esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The M_HERACLES Toolbox  
When addressing the 3D documentation of heritage, in many cases, the historical edifice of interest is located within a larger heritage complex or site. A thorough documentation may therefore incorporate this larger area, often at the scale of a neighborhood, into the mission. In this regard, a multi-scalar and multi-sensor approach is unavoidable; each sensor is usually adapted only for one object scale, e.g., close range photogrammetry for statues or phase-based TLS for building interiors. Indeed, smaller scale objects or larger areas do not need the same fine resolution as an artefact or architectural detail. To address this issue, we propose not only a thorough multi-scalar recording of heritage complexes, but also a progressive point cloud processing. The multi-scalar 3D data acquisition pipeline has been adequately explained in [46]. This section will address the multi-scalar point processing part, starting from the scale of a neighborhood up to that of architectural elements.  
As previously evoked, in this study, the multi-scalar approach is used to progressively segment the point cloud of a heritage complex--first into building units and then further into architectural elements (e.g., wooden frames and structural supports). The general flow of the approach can be seen in Figure 3. In addition to progressive segmentation, the developed method will also try to classify the results as automatically as possible in order to add the semantic dimension to the data, which is vital in BIM and 3D GIS environments. In this regard, a toolbox was created in the Matlab(r) environment to host all the codes and functions written for the study under one project: HERitAge by point CLoud procESsing for Matlab(r) (M_HERACLES). The aim of M_HERACLES is to develop simple algorithmsto help in the automation effort of point cloud processing in the context of cultural heritage. This includes among others segmentation, semantic annotation, and 3D primitive generation. The toolbox is open source and available online via GitHub (see Supplementary Materials for download link). M_HERACLES is developed in Matlab(r) R2018a using its Computer Vision Toolbox and several other third party libraries.  
The datasets were all processed using functions available in the M_HERACLES toolbox; the two main datasets (Kasepuhan and St-Pierre) were processed firstly on the scale level of a heritage complex to that of individual buildings (step 1 in Figure 3). The resulting sub-clouds were thereafter used to be further segmented on the building to architectural element scale level (step 2 in Figure 3). More specifically, Kasepuhan, St-Pierre, and the supporting dataset Valentino were tested for pillar detection (Section 4.2) in this step, while the HK-Castle dataset was used to test the beam detection function (Section 4.3) in M_HERACLES. All datasets were processed using an Intel(R) Xeon(R) E5645 2.4 GHz CPU.
## 3 Controller Design
Design Focus parameters were displayed in Table 1.  
By setting the phase margin to 80 degrees, the controllers were tailored to exhibit robust stability characteristics while also meeting the requirements for precise reference trajectory tracking. This balance between stability and performance is essential for ensuring effective control system operation in real-world applications. Furthermore, useful information of cross over frequency and phase margins of each individual controller is depicted in Table 2.  
For validating the results, the proposed mathematical model was implemented on the geared drives. The Parameters of Dual drives(which includes Drive and AntiDrive), primary and secondary gears used for simulation of the case in hand are being mentioned in Table 3 and Table 4 respectively. Figure 4 depicts the overall control system of the inverted pendulum. The pendulum is assumed to be rigid and has uniformly distributed mass. External disturbances (e.g., wind, vibrations) are ignored or assumed to be negligible. Each controller calculates the voltages for the drive and anti-drive, which are then used to apply forces to the pendulum in order to counteract any backlash. Each controller is responsible for stabilizing