esa-sceva/satcom-chunk-collection huggingface dataset:
# Morphological Change in the Biceps Brachii Muscles during Shoulder Rotation: A Cadaver Study
## 1 Introduction  
The biceps brachii muscle is a powerful supinator, especially in rapid or resisted movements. It flexes the elbow joint, most effectively with the forearm supinated [1]. The biceps brachii is composed of a long and a short head. The long head of the biceps brachii (LHBB) originates from the supraglenoid tubercle of the scapula. In contrast, the short head of the biceps brachii (SHBB) originates from the tip of the coracoid process (CP) and inserts into the radial tuberosity and the fascia of the forearm [2]. The LHBB passes downward through the intertubercular sulcus (IS) between the two humeral tubercles [3,4]. Two tendons, which lie side-by-side, lead into elongated bellies and are separated within approximately 7 cm to the elbow joint [1].  
The role of the biceps brachii in shoulder joint rotation has been studied, mostly focusing on the function of the LHBB [5, 6, 7]. In a cadaver study, [PERSON] et al. [5] indicated that the loaded LHBB provided a stabilizing function to the humeral head in all directions and more importantly, in the anterior-posterior directions in the hanging arm position. Some clinical studies have also suggested that the LHBB acts as a stabilizer in cases of rotator cuff tears [8, 9, 10, 11]. Given these observations, the LHBB is thought to contribute to the stability of the shoulder joint. In contrast, little is known on how the SHBB affects the shoulder joint.  
Considering the attachments of the LHBB and SHBB, the biceps brachii muscle seems to change its shape passively during rotation of the shoulder joint. During external rotation (ER) of a normal shoulder joint, the intertubercular sulcus (IS) moves outward. Because the LHBB passes through the IS, it moves outward with ER of the shoulder joint; vice versa, upon internal rotation (IR), the IS and LHBB move inward. Since the rotation of the shoulder joint mainly occurs at the glenohumeral joint, the movement of the humerus is greater than that of the scapula. Hence, during rotation of the shoulder, the movement of the CP must be smaller than that of the IS. In other words, the LHBB and SHBB may passively move apart during ER and move closer during IR. Concomitantly, the angle of the confluence of the LHBB and SHBB may also increase or decrease, respectively. However, it is not clear how the LHBB and SHBB passively change their morphology during shoulder motion.  
In the present study, we aimed to investigate the passive morphological changes of the biceps brachii muscle during rotation of the shoulder joint, with a special focus on the angle between the LHBB and SHBB.
## 6 Numerical Simulations
### Dynamic Bearing Forces  
Figure 10 illustrates the variation of the bearing forces of some main components involving the carrier, sun and planet in the HPGT system with gear mesh time (denoted as F\({}_{\rm c}\), F\({}_{\rm s}\), and F\({}_{\rm p1}\), respectively). For the model with the planet-gear eccentric error excitation, as displayed in Figure 10a-c, it is observed that dynamic bearing forces for each component (carrier, sun and planet-gear) fluctuate prominently and periodically. The variation of the planet-gear dynamic bearing force is the most significant in each component of the system, and the variation of the sun gear bearing force is relatively smaller, owing to better flexible support of the sun gear. Figure 10d-f represent the dynamic bearing force from the model without manufacturing error excitations. A comparison of Figure 10d-f with Figure 10a-c shows the amplitudes of the dynamic bearing forces for each component in the presence of the planet eccentricity excitations are pronouncedly bigger than those in the absence of error excitations. In contrast to the results for the model in the absence of error excitations, the dynamic bearing forces of each component with the planet-gear eccentric error excitations have larger cyclical fluctuations; in particular, the variations of the planet-gear dynamic bearing forces are the most obvious in comparison with other components (carrier, sun gear), and the maximum amplitude of dynamic bearing forces of the planet-gear changes from \(4.5\times 10^{4}\) N without error excitation, as shown in Figure 10f, to \(7.8\times 10^{4}\) N with the planet-gear eccentric error excitation displayed in Figure 10c, that of the carrier from \(0.25\times 10^{4}\) N without errors shown in Figure 10d to \(1.8\times 10^{4}\) N with errors shown in Figure 10a, with more obvious changes, and that of the sun gear from \(0.1\times 10^{4}\) N without error excitation shown in Figure 10e to \(0.6\times 10^{4}\) N with error excitation shown in Figure 10b, also with more significant variations. By comparing Figure 10a-c with Figure 10d-f, it is noted that manufacturing error excitations such as the planet-gear eccentric error enhance the dynamic bearing force fluctuation for each component in HPGT system, and the dynamic bearing force fluctuation for each component shows apparent periodicity.
