esa-sceva/satcom-chunk-collection huggingface dataset:
# A Combined Dynamic-Kinematic Extended Kalman Filter for Estimating Vehicle Sideslip Angle
## 3 Experimental Setup
### Experimental Lateral Characteristics of Front and Rear Axles  
To properly model type forces, experimental data are fitted with the type model presented in Section 2. Considering the equations of a single track model, front and rear lateral forces are computed by inverting the equations of motion (1) and (2):  
\[F_{yf} =\frac{b}{l\,\cos\delta}m\,a_{y}+\frac{I_{zz}}{l\,\cos\delta}\dot{r} \tag{26}\] \[F_{yr} =\frac{a}{l}m\,a_{y}-\frac{I_{zz}}{l}\dot{r}\]  
where \(\dot{r}\) is the yaw acceleration, computed by deriving the yaw rate signal using the finite difference approximation.  
Front and rear sideslip angles, \(\alpha_{f}\) and \(\alpha_{r}\) respectively, are computed from steering angle signal, yaw rate signal and longitudinal and lateral velocities:  
\[\alpha_{f} =\delta-\arctan\Bigl{(}\frac{v_{y}+ra}{v_{x}}\Bigr{)} \tag{27}\] \[\alpha_{r} =-\arctan\Bigl{(}\frac{v_{y}-rb}{v_{x}}\Bigr{)}\]  
Data are collected during different driving scenarios of the test vehicle in a previous test session.
## 5 Software Release Policy  
### Optimal Release Time and Cost  
In this section, we address the optimal release policy from the point of the view of time and cost. It can be helpful to find the optimal software release time \((\mathrm{T^{*}})\) that has the minimum expected total software development cost. Although the optimal release policy has been studied for decades, it is still a sensitive problem. For example, if the testing period is long, the software can be reliable, but the software development cost is increased. On the contrary, if the testing period is short, the product can be unreliable. Moreover, risk costs, such as the follow-up service cost, can be increased. Thus, it is crucial to find a balanced time point between release time and minimum cost.  
Figure 6 shows the system development lifecycle considered in the following cost model, which includes the testing phase before release time T, the testing environment period, the warranty period, and the operational life in the actual field environment (that is usually quite different from the testing environment) [33].  
The expected total software development cost \(\mathrm{C(T)}\) depends on various factors and it can be expressed as:  
\[\mathrm{C(T)}=\mathrm{C_{0}}+\mathrm{C_{1}T}+\mathrm{C_{2}m(T)\mu_{Y}}+\mathrm{ C_{3}(1-R(x|T))}+\mathrm{C_{4}[m(T+T_{W})-m(T)]\mu_{W}}, \tag{21}\]  
where \(\mathrm{C_{0}}\) is the set-up cost for the test, \(\mathrm{C_{1}T}\) is the cost of test, \(\mathrm{C_{2}m(T)\mu_{y}}\) is the expected cost to remove all failures detected by time T during the testing period, \(\mathrm{C_{3}(1-R(x|T))}\) is the penalty cost of removing failures that occur after the system release time T, and \(\mathrm{C_{4}[m(T+T_{W})-m(T)]\mu_{W}}\) is the expected cost to remove all of the failures that are detected during the warranty period \([\mathrm{T},\ \mathrm{T}+T_{W}]\). Furthermore, it requires the assumption that the cost required to remove errors during the operating period is higher than that during the testing period and the time that is needed is much longer.  
Finally, the expected total software cost can be calculated using the \(\mathrm{m(t)}\) function consisting of the estimated parameters. The primary purpose of the equation is to find the optimal software release time (\(\mathrm{T^{*}}\)) minimizing the expected total cost.
# Enhanced K-Nearest Neighbor for Intelligent Fault Diagnosis of Rotating Machinery
## 3 Proposed Method
### Stage 4: Nearest Neighbor Voting to Obtain Diagnosis Result  
This stage is similar to common operation in KNN, which classifies the testing samples through the voting results of the nearest neighbors obtained in stage 4, and we can obtain the predicted health condition label of the target testing sample easily.  