or controlling a specific aspect of the pendulum's motion in the desired mode. PID controller tuning methods aim to adjust the controller parameters to achieve the desired crossover frequency and phase margin, ensuring the stability and performance of the closed-loop control system. This system operates in a closed-loop mode, where feedback from sensors measuring the position of the pendulum is used to adjust the control voltages and maintain stability. The schematic diagrams of the control loops for the drive-anti-drive system and the single-drive system are shown in Figure 4 and Figure 5, respectively. The power ratio of the anti-drive to the drive is empirically calculated to be 3.4 for the case at hand. \begin{table}
\begin{tabular}{c c c c} \hline
**Controller Mode** & **Design Focus** & \(K_{p}^{p}\) & \(K_{l}^{p}\) & \(K_{d}^{p}\) \\ \hline
0 & Reference Tracking & \(-74.87\) & \(-34.67\) & \(-40.41\) \\
1 & Reference Tracking & \(5149\) & \(6388\) & \(1037\) \\
2 & Reference Tracking & \(-1680\) & \(-2510\) & \(-283\) \\ \hline \end{tabular}
\end{table} 
Table 1: PID controller parameters. \begin{table}
\begin{tabular}{c c c c} \hline
**Control** & **Value of \(p\)** & **Cross-Over Frequency (Hz)** & **Phase Margin** \\ \hline
1 & 0 & 8.36 & 60 \\
2 & 1 & 23.4867 & 75.082 \\
3 & 2 & 46.2814 & 70.706 \\ \hline \end{tabular}
\end{table} 
Table 2: PID controller information parameters. \begin{table}
\begin{tabular}{c c c c} \hline
**Motor** & \multicolumn{3}{c}{**Armature**} & **Inertia (\(J_{s}\), \(J_{p}\) (kg-m2))** \\ \cline{2-4}  & **Resistance (\(R_{a}\), \(R_{d}\) (Ohm))** & **Inductance (\(L_{a}\), \(L_{d}\) (H))** & **Inertia (\(J_{s}\), \(J_{p}\) (kg-m2))** \\ \hline Drive & 1 & 0.01 & \(6\times 10^{-6}\) \\ Anti-Drive & 4.7 & 0.01 & \(6\times 10^{-6}\) \\ \hline \end{tabular}
\end{table} 
Table 3: DC motor parameters.
# Automating Stimulation Frequency Selection for SSVEP-Based Brain-Computer Interfaces
## 2 Related Work  
### BCI Applications  
Nowadays, brain-computer interfaces are being applied in many fields and for many purposes. Many different paradigms are being used to design them (the SSVEP paradigm we have chosen is just one of them). It is difficult to fully cover these areas, and presented here is just a fraction of the research aimed at enhancing quality of life.  
An important direction in this field involves the use of BCIs to restore the function of the cerebral cortex when it is damaged. This is achieved through the introduction of biological feedback. Reference [34] showed--for the first time--that with the help of invasive BCIs, it is possible to restore movement to paralyzed limbs in people suffering from \"locked-in syndrome\" (LIS). This condition is characterized by a complete loss of speech and paralysis while maintaining consciousness and sensitivity.  
BCIs are also used for cursor control, text input, and forming commands for robots. An interesting example is presented in reference [35], where people with spinal cord injuries were able to modulate neural activity associated with the intention to move, even 3 years after the injury.  
In recent studies, BCIs were created that could automatically recognize speech from neural activities recorded using EEGs, as well as reconstruct whole sentences from thoughts with a limited vocabulary [36]. Another line of research demonstrates the possibility of controlling a patient's prosthetics or exoskeletons based on neural activity recorded using EEGs [37]. This opens up new perspectives for reproducing fine motor skills with prosthetics in the future.  
A recent study introduced an unsupervised data-driven pipeline for rejecting blink and muscle artifacts in EEG time series for use in motor imagery (MI)-based BCIs integrated with the Internet of Medical Things (IoMT) [38]. Using this approach reduces processing times, resource demands, and reliance on human intervention, making it a promising avenue for crafting efficient, user-friendly real-time BCI systems. Moreover, the technique proposed by the authors to enhance TL-CNN classification between Mex and MI fingerprinting actions is superior to other state-of-the-art methods, which makes it promising for further development and implementation in practice.
