esa-sceva/satcom-chunk-collection huggingface dataset:
# Physical and Respiratory Rehabilitation in Spinal Muscular Atrophy: A Critical Narrative Review
## 3 Results
### Motor Rehabilitation  
#### 3.1.1 Hydrotherapy  
In three of the selected studies, hydrotherapy (alone or in combination with physiology) was applied to improve trunk and limb motor functions and ambulatory ability.  
[PERSON] et al. assessed the effects of two different training interventions on lung and motor functions, as well as the quality of life in a person with type I SMA [45]. The aerobic training, supervised by a physiotherapist, involved using an ergometer three times a week for twelve weeks, with exercise intensity set at 60-70% of the participant's maximum heart rate measured with a pulse oximeter. Following a six-week washout, hydrotherapy was performed twice a week for twelve weeks. The hydrotherapy sessions included 5-10 min of warm-up and stretching exercises, followed by 40 min of the Halliwick method in a heated pool at 30-32 degrees Celsius, and finally, 5-10 min of cooling-down exercises. The results suggested that if protracted over time, this combined approach could improve trunk and limb movements, motor function (HFMS and GMFM), lung function, quality of life and patient autonomy (PedsQL 3.0). [PERSON] et al. conducted a multiple case study to explore the effects of adding hydrotherapy for individuals with type II and III SMA [47]. Fifty participants (thirty with type II SMA and twenty with type III) performed individual physiotherapy once a week for two years, and at the same time, hydrotherapy (Hallwick method) two times a week. Physiotherapy consisted of kinesotherapy, stretching, and respiratory exercises. The degrees of deformity in the hips, knees, and feet increased in all patients, as well as scoliosis. A total of 93% of type II patients and 100% of type III patients showed improvement in activities of daily living (Barthel Ladder); type II patients improved their motor activities except for ambulation. Type III patients improved all motor activities, including ambulation.  
[PERSON] et al. proposed a hydrotherapeutic programme focused on functional movements in a child with type III SMA to monitor the changes in motor functions and gait's spatiotemporal characteristics [59]. Every session consisted of a warm-up period, in-water exercises, and finally, a cooling period. Both warming and cooling periods were performed in water and included low-intensity aerobic exercises (respiratory exercises, flexibility, and walking activities). Activities were adjusted based on the child's capabilities and to allow practice in the form of play. There was an improvement in the muscle strength of the lower limbs and movements during the swing phase. The GMFM improved by 11%. The gross motor quotient for PDMS-2 improved from 66 to 74. Additionally, spatial and temporal gait measures (walking speed, stride length, etc.) showed improvement.
## 4 Case Study
### Decision Problem
* _Vehicle Routing and Scheduling_ (VRS), a structured decision, which consists in minimizing the sum of fixed and variable vehicle cost, for a given set of potential parking slots [31]. This structured component is not influenced by decision maker preferences and focuses solely on achieving the best operational efficiency. The problem is inherently multicriteria, with objectives that include the following: \(z_{1}\propto k\), representing the occupation of public space; \(z_{2}\), the total cost; and \(z_{3}\), an environmental objective aimed at minimizing CO\({}_{2}\) and other pollutant emissions. The qualitative measures considered by decision makers in the PSS stage can help ensure that the chosen locations for parking slots optimize the logistical aspects of last-mile delivery while also accommodating the preferences of the stakeholders (the company's management, delivery drivers, recipients, and local communities) affected by the company's operations. Preferences may include the following:* Ease of parking for delivery vehicles (e.g., avoid parking on sloping streets);
* Proximity to main roads and motorways (e.g., minimize the number of intersections to be crossed);
* Traffic congestion considerations;
* Feedback from delivery drivers and personnel;
* Ease of unloading parcels and ADRs from vehicles;
* Condition of the parking area (e.g., paved or well-maintained);
* Safety of the location for drivers;
* Security of parked vehicles against theft or damage;
* Compatibility with different vehicle sizes and types;
* Flexibility to adapt to changes in delivery routes;
* Significant opposition from residents due to the excessive concentration of parking slots dedicated to last-mile deliveries. It is worth remembering that these aspects are part of the tacit knowledge of the decision makers, and cannot be easily formalized in performance measures and constraints. Another relevant aspect is that the preferences of certain stakeholders, including company managers, representatives of drivers' unions, and local communities, may be conveyed directly to the decision support system in person. Others (such as the preferences of individual drivers, citizens, and recipients) may be reflected in formal complaint reports, social media posts, or local newspaper articles, and inputted in the system on behalf of the authors.