For the proposed method, when new faults occur in the target machines, as the case-based method preserves the training samples, the new fault samples can be added into existing training dataset to train the feature extraction part of EKNN again, which learns to diagnose them.  
The proposed method combines discriminative feature extraction with a training-free feature classification framework in the existing intelligent fault diagnosis field. It is distinct from the traditional fault diagnosis method because it can automatically diagnose the faults of machineries based on the discriminative feature. In addition, as we can see from stages 1-4, each stage of EKNN can be trained separately or needs no training, which means it has low training cost and low overfitting risk compared with fault diagnosis methods based on deep learning.
## 2 Materials and Methods
### High Spatio-Temporal Resolution Remote Sensing DA  
The rise of high temporal resolution satellite constellations has ushered in a stream of high-frequency revisit data, enabling the continuous generation of time series for critical biophysical parameters such as VIs, LAI, and phenology, thereby facilitating seamless and dynamic tracking of crop growth processes [95; 96]. This groundbreaking advancement provides an unprecedented level of detailed time series inputs for model assimilation, significantly enhancing the precision modeling capabilities of agricultural ecosystems. In cutting-edge high spatio-temporal resolution DA research, scientists employ Enhanced Spatio-Temporal Adaptive Reflectance Fusion Model to skillfully merge Landsat and MODIS data, yielding composite products with both high spatial and temporal resolutions. These are further combined with models like SAFV to accurately quantify crop yields [97]. Additionally, by leveraging RS, reanalysis, and in-situ datasets, advanced point-to-surface fusion techniques like GRASPS create high-resolution, accurate, and seamless maps of surface SM distribution, significantly improving monitoring accuracy of agricultural water status [98].  
Of particular note, high spatial resolution satellites demonstrate unparalleled advantages in revealing within-field heterogeneity, rapidly identifying crop disease patches, and precisely assessing the effects of management practices [99; 100; 101]. This suite of achievements suggests that continuously exploring and innovating in multi-source RS data fusion methods holds the promise of continually enhancing spatial and temporal resolution, thus pushing the application of DA in the agricultural sector into deeper realms [102; 103; 104]. Specifically, recent studies have proposed a series of innovative models and frameworks, such as Dual-Branch Convolutional Neural Networks (DB-CNN), the Cascade-PSPNET model integrating Bayesian theory with deep learning (DL) algorithms, and the Spatio-Temporal Fusion (STF) framework, all of which have achieved remarkable results in practical applications [105; 106; 107]. Fundamentally, it is the continuous innovation and progress of RS data sources that provide unprecedented high spatiotemporal resolution data support for DA research, driving the innovation and application leap of agricultural RS technology in the field of DA.
