm out })\end{array}\right. \tag{19}\]  
where \(V_{2}\) and \(V_{5}\) are, respectively, the initial volume of chamber 2 and chamber 5 when the piston is in the left position; \(V_{6}\) and \(V_{9}\) are, respectively, the initial volume of chamber 6 and chamber 9 when the valve core is in the left position, and \(V_{2}=V_{6}\); \(K_{\rm e}\) is the hydraulic oil bulk modulus; \(q_{\rm p1}\), \(q_{\rm p2}\), \(q_{\rm v1}\), and \(q_{\rm v2}\) are, respectively, the flow rate of chamber 2, chamber 3, chamber 6, and chamber 9; \(C\) is the state judgment quantity, and when chamber 2 and chamber 3 are connected, \(C=1\); otherwise, \(C=0\); \(q_{\rm i}\) is the leakage flow rate of the high-pressure chambers (i = 1, 2, 3, 4, 5, 6, 7); \(l_{\rm pi}\) and \(\delta_{\rm pi}\) are, respectively, the length and clearance of the matching surface between the piston and cylinder (i = 1, 2, 3); \(l_{\rm vi}\) and \(\delta_{\rm vi}\) are, respectively, the length and clearance of the matching surface between the valve core and cylinder (i = 1, 2, 3, 4); and \(\mu\) is the hydraulic oil kinematic viscosity.
# Attention Mechanism Used in Monocular Depth Estimation: An Overview
## 3 Attention Mechanism in MDE Methods
### CNN-Based Attention Methods
#### 3.2.2 Self-Supervised Training
A combination of spatial attention based on dot product operations with channel attention of CBAM is used for self-supervised MDE [52]. However, the order of spatial attention and channel attention is reverse to that of CBAM. A hard attention strategy is also used to weigh multi-scale depth maps in the calculation of photometric loss. To balance the global average pooling and maximum pooling of CBAM, [PERSON] et al. proposed normalized CBAM (NCBAM) by limiting the input features to a range of [\(-\)1,1] [53]. NCBAM modules are incorporated into skip connections of the depth prediction networks, as well as the pose estimation networks, to mitigate the effects of texture-copy and depth drift. Cost volume created by features of two consecutive frames  
Figure 14: Channel attention module of MLDA-Net (image: courtesy to [PERSON] et al. [50]). Figure 13: Attention module used in [48] (image: courtesy to [PERSON] et al. [48]). contains geometric and photometric information, and can thus be used for depth prediction. To recalibrate the cost volume and enlarge the receptive field, [PERSON] et al. designed a voxel-based attention model via 3D convolutional operations and a recurrent-based attention mechanism via sequential 2D convolution along the depth dimension [54]. A lightweight framework is proposed for running on various embedded devices [55] of which channel attention blocks are inserted in the decoder stage. [PERSON] et al. presented an unsupervised MDE method using stereo pairs [56]. In their framework, triaxial squeeze attention modules (TSAM) are embedded into the encoder structures. TSAM, shown in Figure 15, means that attention operations are performed over three directions: vertical, horizontal, and channel. [PERSON] et al. proposed an unsupervised MDE approach using a multi-warp reconstruction strategy [57]. As for attention, they adopted the channel and spatial attention operations embedded into the structures of the encoder and decoder. [PERSON] et al. presented an unsupervised MDE method for autonomous driving, in which channel and spatial attention blocks are inserted in each stage of the decoder structure [58]. Another contribution is that they proposed a scale awareness loss to determine the absolute scale information.
# Open-Source MQTT-Based End-to-End IoT System for Smart City Scenarios
###### Abstract  
Many innovative services are emerging based on the Internet of Things (IoT) technology, aiming at fostering better sustainability of our cities. New solutions integrating Information and Communications Technologies (ICTs) with sustainable transport media are encouraged by several public administrations in the so-called _Smart City_ scenario, where heterogeneous users in city roads call for safer mobility. Among several possible applications, recently, there has been a lot of attention on the so-called Vulnerable Road Users (VRUs), such as pedestrians or bikers. They can be equipped with wearable sensors that are able to communicate their data through a chain of devices towards the cloud for agile and effective control of their mobility. This work describes a complete end-to-end IoT system implemented through the integration of different complementary technologies, whose main purpose is to monitor the information related to road users generated by wearable sensors. The system has been implemented using an ESP32 micro-controller connected to the sensors and communicating through a Bluetooth Low Energy (BLE) interface with an Android device, which is assumed to always be carried by any road user. Based on this, we use it as a gateway node, acting as a real-time asynchronous publisher of a Message Queue Telemetry Transport (MQTT) protocol chain. The MQTT broker is configured on a Raspberry PI device and collects sensor data to be sent to a web-based control panel that performs data monitoring and processing. All the architecture modules have been implemented through open-source technologies. The analysis of the BLE packet exchange has been carried out by resorting to the Wireshark packet analyzer. In addition, a feasibility analysis has been carried out by showing the capability of the proposed solution to show the values gathered through the sensors on a remote dashboard. The developed system is publicly available to allow the possible integration of other modules for additional Smart City services or extension to further ICT applications.  