# Fuzzy-Based Quality Adaptation Algorithm for Improving QoE from MPEG-DASH Video
## 6 Simulation Results
### Dataset 1

Table 5: Performance of algorithms at a buffer size of 30 s and a segment duration of 4 s.  
video rate switches. The DBT algorithm was able to minimize the switching ratio, but it also had the lowest video rate among the competing clients. The DASH-Google algorithm achieved a high video rate, but also experienced the most video rate switches.
# ScriptBlock Smuggling: Uncovering Stealthy Evasion Techniques in PowerShell and.NET Environments
## 3 ScriptBlock Smuggling  
ScriptBlock Smuggling is a novel evasion technique that leverages the manipulability of PowerShell's ScriptBlock attributes, particularly the Extent and EndBlock. This manipulation can lead to discrepancies between the logged representation of the ScriptBlock and the actual code being executed, thereby allowing attackers to evade detection and bypass security measures.  
The Extent attribute contains crucial information about the ScriptBlock, such as the file from which the code originated, the start and end positions of the code, and the text of the original code. This attribute plays a critical role in understanding the context in which the ScriptBlock was created and tracking any changes made to the code. In contrast, the EndBlock attribute contains the executable code of a typical ScriptBlock, and it is the primary focus of any analysis or execution of the ScriptBlock. ScriptBlock Smuggling manipulates the Extent and EndBlock ASTs such that they do not match, so different sets of ASTs are used for malware detection and compilation. Figure 5 shows that benign PowerShell code can be loaded into the Extent, which is then sent to _Automation.AmsiUtils_ for scanning, while the malicious code is loaded into the EndBlock and is sent directly to the CLR for compilation and execution. The Extent and EndBlock attributes of a ScriptBlock do not necessarily need to match, and these properties can be manually set.  
Figure 4: Anatomical diagram of a PowerShell ScriptBlock structural components.  
When a PowerShell command is executed and analyzed, the ScriptBlock log contains the ScriptBlock Extent, while the machine executes the EndBlock. Creating an unmatched ScriptBlock is significant because this discrepancy allows for two distinct sets of commands to be sent--one for execution and another for logging--resulting in a potentially malicious script being smuggled to the compiler without being passed to the AMSI.
# Road to Efficiency: V2V Enabled Intelligent Transportation System
## 4 V2V Communication Architectures
The categorized architectures are as follows: Decentralized mesh network, V2V-based cloud integrated hub, Edge computing-based V2V, Blockchain-enabled V2V network, V2V over hybrid cellular and ad-hoc networks, Artificial intelligence-assisted dynamic networks for V2V communication, and V2V assistance toward sustainable transport systems. These V2V architectures are different, but at the same time, they provide variety and supplement each other in that they are endowed with unique functions that tackle various areas of vehicle communication and transport management. Thus, critical review and integration of these architectures shall provide reliable and effective V2V systems for safety, efficiency, and environmental gains in practical applications.
# A Method for Detecting the Yarm Roll's Margin Based on VGG-UNet
## 5 Conclusions  
This study combines deep learning with traditional image processing methods. Through comparative experiments, the VGG-UNet model was selected for segmenting the margin of yarn rolls. By replacing the encoder part of the U-Net with the first 13 convolutional layers of VGG16 and applying transfer learning by loading pre-trained weights, the original VGG-UNet model, which initially had average performance metrics, was enhanced by replacing the UNet's encoder with the first 13 convolutional layers of VGG16 and applying transfer learning with pre-trained weights. After introducing pre-trained weights, the model showed significant improvement in overall performance and generalization ability. The precision, recall, and mean IoU of the segmentation were superior to other models. After obtaining the segmentation results, traditional image processing methods were used to extract contour edge points, which were then fitted using the least squares method. The measurement results were then calculated based on the relationship between pixel dimensions and actual dimensions. A comparison between automatically and manually measured values revealed that the error in detecting the yarn roll's margin using the proposed algorithm is less than 3 mm, with a relative error within 4%, demonstrating practical utility. This research method ensures high accuracy while maintaining detection efficiency.  