# Traffic Measurement and Congestion Detection Based on Real-Time Highway Video Data
## 3 Approach
### Core Components
#### 3.3.4 Tracking System  
The tracking system receives the detections from a single frame enriched with the corresponding predictions for every detected object. For the used tracker the aforementioned information is necessary to accomplish tracing of objects under restricted circumstances which may occur in a usage scenario of the system. OpenCV comes with a huge variety of different tracking algorithms and frameworks. These methods are well tested and promising, but are usually only applicable for video streams with 25 or more frames per second. Depending on the speed of objects certain tracking algorithms work well down to about 7 FPS where the scene does not lag that much. Without any additional data, simple tracking frameworks fail completely in cases with low frame rates because object positions can change significantly from one frame to the next, especially when a high amount of objects is involved.  
Based on these experiences solely raw video data will not achieve promising results for the mentioned issue. Additional information in the form of estimated object positions enable the tracking framework to compare just two object locations. Based on the assumption that the prediction model  
Figure 8: Exemplary prediction model visualization: The position of current detections (filled rectangles) are extended with data from the nearest driving paths (lines) to get future positions (framed rectangles). Screenshot taken from video ([[https://www.youtube.com/watch?v=nt3D26](https://www.youtube.com/watch?v=nt3D26) lrkho]([https://www.youtube.com/watch?v=nt3D26](https://www.youtube.com/watch?v=nt3D26) lrkho)).  
Figure 7: Schematic illustration of the prediction model setup. Detections (circles) from all the last frames are saved (**a**). The prediction model tries to associate these data points to get driving paths (lines) based on specific limitations (**b**). Source [27].  
delivers accurate results, the estimated position from the last frame and the actual position from the current frame will overlap. In that case a simple distance tracker is a perfect fit because it only needs these two locations for a single object. Figure 9 is an example of such a distance tracker: If the additional estimated positions for every object are precise enough, the frequency of frames per second has only little effect on the tracking algorithm.
## Processing Setup and Description
### Precise Orbit Determination  
Overall, there are three different approaches to the orbit determination of satellites in LEO: (1) kinematic; (2) dynamic; and (3) reduced-dynamic. For _kinematic_ POD [70] no force models are applied to the LEO, and the satellite positions are solely derived geometrically from the GNSS observations. The independence from dynamical models makes the kinematic approach attractive for many applications, e.g., gravity field determination [71]. However, kinematic orbit determination is sensitive to data problems in GNSS measurements, bad GNSS constellation (limited visibility), and data gaps [72]. Employing _dynamic_ modeling [73] can overcome the strong dependence on the quality of the GNSS observation. Gravitational and non-gravitational force models are introduced to constrain the estimate of the orbit and ensure a continuous orbit in periods of low visibility and data gaps. The quality of the resulting orbit solution is limited to the quality of the force modeling.  
The strong dependence on the force models may be reduced by a so-called _reduced-dynamic_ approach [74; 75; 76]. By applying additional empirical parameters [68] the satellite trajectory is adjusted to the observations and the residuals are minimized, which results in orbit solutions of highest quality. For the application of RO, reduced-dynamic approaches are favored over a kinematic approach because of the need of RO for the orbit to be consistent, continuous, and of high precision. These requirements ensure the most accurate interpolation of the position, velocity, and clock estimates to the occultation measurements of an average duration of 2 min with a 50 Hz sampling rate. The usage of empirical orbit parameters differs between POD software packages. NAPEOS follows a more dynamical approach with fewer empirical parameters applied compared to the setup of Bernese as used for this study [77].  