# Strong Ground Motion Sensor Network for Civil Protection Rapid Decision Support Systems
###### Abstract
[2] recapitulate the different types: economic (business interruption and productivity losses, damage of properties, critical infrastructure, lifelines and pipelines, public buildings, schools, hospitals; transport, energy, and communication networks), social (fatalities, casualties, people requiring short- or long-term post-hazard assistance, unequal hazard impact on different ethnic, demographic or regional populations, social distress, evacuation upheaval, homelessness and displacement, loss of public services), environmental (terrestrial, aquatic, protected areas), and other (political implications, loss of credibility of local authorities, safety, cultural heritage, universal monuments). Furthermore, a DSS for seismic risk reduction must incorporate risk-assessment methodology to monitor and evaluate the effectiveness of different risk-reduction measures undertaken, using decision indicators such as direct losses, indirect costs, risk-reduction implementation costs, possible indirect benefits from earthquake hazard, negative and/or positive side-effects. Earthquake hazard zonation for urban areas (seismic microzonation) is the primary step toward a seismic risk analysis and reduction strategy. Seismic Hazard Analysis (SHA) is a multidisciplinary modeling that aims to forecast earthquake occurrence and its resultant ground shaking [7]. The SHA aim to evaluate the ground intensities with a probabilistic (PSHA) or a deterministic (DSHA) approach. The first one relays on recurrence relation, like the Guttenberg-Richer law, while the second one focuses on the structures design based on Peak Ground Acceleration (PGA) and spectral acceleration values [8]. A modern approach, the neodeterministic (NDSHA) is a scenario-based procedure that takes earthquake source, seismic wave propagation path and local site effects into account, to supply realistic time histories of strong ground motions [9]. Well-instrumented urban areas are able to produce a large database of strong-motion recordings. These results can improve the empirical characterization of ground motion and help to better understand the seismicity patterns. We present the deployment of a StrongGround Motion Network (SGMN) in a metropolitan area (Chania, Crete Island, Greece) together with multiparametric analysis techniques of the data collected in recent indicative cases of earthquake occurrence. The dense SGMN is a permanent installation providing in near real-time hazard information for the ground motion that can automatically feed a DSS for seismic risk reduction and emergency response. Compared to the traditional microzonation techniques which usually rely on single-site measurement or temporal seismometer deployments, the SGMN creates continuously a detailed image of the ground intensity parameters for an area with difficult geological setting and helps to study the various effects of the seismic waves.
## 1 Introduction
Here, one of the two branches of the proposed network include an edge device-friendly Mobilenet-v2 backbone [20] with an efficient spatial asymmetric attention (SAM) [31] module to extract and refine malware features from high-resolution images. Our deep network's second branch (the auxiliary branch in the later sections) learns salient feature extraction on low-resolution malware images. Thus, it can handle the missing information of a malware input appearing by the compression  
Figure 1: Comparison between different classification methods for malware classification. The proposed method outperforms existing methods by a notable [3; 15; 16; 17; 18; 19; 20; 21; 22]. artifacts [32], attenuation, parsing errors, etc. We correlate the extracted features of both branches with multi-head attention (MHA) [33, 34] to achieve a higher detection accuracy. We compared our proposed method with the existing malware classification methods and studied our method on numerous hardware. Our proposed method outperforms the existing deep malware detection and image classification methods by a notable margin. In addition to that, we illustrate an optimization strategy for deploying our deep model in low-power edge devices to reveal the practicability of deep, efficient malware detection for securing future solutions, including securing IoT and 5G/6G infrastructures [35, 36]. To the best concern, this is the first work in the literature demonstrating the optimization and practicability of malware detection on real-edge hardware with sophisticated experiments. In a nutshell, the proposed network is hardware-independent and could be incorporated into any system based on the application requirement. The main contribution of this study is as follows:  
* We propose a novel deep network with attention mechanisms and an auxiliary branch to learn salient features from malware images. The proposed network also incorporates a Faster SAM (FSAM) with 83% lower trainable parameters than the well-known SAM. Apart from FSAM, we also propose to leverage MHA to correlate our feature branches for efficient malware classification. * We illustrate the practicability and optimization strategy for DAMN for real-world usage. We achieved a frame per second (FPS) of 545.29 on a real-edge device. Higher frame rates on low-power computing devices reveal countless possibilities for securing future IoT and network applications. * We densely study the existing methods from malware and image classification domains to summarize the performance of deep networks for malware classification. We outperform the existing works for the benchmark dataset in multiple evaluation metrics by a large margin. The rest of the paper is organized as follows. Section 2 reviews the related works, Section 3 details our proposed method, and Section 4 densely evaluates and summarizes the results. In addition to that, Section 5 illustrates the optimization and deployment scope of malware detection algorithm on edge devices. Finally, Section 6 concludes this work.