Based on the initial experimentation, it has been identified that there are areas for improvement in the proposed method. In future work, we plan to further research the following aspects:  
1. Enhancing the model's generalization ability: Research methods to ensure detection effectiveness in more complex environments and improve the model's generalization capabilities.
2. Optimizing the model's inference speed: The semantic segmentation network is significantly slower than general detection networks. Our model's current inference speed is 18.19 FPS, which temporarily meets industrial requirements. In the future, we will consider pruning the model and optimizing the algorithm to improve inference speed.  
Conceptualization, J.W. and X.Z.; methodology, J.W. and X.Z.; validation, J.W. and X.Z.; formal analysis, H.W.; investigation, X.Z. and L.P.; resources, J.W. and L.P.; data curation, X.Z. and H.W.; writing--original draft preparation, X.Z.; writing--review and editing, J.W. and L.P.; supervision, [PERSON]; project administration; funding acquisition, H.W. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
## 2 Experimental Details
### Configuration of the Orbital-Rail-Type Digital Radiography Inspection Device  
An inspection device that can automatically perform DWSI inspection using orbital rotation was developed based on the experimental results. This device consists of three parts: the mechanical part, the detector part, and the controller part (for powering and operating the device).  
The mechanical part involves configuring the rails for easy installation and secure attachment to the pipes. The rails are adjusted to the pipe diameter and the fixing method minimizes the need for wrenches or spanners in the field.  
Next, the optimal angle was set using a tungsten collimator to ensure accurate imaging of the radiation source in the digital detector array's active area. The collimator directs the radiation source during inspection and blocks it elsewhere for safety. Details on the angle and design of the tungsten collimator are in Section 3.  
The tungsten collimator, which is connected to the radiation source, is attached to a 10 m long source guide tube to prevent kinking and deviation during rotational inspection. The angle was determined to allow one collimator to cover multiple pipe diameters.  
The inspection device was designed to be flat to fit the narrow spaces (100-150 mm wide) between installed pipes. The detector part includes a 10 mm thick solid lead-plate shield to prevent radiation leakage. The lead-plate shield covers the entire bendable DDA area and is designed as a slot type for easy attachment and detachment in radiation-permitted areas.  
The controller, which operates the mechanical part, is configured to use a smart pad with wireless communication available in the DDA. The system determines the number of  
Figure 1: Differences in radiographic examination techniques for pipe welds. (a) Double-wall single image (DWSI). (b) Double-wall double image (DWDI).  
inspections needed based on the pipe's thickness and diameter, memorizes each section's location after inspection, and moves to that location for re-inspection.  
Inspection results are sent in real time to a desktop or mobile device via wireless communication from the controller.  
Figure 2 shows the device configuration and Figure 3 presents the inspection flowchart.
# Privacy-Preserved Image Protection Supporting Different Access Rights
## 2 Related Works
### RSA Public-Key Cryptography
#### 2.1.3 Digital Signature  
In order to verify the origin of messages, RSA can also be used for digital signatures. Assume that the user \(U_{1}\) wants to send a signed message \(S\) to the user \(U_{2}\). \(U_{1}\) uses \(U_{1}\)'s private key \(d_{1}\) to sign the message \(m\) and generates a digital signature \(S=m^{d_{1}}\ \mathrm{mod}\ n_{1}\). When \(U_{2}\) receives the signed message \(S\) and \(m\), \(U_{2}\) can use \(U_{1}\)'s public key \((e_{1},\ n_{1})\) to authenticate whether \(S\) is generated by \(U_{1}\) or not. \(U_{2}\) computes \(m^{\prime}=S^{e_{1}}\ \mathrm{mod}\ n_{1}\) and compares \(m\) with \(m^{\prime}\). If \(m\) and \(m^{\prime}\) are the same, \(U_{2}\) knows that \(S\) is the digital signature of \(m\) signed by \(U_{1}\) and trusts that the message has not been tampered with since being sent.