Introducing CODE and JPL GNSS orbits (15 min) and clocks (30 s sampling, see Section 3.1) the LEO orbit determination is performed using Bernese and NAPEOS. In general the processingsetup is similar to both software packages. Bernese employs a discrete kinematic approach for the initial code observations-based orbit and uses orbit integration to obtain a continuous a-priori orbit. In NAPEOS, however, initial values from the implemented Bancroft algorithm [78] serve as a basis for a dynamic orbit fit through the observations. Subsequently, iterative data screening (cycle slip and outlier detection) takes place before the final orbit integration. In Bernese the final orbit is established by means of empirical accelerations to the estimation process [79]. Models and parameters applied in the orbit processing are summarized in Table 1.  
For processing raw observation data with large inherent clock estimates, such as for GRACE (cf. Section 3.1), the aforementioned processing scheme of Bernese was extended introducing an additional a-priori orbit calculation and modified data screening options.
## 2 LiCSAR and LiCSBAS
### LiCSBAS Overview  
Here, we provide a brief LiCSBAS overview prior to the detailed description presented in Sections 2.3-2.5. The workflow of our InSAR time series processor is largely divided into two parts: preparation of a stack of unwrapped data (Step 0) and time series analysis (Step 1; Figure 1). LiCSBAS starts with downloading the LiCSAR products covering the area of interest (Step 0-1) and is followed by data format conversion (Step 0-2). Tropospheric noise correction using the external Generic Atmospheric Correction Online Service for InSAR (GACOS) data [38] (Step 0-3) and masking (Step 0-4)/clipping (Step 0-5) the unwrapped data are optional steps that can be carried out to improve accuracy and make processing more efficient. In the time series analysis, incorrectly unwrapped data, which degrade the results, are identified and discarded based on the coherence and coverage of the unwrapped data (Step 1-1) and by checking loop closure (Step 1-2). The refined stack of unwrapped data is inverted to obtain the displacement time series and velocity (Step 1-3), followed by estimation of the velocity standard deviation (STD) (Step 1-4) and masking of noisy pixels based on several noise indices (Step 1-5). Finally, a spatiotemporal filter is applied to the time series to mitigate the residual noise and derive the filtered time series and velocity (Step 1-6). All steps can be executed from the command line or using a batch script with defined parameters. The derived results (i.e., velocity and time series) can be easily displayed by an interactive time series viewer and exported to GeoTIFF, KMZ, or text format.  
LiCSBAS is written in Python3 and the Bourne Again Shell (bash) and operates without relying on any commercial software. The source codes are available on GitHub [31]. Although Steps 0-1 and 0-2 are dedicated specifically to manipulating LiCSAR products, interferograms from satellites other than Sentinel-1 and generated using a variety of software packages can be processed from Step 0-3, assuming the required metadata are available and files are converted into a compatible format (4-byte, single-precision floating-point format). Future LiCSBAS enhancements may include functions to prepare input data from InSAR archives other than LiCSAR (e.g., ARIA, etc.).
# A Geographically Weighted Random Forest Approach to Predict Corn Yield in the US Corn Belt
## 3 Results
### Model Performance with Different Sets of Input Features
#### 3.2.5 Soil Data  
Finally, we trained all six machine learning models to predict corn yield using soil data. Figure 9 shows the R\({}^{2}\) and RMSE of the models. The derived R\({}^{2}\) ranges from 0.146 to 0.501. The R\({}^{2}\) and RMSE of GWRFR are 0.501 and 1.715 MT/ha, respectively. GWRFR again outperforms other models such as MLR, PLSR, SVR, DTR, and RFR by 0.015-0.355 in terms of R\({}^{2}\). Again, MLR and PLSR have poor performance in terms of R\({}^{2}\) (0.146) and RMSE (2.245 MT/ha). GWRFR improves the yield prediction by 0.015 in terms of R\({}^{2}\) and by 0.0269 MT/ha in terms of RMSE when compared with its non-spatial version RFR (R\({}^{2}\) = 0.486 and RMSE = 1.742 MT/ha). The scatterplots of the observed and predicted yield values are also presented in Figure 9. As compared to previous feature sets, more under- and over-estimations can be observed in the scatterplots, and model accuracy decreases significantly. GWRFR can still perform consistently better than other models.  