###### Abstract  
Preoperative determination of implant size for total knee arthroplasty surgery has numerous clinical and logistical benefits. Currently, surgeons use X-ray-based templating to estimate implant size, but this method has low accuracy. Our study aims to improve accuracy by developing a machine learning approach that predicts the required implant size based on a 3D femoral bone mesh, the key factor in determining the correct implant size. A linear regression framework imposing group sparsity on the 3D bone mesh vertex coordinates was proposed based on a dataset of 446 MRI scans. The group sparse regression method was further regularized based on the connectivity of the bone mesh to enforce neighbouring vertices to have similar importance to the model. Our hypergraph regularized group lasso had an accuracy of 70.1% in predicting femoral implant size while the initial implant size prediction provided by the instrumentation manufacturer to the surgeon has an accuracy of 23.1%. Furthermore, our method was capable of predicting the implant size up to one size smaller or larger with an accuracy of 99.1%, thereby surpassing other state-of-the-art methods. The hypergraph regularized group lasso was able to obtain a significantly higher accuracy compared to the implant size prediction provided by the instrumentation manufacturer.  
total knee arthroplasty; templating; machine learning; group lasso +
Footnote †: journal: Medical Imaging Research Center, UZ Leuven, 3000 Leuven, Belgium
# An Efficient Single-Anchor Localization Method Using Ultra-Wide Bandwidth Systems
## 3 Single-Anchor Localization Method  
This section describes the SAL method. We first introduce a variation of the conventional two-way ranging algorithm and then detail the multi-agent (quasi-)simultaneous response method that enables the SAL method.
# Tensor-Based Approaches for Nonlinear and Multilinear Systems Modeling and Identification
## 5 Tensor-Based Approaches for Nonlinear Systems
### Block-Oriented Models
#### 5.5.1 Hammerstein Model  
It is constituted with a nonlinear functional block followed by a FIR linear one \(g(.)\), with memory \(M_{g}\). In control applications, as illustrated on Figure 7, the Hammerstein model is used for representing control systems with nonlinearities in the actuator.  
The output \(y(t)\) of the Hammerstein model is given by  
\[y(t)= \sum\limits_{i=1}^{M_{g}}g_{i}v(t-i) \tag{49}\] \[= \sum\limits_{i=1}^{M_{g}}g_{i}\left(\,\sum\limits_{p=0}^{p}c_{p}u ^{p}(t-i)\right)\] (50) \[= \sum\limits_{p=0}^{p}c_{p}\sum\limits_{i=1}^{M_{g}}g_{i}u^{p}(t-i). \tag{51}\]  
Figure 6: Realization of a third-order Volterra–GOB model.  
Figure 7: Block diagram of a Hammerstein model.  
This model is therefore equivalent to a Volterra model of order \(P\), with the following \(p\)th-order kernel  
\[h^{(p)}_{i,i_{2},\cdots,i_{p}}=c_{p}g_{i}\delta_{i_{1}i_{2},\cdots,i_{p}}\,\ i\in \langle M_{g}\rangle, \tag{52}\]  
where \(\delta_{i_{1}i_{2},\cdots,i_{p}}\) is the generalized Kronecker delta. The corresponding tensor is diagonal and given by [49]  
\[\mathcal{H}^{(p)}=c_{p}\mathcal{G}^{(p)}, \tag{53}\]  
where \(\mathcal{G}^{(p)}\in\mathbb{R}^{M_{g}\times\cdots\times M_{g}}\) is a diagonal tensor whose diagonal elements are the components of the FIR coefficients vector \(\mathbf{g}=[g_{1},\cdots,g_{M_{g}}]^{T}\).
## 2 Bidirectional FSI Numerical Model
### Experiments and Validation  
#### 2.4.1 Experimental Setup  
An indoor simulated drilling experiment platform was developed based on similarity theory. The experimental system comprises a drive module, drill string module, borehole-bottom module, data acquisition and processing module, and drilling fluid circulation pipelines, as illustrated in Figure 8a. Considering the favorable physical property similarity between acrylonitrile-butadiene-styrene (ABS) and actual drill strings, an ABS pipe with an inner diameter of 26 mm and an outer diameter of 32 mm was selected as the simulated drill pipe. Transparent acrylic glass with an inner diameter of 35 mm was employed for the borehole wall to facilitate observation and machining. The dimensional correspondence between the simulated drill string, wellbore wall, and actual parameters is presented in the Table 1. The total length of the simulated drill string is 5650 mm. This platform enables adjustable control of key parameters including feed pressure, rotational speed, and drilling fluid flow rate. As shown in Figure 8b, the data acquisition system integrates multiple sensors for real-time measurement. Acquired signals are converted and transmitted to a computer via the RS485 communication protocol, with independently developed MATLAB-based software (R2025a (v25.1)) performing integrated analysis.  