## 1 Introduction
[16]; decision trees (DTs) [17; 18; 19] and their extension named random forest (RF) [20]; and artificial neural networks (ANNs), first introduced by [21]. An example involving KNN and SVM can be found in Ref. [22], where these techniques were implemented to validate the classification of the driver behavior regarding safe/unsafe stopping at junctions when the yellow light is on. In Ref. [23], the authors proposed an explicit DT approach for automated driving on a two-lane highway. [PERSON] et al. [24] presented vehicle control algorithms in order to simulate a racing car control adopting imitation learning, using DTs and RFs. The use of ANNs in the context of autonomous driving has been considered by [PERSON] et al. [25], where a self-driving car using ANNs and computer vision is able to detect lanes, traffic light, and avoid frontal collision. The authors of Ref. [3] focused on pedestrian and vehicle detection in autonomous cars. In Ref. [26], the authors presented an overview of machine learning/deep learning algorithms in the context of autonomous driving architectures. More recently [27], another comprehensive survey of ML techniques applied in the context of autonomous driving was presented. Applications of ML methods for classification, recognition, or tracking of objects, living beings or phenomena in other contexts are described in recent reviews [1; 2; 4; 13; 28]. Combining the polarimetric imaging with ML algorithms becomes a popular approach [7; 8; 9; 29; 30; 31; 32; 33], often applied in but not limited to the context of autonomous driving. In an earlier work by our group [9], it was described how support vector machines and artificial neural networks are used for object classification in the context of autonomous driving. The data on the urban objects to classify are obtained by means of the Stokes-Mueller polarimetry, wherein the MMEs are evaluated. However, obtaining all MMEs may be technologically difficult and/or too expensive for practical use. In particular, it may be problematic to implement the part related to circular polarizations in a portable polarimeter. Therefore, here we analyze how the success of classification varies with the amount of input information (number of considered MMEs), for classifiers based on different ML algorithms. We propose an ANN-KNN classifier, which can be a compromise between the classification accuracy and cost. The remainder of this article is organized as follows. In Section 2, we provide some details on the data from the polarimetry setup and the features we use to create a robust classifier. We also analyze the data in order to identify the attributes that are essential for classifying the objects correctly, even though redundancy may help to achieve better results. Section 3 is dedicated to the ML architectures and their deployment within the object identification context. Here we compare classification results obtained with different sets of attributes (minimal, intermediate, and full) and propose the ANN-KNN classifier. Finally, Section 4 presents a brief summary, conclusions, and outlook.
# SDR-Based 28 GHz mmWave Channel Modeling of Railway Marshaling Yard
## 4 Results
### Relationship between Parameters and Position
#### 4.1.3 Relationship between Received Power and Position  
The 95% statistical received power value of all effective paths in each position are calculated and are shown in Figure 20.  
The 95% statistical power values are summed by position, and we get Figure 21.  
The nonlinear curve fitting method is used to fit the curve in Figure 21; the fitting curve is the red line in the figure, and the fitting equation is:  
\[y=b\times\ln(x-a) \tag{9}\]  
where \(a=2.45198\pm 3.83339\) and \(b=-7.2857\pm 0.15875\). There is deep fading at the position of 152.2 m as well as 202.4 m, which will be further analyzed in the section on the impact of typical objects.
# Modification and Optimization of Cycloidal Gear Tooth Profile Based on Machining Error Compensation
## 2 Theoretical Analysis of Machining Error
### Machining Error Compensation
\begin{table}
\begin{tabular}{c c} \hline