Figure 8: The scatterplots of actual yield and predicted yield derived from (**a**) MLR, (**b**) PLSR, (**c**) SVR, (**d**) DTR, (**e**) RFR, and (**f**) GWRFR using only climate data.
# Attacks and Preventive Measures on Video Surveillance Systems: A Review
## 3 Security Measures for VSS  
The security of the hardware, firmware and network communications of video surveillance systems can be enhanced by following the guidelines summarized in this section. Preventing attacks against connected devices requires effort from both the industry and users. Vendors must adopt good practices for built-in security measures, such as secure remote access, basic encryption, and patching all known vulnerabilities [39; 40; 41; 42; 43; 44; 45; 46; 47]. Without proper safeguarding, IP-connected cameras are vulnerable to hacking, which can lead to the compromise of millions of security cameras and video recorders. The security mechanisms that can be used to prevent different types of attacks is tabulated in Table 3. To protect from security attacks, the security measures that are suitable at different layers (perception layer, network layer and application layer) are summarized in Figure 6.  
Figure 5: Pervasive attacks on VSS. Figure 6: Summarizing the security measures at different layers. The basic and necessary steps to avoid video surveillance camera attacks are as follows:  
1. Installation and regular updating of the anti-virus application is the foremost step to defend against malicious attacks. 2. Network topology and configuration of a system is critical in maintaining the security of IP-based cameras, as there are multiple entry gateways through which it can be attacked. Configuration access is the key to provide security to the system. A local network system with a firewall and virtual private network (VPN) software for remote access is always safe. A secure, encrypted connection is a barrier for any attackers before they can go through a firewall. An alternative solution could be to use a cloud-connected IP security camera. In this type of locally connected system, rather than relying on a password to gain access to the firewall of a camera system, cloud-connected IP security cameras will communicate with a secured cloud-based server over an encrypted connection. Users can gain access by linking up their devices with those servers. Cloud-connected devices have the added advantage of continuous monitoring over locally connected systems. 3. Using a unique, lengthy and unpredictable password for each camera is a good starting point. This is especially critical for a port forwarding system. However, if a system employs VPN, having a single secured password for all connected cameras is enough. As a rule, changing passwords every 90 days is recommended. 4. Priority should be given to provide cybersecurity training to all employees who may have access to a central video surveillance system and all personnel must be made aware of cyber security guidelines. 5. In case of security breaches, a cybersecurity incident response team can be constituted to respond to such emergencies. \begin{table}
# Enhancing Autonomous Vehicle Navigation with a Clothoid-Based Lateral Controller
###### Abstract  
This study introduces an advanced lateral control strategy for autonomous vehicles using a clothoid-based approach integrated with an adaptive lookahead mechanism. The primary focus is on enhancing lateral stability and path-tracking accuracy through the application of Euler spirals for smooth curvature transitions, thereby reducing passenger discomfort and the risk of vehicle rollover. An innovative aspect of our work is the adaptive adjustment of lookahead distance based on real-time vehicle dynamics and road geometry, which ensures optimal path following under varying conditions. A quasi-feedback control algorithm constructs optimal clothoids at each time step, generating the appropriate steering input. A lead filter compensates for the vehicle's lateral dynamics lag, improving control responsiveness and stability. The effectiveness of the proposed controller is validated through a comprehensive co-simulation using TruckSim(r) and Simulink(r), demonstrating significant improvements in lateral control performance across diverse driving scenarios. Future directions include scaling the controller for higher-speed applications and further optimization to minimize off-track errors, particularly for articulated vehicles.  
autonomous vehicle lateral control; clothoid-based navigation; adaptive lookahead strategy; Frechet distance
## 3 Methodology
### SDR Module  
In the development of the algorithm for the detection of drones through radio signals using artificial intelligence, it was necessary to consider some conditions that have influenced the development of the algorithm and the selection of the neural networks to be used.  