Internet of Things; Smart Cities; MQTT; BLE; Android; Raspberry PI; open-source +
Footnote †: journal: _Future Internet_  
0222
## 3 Feature Extraction of Internal Leakage Fault  
This section analyzes the internal leakage feature extraction based on the pressure signal in the pressure maintenance phase (Phase 4). Firstly, from the analysis of the frequency domain, the influence of the internal leakage on energy attenuation and energy distribution of signal sub-band was revealed, and three frequency-domain features were extracted. Then, in time domain, the influence of the internal leakage on the steady-state response of the pressure maintaining signal was revealed, and two time-domain features were extracted.
## 4 RF Energy-Harvesting Techniques
### Passive vs. Active RF Energy Harvesting
Active systems, while more complex and requiring an external power source, offer enhanced performance and reliability [49; 58; 66]. They are ideal for applications demanding higher power levels or operating in environments with low ambient RF energy. The integration of active components allows for the better control and optimization of the RFEH process, making them suitable for critical applications such as medical implants and wearable electronics. For example, closed-loop control circuits in active systems can dynamically adjust load impedance or antenna parameters based on real-time input, improving conversion efficiency and enabling smart energy management.
## 3 An Approach to Generate the MEP Logic Chain from BIMs
### Spatial Topological Analysis within MEP Models
#### 3.1.2 The Nonparametric Topological Analysis Algorithm  
There are many other nonparametric data formats available for MEP modeling, such as triangle-based B-rep representations. For example, if these objects are imported through rendering-oriented software such as 3 ds Max, or shape-oriented design tools, such as CATIA or Sketch Up. Moreover, some BIM platforms focus on efficient 3D display to help real-time management, and they will transform the parametric models to triangles to enhance the OpenGL or DirectX render efficiencies. Therefore, it is necessary to propose the nonparametric topological analysis algorithm.  
Generally, pipe fittings, equipment, and short pipes have different kinds of connections between each other. The connections between segments are usually in one-to-one form, while for fittings and segments are usually in one-to-many form. For example, a reducing tee connects three pipes. Considering the magnitude of errors buried in MEP modeling, a deviation threshold, i.e., 10 mm between outlets of MEP segments, fittings or equipment can be determined as touching. The algorithm to determine the relation between components is depicted as follows.  
The test can return the result of spatial analysis of triangles, which distinguish between touching and intersecting triangles. If a pair of triangles from A and B intersect, this also indicates an intersection of the two interiors of the objects under examination. It is possible that A covers B, or vice versa. A and B can, nevertheless, still touch each other. To disprove the inside relationships, the algorithm is realized by means of ray tests. Each vertex of a triangle is used as a starting-point for rays. The rays are emitted from the selected vertex and the number of intersections with the B is counted. An even intersection number indicates that the triangle is located outside, whereas an odd number indicates that the triangle is inside another.  
After iterating all components and spaces, the connection tables of the MEP components and the space are ready for the next step. The connection tables can also be used to record the connection components or equipment. Each connection pair indicates that the two components are connected in space, the connection type and the name of components. For example, DS-SS- <E1, E2> indicates that entity E1 is connected to entity E2, both are segments in the duct system. Other typical connection types include FS, ES, which stand for fittings or equipment connected to segment, respectively. Moreover, different types can then be used for analysis of upstream or downstream relations.
# A Statistical Approach to Detect Jamming Attacks in Wireless Sensor Networks
## 7 Discussion  
To detect a jamming attack in a WSN, it is important to use a minimum number of metrics that can detect this attack to reduce complexity and associated overhead for resource-constrained sensor nodes. Therefore, in this work, we have proposed only the packet IAT to detect the change in traffic pattern during jamming attack using EWMA algorithm and deployed in the cluster head and base station with higher resources. Furthermore, the choice of packets IAT as compared to others makes it difficult for the attacker to manipulate our selected metric and still evade detection.  
In evaluating our proposed algorithm, we have used a publicly available dataset, CRAWDAD [36] comprising of three different jamming attacks, namely constant jamming, periodic jamming and reactive jamming; together with a trace of no jamming. Our evaluations show that even with 20 packets of non-jamming and jamming trace, our proposed algorithm detects an abrupt change, thus showing the efficiency of our algorithm. Table 1 presents the result of our findings.  
Our proposed technique has also been compared with other closely related proposed methods in the literature by considering the detection metric, jamming attack detected, simulator used and accuracy (see Table 2). We observe that most of the proposed methods produced a very high detection rate, just like our technique. However, most of these methods combined different detection techniques and use more than one detection metric, unlike our proposed technique, which tends to incur more overhead to the already resource constrained sensor node.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Approach** & **Detection Metrics** & \begin{tabular}{c} **Jamming Attack** \\ **Detected** \\ \end{tabular} & **Simulator** & **Accuracy** \\ \hline \hline \begin{tabular}{c} Error sample acquisition, \\ interference detection \\ and sequential jamming \\ test [16] \\ \end{tabular} & RSS & Reactive & \begin{tabular}{c} COTS BTnodes and \\ Tmote Sky nodes \\ \end{tabular} & \begin{tabular}{c} For \(\geq\)16 jammed \\ bits: 100\% \\ \end{tabular} \\ \hline \begin{tabular}{c} Query-based jamming \\ detection algorithm \\ (QUJDA) [23] \\ \end{tabular} & PDR, BPR and ECA & \
\begin{tabular}{c} Reactive, Random, \\ Constant, Cluster, \\ Deceptive, Listen \\ and Control \\ \end{tabular} & OMNET++ &
\begin{tabular}{c} 97\% and above for \\ varying jamming \\ attacks. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of jamming detection approaches in WSN.  