**Error** & **Coefficient of Variation** \\ \hline The grinding wheel radius error \(\xi_{1}\) & \(k_{1x}=(K_{1}\cos\beta-\cos\alpha)S^{-0.5}\) \\  & \(k_{1y}=(-K_{1}\sin\beta-\sin\alpha)S^{-0.5}\) \\ The distance error from the grinding wheel center to the spindle center \(\xi_{2}\) & \(k_{2x}=\cos\alpha-K_{2}\lambda S^{-0.5}\cos\beta-K_{2}\lambda T_{2}S^{-1.5}( \cos\alpha-K_{2}\cos\beta)\) \\ The eccentricity error of the machine tool spindle \(\xi_{3}\) & \(k_{3x}=z_{p}\lambda S^{-1.5}T_{3}\cos\alpha-1+z_{p}\lambda S^{-0.5}(1-K_{3}S^{- 1}T_{3}\cos\beta)\) \\  & \(k_{3y}=z_{p}\lambda S^{-1.5}T_{3}\sin\alpha+1-z_{p}\lambda S^{-0.5}(1-K_{3}S^{- 1}T_{3}\sin\beta)\) \\ The transmission ratio error of the machine tool \(\xi_{4}\) & \(k_{4x}=(r_{p}-r_{rp}S^{-0.5})\varphi\sin\alpha+\left(a-K_{4}r_{rp}S^{-0.5}\right) \varphi\sin\beta\) \\  & \(k_{4y}=-(r_{p}-r_{rp}S^{-0.5})\varphi\cos\alpha+\left(a-K_{4}r_{rp}S^{-0.5} \right)\varphi\cos\beta\) \\ The eccentricity error of the installation of the cycloidal & \(k_{5x}=-\cos(\alpha-\theta)/K_{5}\cos\beta(\cos\alpha-K_{5}\cos\beta)\) \\ gear blank \(\xi_{5}\) & \(k_{5y}=-\cos(\alpha-\theta)/K_{5}\cos\beta(\sin\alpha+K_{5}\cos\beta)\) \\ \hline \end{tabular}
\end{table} 
Table 1: Variation coefficient of the machining error on the tooth profile. Figure 5: Schematic diagram of the grinding radius error of the grinding wheel. The coordinate equation of the cycloidal gear profile after compensation is obtained by combining Formulas (1)-(3) and related formulas in Table 1.  
\[ \begin{array}{l}x_{2}=\left(r_{p}-r_{rp}S^{-0.5}\right)\cos(1-i_{H})\varphi- \left(a-K_{1}r_{rp}S^{-0.5}\right)\cos i_{H}\varphi-g_{x}(\varphi,\xi)\\ y_{2}=\left(r_{p}-r_{rp}S^{-0.5}\right)\sin(1-i_{H})\varphi+\left(a-K_{1}r_{ rp}S^{-0.5}\right)\sin i_{H}\varphi-g_{y}(\varphi,\xi)\end{array}  \tag{4}\]
## 4 Results and Discussion
### Correlation Possibilities with the BAF-2 Well  
Approximately 3.4 km from the site of this study is the BAF-2 well located, which intersected the BCF for more than 900 metres. For the BAF-2 well, fracture network modelling and evaluation was performed by [PERSON] et al. [49] using an approach similar to that presented in this study. The main difference between the two wells is that in the BAF-2 well, the caprock of the BCF, the KSF, is missing, and the claystone formation is uncovered (Figure 8).  
In the BAF-2 well, a weathered type of the BCF was intersected between 0 and 100 m, resulting from long-term near-surface exposure. Below this interval, the hydrodynamic features indicated by the FZI values are uniform and form a single HFU. Further down, a large-scale tectonic boundary was encountered at 400 m based on fracture network features and hydrodynamic properties of the well.  
Based on sedimentological observations, the uppermost \(\sim\)200 m of the BCF has been eroded in the case of the BAF-2, while BAF-3 likely essentially penetrated this segment below the KSF. Good depth markers for the correlation of the two wells are the locations of the reductive zones within the claystone body. In borehole BAF-3, the only reductive zone is at 690 m, corresponding to 347 m in the claystone. In the BAF-2 well, there are two reductive zones, the recurrence of which in the well is probably due to tectonic activity. These zones appear at approximately 151 and 235 m.  
Based on the boundaries of connected fracture subsystems and the location of the reductive layers in the wells, at least 110 m of claystone is missing from the BAF-2 well.  
Figure 8: Possible correlation between the BAF–2 and BAF–3 wells based on the lithology and the fracture network modelling.  
Therefore, a few tens of metres short section could be correlated between the uppermost part of the BAF-2 well and the middle part of the BAF-3 well, where the reductive layer is located. Just above this horizon the fracture network can be divided into two connected subsystems in both wells what further confirms the possibility for correlation (Figure 8).  
On the other hand, the upper 100 m of the BAF-2 well is highly altered by weathering, which is not the case in the BAF-3 well. Therefore, the characteristics of the fracture system and its hydrogeologic properties differ significantly in the two wells. Due to the different stratigraphic positions and diagenetic processes in the two boreholes, the correlation is not straightforward with borehole BAF-2 regarding the fracture system of the claystone. BAF-3 instead provides essential complementary data to the BAF-2 well model for the uppermost part of the Boda Claystone and its caprock.