###### Abstract  
At 2 \(\upmu\)m wavelengths (149.9 THz), hollow-core photonics band gap fibers have higher light power damage thresholds, stable polarization states, and lower losses of 0.1 dB/km. Additionally, a thulium-doped fiber amplifier can provide a gain of \(>\)35 dB. Specifically, an indium-rich InGaAs photodetector shows a naturally higher photoresponsivity at 2 \(\upmu\)m wavelengths than the C-band. Therefore, using tunable photo-generated microwave technology at 2 \(\upmu\)m wavelengths could achieve higher photo-to-electric power conversion efficiencies for higher RF output power applications using the same method at the same frequency. Here, a double sideband with the carrier suppression modulation method was experimentally applied on 2 \(\upmu\)m wavelengths to generate tunable and stable microwave carriers. Comparison experiments were also applied on the 1.55 \(\upmu\)m (193.4 THz)/1.31 \(\upmu\)m wavelengths (228.8 THz) based on the same indium-rich InGaAs photodetector. Through normalization on the wavelength-corresponded squared external quantum efficiency to visualize the photo-to-electric power conversion efficiency at different wavelengths under the same input optical signal power, the ratio between the results at 2 \(\upmu\)m wavelengths and C/O-band is abstracted as 1.31/1.98, approaching theoretical estimations. This corresponds to a power conversion efficiency measurement of \(\sim\)1.16 dB/\(\sim\)2.98 dB. To our knowledge, this is the first study on 2 micron wavelengths that proves the corresponding high efficiency power conversion property.  
2 \(\upmu\)m wavelengths; photo-generated microwave carriers; double sideband with carrier suppression modulation
# A New Concept Compliant Platform with Spatial Mobility and Remote Actuation
## 3 Numerical Simulation of the Platform Pose  
The peculiar geometry illustrated in Figure 1 is the result of a preliminary study that led to the definition of the patented structure. Three equal legs were positioned in a parallel configuration, each leg being actuated by two cables, and so, the whole structure was a tendon-driven mechanism. The geometry of each leg was chosen in such a way that the lower and upper cables induced a raising and lowering motion, respectively. The attachment points were also selected in order not to induce cable jamming. Further optimization of the attachment points and of the angles of the pseudo-rigid links will be the object of future investigations. Considering the nomenclature introduced in Figure 1, any moving cable (i) acts on a CSFH turning pair, relevantly operates the bending of the CSFH Type (h) flexure, and actually drives the rotations of Type (g) links. Cables (f) will have the effect of pulling the platform down because they are attached to the upper links. More in general, a configuration of the platform (a) will be defined by the whole set of forces applied to all the wires, since it depends on the interaction between the applied forces and the elasticity of joints.  
Finite element analysis is a convenient way to assess the deformation of the whole structure for assigned values of the cable displacements. More specifically, FEA provided evidence that the adoption of the proposed platform can be a promising approach to find an efficient way for remote handling in several contexts.
## 2 Design Methodology
### Low-Profile Implementation  
The FSS structure was designed for the resonant frequencies of 2 GHz and 5.4 GHz with reduced inter-element spacing to achieve a low-profile design. Unfortunately, the unit cell simulation will not provide accurate results for practical 3 \(\times\) 3 or 4 \(\times\) 4 implementations, as explained below.  
Figure 7 compares the transmission coefficient for the same FSS in simulation and measurement. The simulation corresponds to the FSS design with unit cell (infinite) boundary conditions, under the influence of mutual coupling (named FSS\({}_{mc}\)), due to close resonant elements with an inter-element spacing of 6 mm. The measurement corresponds to its implementation in a 4 \(\times\) 4 array of the same resonator (named 4 \(\times\) 4 FSS\({}_{mc}\)). As observed, there is a shift in the first expected frequency band. The reason for this is that each unit cell is surrounded by equal ones at all sides in the simulation, whereas, in the practical implementation, that only happens for  
Figure 8 shows the impact of cell inter-element spacing on the transmission coefficient. As can be observed, the increase in inter-element spacing decreases mutual coupling impact until the first filtered band converges, regardless of the distance (no influence among cells). To illustrate the practical implication of this effect, a new 4 \(\times\) 4 array of the same resonator is now implemented and compared to the unit cell simulation in the convergence point, that is, with 30 mm inter-element spacing (see Figure 9).  