Figure 5: EWMA plot of the IAT metric against packet number for reactive jamming attack.
# Contactless Palm Vein Recognition Based on Attention-Gated Residual U-Net and ECA-ResNet
## 3 Attention-Gated Residual U-Net and ECA-ResNet
### Palm Vein Recognition
#### 3.3.2 Proposed ECA-ResNet  
Feature extraction is an essential process in biometrics recognition, which aims to extract key features from the input image. At this stage, to improve the prediction accuracy and generalization ability, the modified CNN based on ECA and ResNet-50 was proposed as the feature extractor to produce 512 feature embedding vectors for classification and metric learning. The ECA module is introduced into the ResNet-50 to enable the models to learn the channel-wise information in the feature maps in an adaptive and efficient manner without computational overhead. The modified ResNet-50 model solves the problem of the vanishing gradient or the degradation problem where the repeated multiplication in the back-propagation may cause the gradient to be infinitely small. After the residual blocks, the average pooling operation is applied, and 512-D feature embedding is separately fed into the binary head and margin head for the purpose of multi-task training.  
Figure 8 shows the overall proposed network architecture. The modified ResNet-50 architecture, which consists of convolution layers, pooling layers, and an efficient channel attention module, is used as the backbone structure to extract palm vein features. The proposed network model takes segmented palm vein images from prior U-Net models as input with a 1 \(\times\) 112 \(\times\) 112 size. The network backbone structure comprises four convolutional layer block stages, consisting of 3, 4, 6, and 3, and the feature maps are \(64\times 56\times 56\), 128 \(\times\) 28 \(\times\) 28, 256 \(\times\) 14 \(\times\) 14, and 512 \(\times\) 7 \(\times\) 7 each. The ECA module is applied after two convolutions on each residual block to enhance the critical features while suppressing irrelevant features. The ECA module contains a series of global average pooling, a 1 \(\times\) 1 convolution, and a sigmoid activation function, the task of which is to compute the attention weights and refine the feature map to form F\({}_{0}\). This operation is repeated until the last convolutional block, followed by an average pooling operation and batch normalization to form the 512-feature embeddings.  
Figure 7: Efficient channel attention (ECA) module.
# Train Wheel Condition Monitoring via Cepstral Analysis of Axle Box Accelerations
###### Abstract
In addition, supervised machine learning approaches need sufficient labeled data for training. A strong focus on machine learning techniques bears the risk that powerful traditional signal processing techniques are overseen, even when they might be the right choice for a specific data analysis problem. One example of such a powerful but uncommonly used tool is the cepstrum analysis. It dates back to the 1960s and 1970s, when it was introduced to analyze, e.g., echoes and reverberations in radar, sonar, speech and seismic data. For a detailed overview see [19; 20] and the references therein. The power cepstrum was first defined by [PERSON] et al. [21] as the power spectrum of the logarithm of the power spectrum of a signal. In contrast to the complex cepstrum it does not consider any phase information and therefore only involves the logarithm of real, positive numbers. [PERSON] and [PERSON] [22] used cepstrum analysis of rail acceleration to detect wheel flats of railway wheels. They identified the power cepstrum as the best instrument to reveal periodic acceleration peaks as those excited by wheel flats. Here, we adapt this methodology to the analysis of ABA data for wheel condition monitoring. Specifically, and in analogy to the estimation of the arrival times of echoes and their relative amplitudes, we use the power cepstrum to estimate the wheel circumference and the relative contribution of the wheel imperfections to the excited wheel vibrations. The main contribution of this research is to introduce a simple, robust and yet precise methodology to extract wheel wear related features from the ABA signals without relying on a priori knowledge or training data. This methodology relies on the processing of ABA data in the distance domain. That is, the ABA time series need to be transformed usingspeed information so as to obtain spatial acceleration signatures. Hence, the vehicle speed must be estimated using further onboard sensors, namely a global navigation satellite system (GNSS) receiver [23] and an inertial measurement unit (IMU) [24]. The estimation relies on Kalman filter methods [25]. The remainder of the paper is structured as follows. Section 2 provides the theoretical background by reviewing the calculation of the power cepstrum and introducing the term navewumber. In Section 3, the sensitivity of the cepstral analysis to different wheel surface defects is tested by means of simple synthetic data. In Section 4, experimental data are used to test the performance of the cepstrum under real-world conditions. Data pre-processing and vehicle speed estimation is also explained in this section. In Section 5, the results are discussed and Section 6 provides a final conclusion.