To eliminate the additional frictional torque consumption caused by the friction of dynamic sealing components at both ends of the drill string, a no-load baseline experiment was conducted (feed pressure 0 N, flow rate 0 L/min, rotational speed 256 rpm). The driving torque was measured to be 0.46 N-m at this time. This value was added as an initial boundary condition to the simulated kinematic pair. Simultaneously, the friction coefficients measured in the laboratory between the drill string and the wellbore wall, and between the drill bit and the bottom hole, were 0.3621 and 0.3529, respectively. These were used to calibrate the simulation model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**Drill String**} & \multicolumn{2}{c}{**Wellbore Wall**} \\ \cline{2-5}  & **Actual Value** & **Simulated Value** & **Actual Value** & **Simulated Value** \\ \hline Young’s modulus (GPa) & 205 & 2.2 & 55 & 2.6 \\ Poisson’s ratio & 0.29 & 0.394 & 0.26 & 0.35 \\ Density (kg/m\({}^{3}\)) & 7850 & 1100 & 2600 & 1180 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dimensional correspondence between the simulated drill string and wellbore wall and actual parameters.  
Figure 7: Flow chart of fluid–structure interaction analysis.
# KubeHound: Detecting Microservices' Security Smells in Kubernetes Deployments
## 4 Detecting Security Smells in Kubernetes-Based Microservices
### Own Crypto Code  
We consider two different instances of the _Own Crypto Code_ smell, for which we hereafter propose two different detection techniques. The detection techniques are distinguished based on whether they search for the occurrence of cryptographic primitives ([[https://csrc.nist.gov/glossary/term/cryptographic_primitive](https://csrc.nist.gov/glossary/term/cryptographic_primitive)]([https://csrc.nist.gov/glossary/term/cryptographic_primitive](https://csrc.nist.gov/glossary/term/cryptographic_primitive)), accessed on 26 June 2023) or suspicious cryptographic names.  
**Usage of Cryptographic Primitives**. This technique enables the detection an instance of the _Own Crypto Code_ smell by statically analysing the microservice's source code to detect the use of cryptographic primitives, e.g., AES or RSA. The usage of such primitives could be a symptom of a custom cryptography solution being re-implemented, instead of reusing established and trusted high-level solutions. One such detection of primitive cryptographic function calls can be enacted using the tool Sonarqube [20], which tags as a \"hotspot\" any invocation of cryptographic primitives. This is not to be confused with the usage of weak and outdated cryptographic systems (e.g., the DES cryptosystem), which is considered by Sonarqube a security vulnerability.  
**Suspicious Cryptographic Names**. This technique enables the detection of an instance of the _Own Crypto Code_ smell by statically analysing the microservice's source code to detect suspicious cryptographic names used therein, e.g., RSA, IV, or AES, which could suggest some customised cryptographic implementation. This of course does not mean that the code performs custom cryptography computation, but it at least informs the developers that the naming of the function could be misleading.
# Ground-Based Cloud Image Segmentation Method Based on Improved U-Net
###### Abstract  
Cloud image segmentation is a technique that divides images captured by meteorological satellites or ground-based observations into different regions or categories. By extracting the distribution, shape, and dynamic features of clouds, it provides precise data support for the meteorological and environmental fields, significantly influencing photovoltaic (PV) power generation forecasting, astronomical telescope observatory site selection, and weather forecasting. A ground-based cloud image segmentation model based on an improved U-Net is proposed, which adopts an overall encoder-decoder structure. In the encoder phase, this paper constructs a dilated convolution-atrous spatial pyramid pooling (ASPP)-dilated convolution structure to enhance early cloud feature extraction. Dilated convolution is a novel type of convolution that expands the receptive field by inserting holes into standard convolution, thereby capturing a larger range of contextual information. ASPP maintains high resolution while paying attention to both local details and global structures of the image. In the decoder stage, the bicubic interpolation method is used for up-sampling to restore the feature map resolution and improve the clarity of the segmented image. The bicubic interpolation method refers to the use of cubic polynomial functions to interpolate the pixel values of the input image. In addition, this paper designs a novel skip connection layer structure between the encoder and decoder, composed of a depthwise separable path (DS path) and an improved channel spatial attention module (Im-CSAM) connected in sequence. The DS path combines depthwise separable convolutions and residual structures to facilitate information exchange between high-level and low-level features. The Im-CSAM is a modular attention mechanism that focuses on important cloud features in spatial and channel dimensions to enhance segmentation accuracy. Experiments show that compared to the traditional U-Net, the accuracy, precision, and MIoU of this model improved by 2.2%, 4.1%, and 5.0%, respectively, in the SWINySEG dataset, and by 3.2%, 3.6%, and 5.8%, respectively, in the TCDD dataset, proving that the improved method has a better generalization ability and segmentation performance.  