Firstly, the system needed to be a generalist to detect any UAV, regardless of the type, the communication and control protocols it uses, or the frequency bands it operates on. This is because the various UAV detection algorithms in the literature focus on detecting UAVs that use only specific protocols or detect only a single frequency band.  
Secondly, the system must be plug and play, meaning its installation should not require testing or prior training at the deployment site. This implies that the selected detection technique should not require an extensive and varied database to be created at the installation site, as would be necessary with supervised methods such as CNNs. These methods require collecting specific spectral data under different environmental conditions and with different UAV models.  
For these reasons, autoencoders were chosen. These constitute an unsupervised learning technique that is trained only with unlabeled spectrum samples when no UAVs are present. This means there is no need to manually generate a database at the deployment site, ensuring the system meets the plug-and-play condition. Moreover, autoencoders allow for the creation of a general algorithm, which will be explained later, that is capable of detecting all types of UAVs as required.  
The developed algorithm consists of two parts that run in parallel. The first part is responsible for receiving and processing the RF signals obtained with the SDR, and the second part analyzes the samples obtained to identify using the autoencoders whether there is an intruding UAV based on the radio signals.
## 2 Materials and Methods
### Molecular Docking
#### 2.1.2 Protein Structure Preparation  
The structure of P-glycoprotein (PDB: 7A6E) [30] was obtained from the Protein Data Bank. This structure was experimentally obtained by cryoelectron microscopy, with a resolution of 3.60 Angstroms (A). It was chosen as it is from _Homo sapiens_ and is coupled to tariquidar, a third-generation P-gp inhibitor. To prepare the structure of P-gp, we followed a careful workflow. Initially, the 7A6E.pdb file was edited using the YASARA software  
Figure 2: Generations of P-glycoprotein inhibitors and their respective mechanisms of action. The table categorizes the compounds by generation and lists their respective mechanisms of action, including competition for binding sites, the modulation of ATPase activity (stimulating or inhibiting), and the regulation of P-gp expression (up or down). References for each compound: Verapamil [11,12], Ciclosporin A [12,13], Vincristine [12], Respering [12], Quinidine [12], Tamoxifen [12], (R)-Verapamil [12], Dexnigulipine [12], Elacridar [12,14], Birciodar [12], Dofequidar [12], Trifluoroperazine [12], Valspodar [12], Tariquidar [12,24], Zosuquidar [14,24], Laniquidar [24,25], Ontogen [14,24], Mitotane [24,26], and Anamycin [24,27].  
to remove binding molecules (such as the inhibitor tariquidar, cholesterol molecules, and water) and unnecessary records (such as TER and CONNECT), resulting in a clean protein. Next, we used a common text editor to review this process, ensuring the complete removal of non-essential elements. After this step, the edited structure was subjected to energy minimization using the YAMBER3 force field in YASARA, ensuring the stable and adequate conformation of the protein for subsequent docking studies. Following these preparation steps, a three-dimensional grid box was created in the region corresponding to the protein's active site based on the original location of the tariquidar inhibitor in the experimental structure. The X, Y, and Z grid dimensions were 30 A \(\times\) 30 A \(\times\) 30 A, and the selected center of the grid box was positioned at coordinates X = 164, Y = 155, Z = 154 of the PDB structure used (PDB: 7A6E). In addition, a membrane environment was built using the YASARA script md_runmembrane.mcr, allowing a more accurate simulation of the protein's behavior in its native lipid bilayer context. The grid preparation steps were performed using the YASARA software, and the scripts used are also available in YASARA [29]. In the membrane model, we used phosphatidylethanolamine (PEA), a common lipid known for its stability. The Z coordinates of the atoms indicated a mean plane around \(Z\approx 0\). PEA molecules were evenly distributed across both leaflets, with 49.7% in the upper leaflet and 50.3% in the lower leaflet. All lipids were assumed to be 1-palmitoyl-2-oleoyl by default. It was recommended that the simulation be carried out at physiological pH (pH = 7.4). A 0.9% NaCl solution, which is representative of a normal physiological configuration, was used to define the ion concentration. A temperature of 298 K was used for the simulation.