esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Optimization-Based Strategies
### Instantaneous Optimization
#### 3.1.1 Equivalent Consumption Minimization Strategy (ECMS)
ECMS, as an instantaneous optimization algorithm, can compromise approximate global optimality and computational burden; however, it is implemented over a driving cycle provided in advance. Even though previous knowledge of the future driving cycle can be predicted based on trip distance, future traffic and terrain conditions, the prediction accuracy and the value of the optimal equivalent factor will have a significant influence on the optimality of the EMS, which limits the further application of this strategy.
## 2 Materials and Methods  
### Structure of Tractor ANS  
The structure of the ANS is shown in Figure 1. The ANS is mainly composed of a navigation host computer, which performs navigation decisions, and a navigation slave computer, which executes navigation control commands. The navigation states are transmitted to the navigation host computer via an RS232 serial port, and the predefined path is combined with the real-time state of the tractor, which then outputs the desired steering angle to the navigation slave computer. The embedded microcontroller receives the expected steering angle from a navigation decision module and calculates the actual steering angle based on the angle sensor fixed to the front wheel of the tractor. Finally, the decided steering angle is sent to the travel control actuator. The parameters of the Dongfanghong 1104 are listed in Table 1; it is equipped with power-assisted steering, making it suitable for electric automation reconstruction for self-driving. The RTK system included a base station and a rover station, the positioning cards of which were provided by the Beijing Beidou Satellite Communication Group Co., Ltd. (Beijing, China). Its positioning accuracy was 1 cm + 1 ppm, and, in cases where the distance between two antennas reached 2 m, the heading accuracy was 0.08\({}^{\circ}\). The microcontroller for tractor travel control was provided by Beijing Zhongqian Lingyun Electronic Co., Ltd. (Beijing, China). In addition to executing travel control commands, the controller also sent the state of the electronic control unit (ECU) to the navigation host computer. Both the navigation decision information and tractor steering angle were updated at 5 Hz.  
Figure 1: The structure of an ANS for tractors. (**1**) RTK base station; (**2**) tractor; (**3**) RTK radio; (**4**) rover receiver antenna; (**5**) rover receiver; (**6**) navigation host computer; (**7**) steering motor; (**8**) electronic throttle; (**9**) hydraulic lifting; (**10**) navigation slave computer; (**11**) angle sensor; (**12**) ECU.
### Literature Review on Available Solutions
The efficiency of the solutions presented above is strictly related to the real-time knowledge of the spatiotemporal distribution of the population, which is fundamental for risk analysis and emergency management (i.e., for an estimation of human exposure and vulnerability) [49]. One of the most used techniques to model or track moving crowds is Computer Vision [50]. Convolutional Neural Networks (CNNs) represent the key algorithms in computer vision, and recently [51], enhanced versions of the tree growth and firefly metaheuristics algorithms have been proposed to automatize the hyperparameters' optimization process (i.e., find the right set of hyperparameters that allow one to obtain the best CNN model accuracy), obtaining better performances (in terms of image classification accuracy and the use of computational resources) than the traditional approaches (that require time expertise). Furthermore, faster regions CNN (Faster R-CNN) is considered one of the most important techniques for automatic pedestrian detection from video [52], and, if automatic color enhancement (ACE) is used, good performances (in terms of recognition rate and offset of target selection) can be obtained because of the reduced susceptibility to the diversity of pedestrians' appearances and the light intensity in specific scenarios (e.g., a subway) [52]. Other suitable techniques, such as satellite imagery processing, could be used as an aid for the automatic recognition of vegetation, landslides, and geospatial data [53; 54]. Note that an easy detection of a full-body person in perspective can be carried out using other methods, such as Mask R-CNN [55], Pose2 Seg [56], and EfficientDet [57]. Finally, Convolutional Neural Networks are methods specialized in a grid-like structure or multiple arrays form [58; 59].
# Effect of Blood on Synovial Joint Tissues: Potential Role of Ferroptosis
## 1 Introduction  
Smyonial joints allow for a significant range of motion, including the flexion and extension of the knee, and are characterized by a synovial cavity that contains synovial fluid (SF) [1]. This viscous solution is responsible for joint lubrication and reducing friction between articular cartilage during movement. Under arthritic conditions, the rheological properties of synovial fluid are significantly impaired [2]. Synovial neutrophils from patients with rheumatoid arthritis (RA) and osteoarthritis (OA) produce significantly higher levels of reactive oxygen species [3]. Increased inflammation and oxidation stress exacerbate the degradation of hyaluronic acid (HA), a major component in SF, leading to various joint-related diseases [1].  
Joint problems resulting from recurrent joint bleeding or hemarthosis, such as chronic synovitis and degenerative arthritis, are major causes of morbidity, where chronic arthropathy affects \(\sim\)20% of hemophiliacs [4]. In addition to hemophilia, conditions such as rheumatoid arthritis (RA), periarticular and articular fractures, osteochondralal autograft transplantation surgery, and anterior cruciate ligament (ACL) injury are associated with jointbleeding [4; 5; 6; 7; 8]. Additionally, traumatic injuries to the synovial joint have been associated with inflammation, acute pain, bloody joint effusion, and knee instability (Figure 1). To curtail the deleterious effects of blood, some clinicians recommend aspiration of blood from the joint post-injury and prior to surgical closing [8; 9]. Despite the significant potential clinical impact of joint bleeding, the mechanism(s) by which joint bleeding, acute or microbleeds, leads to deleterious changes to the synovial joint remains understudied [7]. While apoptosis is a primary candidate for regulated cell death (RCD) in blood-exposed chondrocytes, based on our studies of blood in co-culture with chondrocytes/cartilage (as mentioned in Section 4.1), we speculate that an iron-dependent, nonapoptotic form of RCD, ferrotopsis, plays a critical role in the development of hemophilic arthropathy (HA) and may impact injurious changes to joint tissues [10; 11; 12; 13; 14]. While the damaging effects of blood on cartilage and synovium are well-described, there are much fewer reports describing the negative effects of blood on the meniscus, cruciate ligaments, and subchondral bone [15; 16; 17; 18; 19]. This paper aims to provide a comprehensive overview of the impact of blood on synovial joint tissues from clinical observations as well as in vitro and in vivo investigations. A better understanding of blood-synovial tissue interactions may help to inform the development of therapeutic treatments that can mitigate the deleterious effects of synovial joint bleeding.