Figure 8: Simulated effect of increasing inter-element spacing (\(d\)) under Floquet mode boundary conditions.  
Figure 7: Floquet mode simulation vs. measure of the unspaced 4 \(\times\) 4 FSS design for antenna isolation.  
As expected, both simulation and measurement in their filtered bands agree. The target would be to match simulations and measurements, even if the mutual coupling between cells is present (shorter inter-element spacing, low-profile design). It could be argued that by scaling the design by a factor given by the ratio of the two resonant frequencies of the lower band in Figure 7 (simulated and measured), the new fabricated FSS would be brought back to the initial design frequency. Therefore, the problem would boil down to finding this ratio. In the following section, a method is proposed to extract this factor without measuring any prototypes.
## 3 The FAURAS Framework
### Bandwidth Allocation
#### 3.1.2 Problem When New Clients Join  
Every time existing clients stop or new clients start their streaming sessions, the module recalculates and assigns the new fair share \(C_{t^{\prime}}^{f}\) for all active clients. Figure 2 illustrates an example experiment of the two cases. In this experiment, one client is tested by streaming 200 segments from a video server via a proxy. The detailed settings will be presented later in Section 4. To simulate the start/stop of clients, we set the proxy to allocate different bandwidth at different periods for the client as described in Table 2.  
The client starts with the initial bandwidth of 1 Mbps at time \(t_{1}\). After downloading segment 60 th at time \(t_{2}\), the bandwidth increases to 3 Mbps. This simulates the case when existing clients finish their streaming sessions and leave the network. As the number of clients has decreased (\(X_{t_{2}}<X_{t_{1}}\)), the bandwidth assigned for each client is increased (\(C_{t_{2}}^{f}>C_{t_{1}}^{f}\)), which also increases the fair bitrate (\(R_{t_{2}}^{f}\geq R_{t_{1}}^{f}\)). Such a situation deals with no negative impact, but, instead, can increase the user's satisfaction.  
On the other hand, from segment 121 th at time \(t_{3}\), the bandwidth is shaped to only 1 Mbps, resembling that new clients have joined to the network (\(X_{t_{3}}>X_{t_{2}}\)). At this time, the current client will experience a drop of bandwidth (\(C_{t_{3}}^{f}<C_{t_{2}}^{f}\)), which is the main consideration in the development of HAS. As a result, it has to decrease its bitrate to match the new fair bitrate (\(R_{t_{3}}^{f}\leq R_{t_{2}}^{f}\)). Given the fact  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Segments** & 1–60 & 61–120 & 121–200 \\ \hline
**Bandwidth (Mbps)** & 1 & 3 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Bandwidth allocated to the client at specific periods in the streaming session.  
Figure 2: An example experiment of how a client reacts to the changes of fair share.  
that most ABRs often decrease the bitrate step-by-step to maintain the user's QoE [12; 37], the client usually needs \(\Delta t\) seconds to adapt to the new condition and the buffer is likely to degrade during the time. As a consequence, a large gap \(\Delta C\) between \(C_{t_{3}}^{f}\) and \(C_{t_{2}}^{f}\) will doubtlessly result in a large adaptation delay \(\Delta t\). Despite the fact that such a large \(\Delta t\) can help the client experience the high bitrate longer, it is likely to increase the risk of buffer underflow. This is illustrated clearly in Figure 2: the client's buffer depletes three times during this period. This has shown that, under such a situation, the Bandwidth Allocation module itself cannot efficiently help the client react in time to bandwidth degradation. To overcome this disadvantage, in the next subsection, a Request Overwrite module, which is the most important component of our system, is presented to assist clients shifting to the fair bitrate before draining out their buffers.