photovoltaic; ground-based cloud image; semantic segmentation; improved U-Net; improved channel spatial attention module +
Footnote †: journal: applied sciences
## 5 Materials and Methods
### EFR32 BG22 WuR Command Line Example  
#### 5.3.1 Principle  
The 2.4 GHz carrier used for WuR is susceptible to interference. The established protocol considers this issue and provides two alternatives:  
* Legacy mode The microcontroller uses a time-based filter to identify WuR packets. The system can be configured to monitor a selected frequency band for a specific duration, measured in microseconds. Choosing a longer sensing duration is recommended when operating under noisy conditions. To enable wake-up functionality, we used a Silicon Labs RAIL API [73]. The RAIL_StartRfsense function is responsible for this feature, and the receiving node must call it before entering EM2 or EM4 to be woken up. After sensing on the RF for the designated period, it transitions to EM0. Notably, calling RAIL_StartRfsense again is required to reuse the WuR.
* Selective mode This approach enhances the protocol's security through the use of a sync word, a 1 to 4 bytes code that must match between the sending and receiving nodes. This feature effectively thwarts accidental wake ups, although it does not combat sniffing. To use this mode, the setup differs on both ends. An example provided by Silicon Labs, illustrated in Figure 10, was used.
# Title-to-Length Coupling Analysis of an Off-Axis Optical Bench Design for NGGM
## 2 Off-Axis Optical Bench Design
The position of lens L4 is first adjusted along the optical axis to compensate for the offset between the back focus of lens L4 and the equivalent back focus of lens L1, while the position of lens L5 is adjusted for the same purpose. Lens L3 is then adjusted to compensate for the offset between the back focus of lens L3 and the equivalent back focus of lens L2. The QPDs are fine tuned along the optical axis in order to reduce the beamwalk of the LO beam on the active areas of the photodiodes. As the last step, the receiving aperture is tuned until an optimized TTL coupling is derived, both in yaw and pitch degrees of freedom [9]. For the off-axis layout, offset exists between the reference point (RP) of the optical bench and the CoM of the satellite, which will result in a variation of OPD when coupled with the attitude jitter of the satellite. The offset may be minimised by an open loop correction mechanism. For the KBR of the GRACE or GRACE Follow-on mission, the manoeuvre is by now quite standard and is usually performed every three to six months. An accuracy of micrometers may be achieved [13]. However, when we go further up in the LRI ranging sensitivity, the limit of sensitivity for this manoeuvre is not known for the time being. This limit is not determined by the LRI ranging sensitivity alone; star sensor and the rotational degrees of freedom of the accelerometer as well as stability of the satellite platform will also play a role. After a spacecraft manoeuvre to calibrate the reference point relative to the CoM of the satellite [13], an open loop correction mechanism is activated to correct the offset on orbit. One possible design of this open loop control is to employ two FSMs to control the position of the RP point, as depicted in Figure 3. It is likely that an alternative design of this open loop control is feasible. This will be further studied in future work. Figure 2: The schematic drawing of the off-axis optical bench design, output by Optocad [12]. The RX beam is in red, while the local beam and TX beam are shown in blue. The triangle markers give the position of the beam waist, the green marker is the tangential waist indicator, and the magenta marker is the sagittal waist indicators.