# Evaluation of a New-Concept Secure File Server Solution
## 4 Results
### Results on 1 Gbit/s Connection
#### 4.1.1 Results of Reading Performance Tests on 1 Gbit/s Connection  
The detailed results of the reading tests conducted on the network with 1 Gbit/s throughput are illustrated in Table 2 and Figure 2. The used notation convention of files involved in the operation is as follows: _the number of files \(\times\) file size_.  
For files of 256 MiB and 1 GiB, all six client-server platform pairs performed very similarly (min. 101.01 MiB/s, max. 111.21 MiB/s), as it can be seen in Table 2, but for files of 4 MiB, a significant performance drop was observed for Windows clients. However, the results also showed that the transfer speeds for both server platforms were nearly the same for Windows 7 and Windows 10. It was also striking that, regardless of the file server, the Ubuntu 22.04 client was able to achieve 30-40 percent faster transfer speeds. When reading files up to 500 kiB, the speed dropped by almost a fifth for Windows 7 and Windows 10, regardless of the server-side platform, while the Ubuntu client was roughly three times faster in both scenarios.
## 2 Materials and Methods
### GC-MS Analysis of Aroma Compounds in Grapes and Wines  
Aroma compounds from grape and wine samples were extracted by headspace solid-phase microextraction (HS-SPME) and analyzed by gas chromatography-mass spectrometry (GC-MS) as described by [PERSON] et al. [34]. Qualitative and quantitative methods were used as described by [PERSON] et al. [35]. Aroma compounds were analyzed using Agilent 6890 GC equipped with Agilent 5973C MS and fitted with an HP-INNOW AX capillary column (60 m \(\times\) 0.25 mm, 0.25 \(\upmu\)m, J \(\&\) W Scientific, Folsom, CA, USA). Over temperature began with 50 \({}^{\circ}\)C for 1 min, then increased to 220 \({}^{\circ}\)C at a rate of 3 \({}^{\circ}\)C/min and held at 220 \({}^{\circ}\)C for 5 min. The temperature of the ion source and quadrupole was set at 250 \({}^{\circ}\)C and 150 \({}^{\circ}\)C, respectively. Helium was the carrier gas at 1 mL/min and the GC inlet was set in the splitless mode. The full scan mode was employed to collect electron ionization mass data from \(m/z\) 30-350. The ionization voltage was set at 70 eV. Aroma compounds were identified based on mass spectra matching in the standard NIST08 library and retention indices in the literature. The quantification procedure was based on a previous report [36]. The concentrations of volatile compounds were expressed as ug/L in wines and ug/kg of fresh berry weight in grapes.
# Recent Progress on Solution-Processed CdTe Nanocrystals Solar Cells
## 5 CdTe/ZnO Heterojunction Solar Cells
To achieve high device performance, alloying is a versatile tool for engineering the optical and electronic properties of materials, which has been confirmed in CIGS- and CZT(S,Se)-based solar cells. By tuning the CdSe\({}_{x}\)Te\({}_{1-x}\) film composition, [PERSON] and co-workers [9] were able to vary the optical bandgap from 1.38 eV (absorption onset 899 nm) to 1.73 eV (absorption onset 717 nm). They adopted this strategy to fabricate CdTe solar cells with a single CdTe layer, three CdSe\({}_{x}\)Te\({}_{1-x}\) layers, and a ZnO NC layer. They found that the \(V_{oc}\) was essentially independent of the alloy layer composition and the alloy based solar cells had a lower \(I_{sc}\) than CdTe-only devices. In such a case, the built-in voltage of the device was governed by the work function difference between the ITO and the heavily-doped ZnO layer, which was about 0.5-0.6 V. Optimizing the thickness and, therefore, the spectral absorption of the alloy layers, were very crucial to obtain high device performance.
## 2 The CADLL Architecture for Multipath Estimation and Mitigation  
The CADLL architecture was introduced in [8] and [10] as a multiple DLL structure able to estimate amplitude, delay and phase of a number of relevant reflections of the GNSS LOS signal, in order to cancel them from the incoming signal, thus improving the positioning performance.  
As shown in Figure 1, CADLL is composed of several tracking units, each tracking a component signal. After a transient time, the estimated multipath signals are subtracted from the total input signal to reduce the error caused by multipath in LOS signal. Inside each tracking unit, a normal code DLL with wide or narrow spacing is used to track the code phase and two Amplitude Lock Loops (ALL) are used to track the amplitudes of the I and Q components of the signal. The ALL is composed of an estimator, a loop filter and an integrator.  
The working procedure of CADLL is made of different steps. It first uses a conventional tracking loop to lock onto the incoming signal, getting a rough estimation of the LOS code phase; then it activates two units in order to try to track a multipath signal. If it fails, this means there is no multipath in the incoming signal; if it succeeds, it will continue to try to insert a new unit into this feedback loop to look for a new multipath component.  
The monitor block (see Figure 2) is governing the process of searching a new multipath component by checking the tracking results of each unit. If it is considered that there is no new multipath component,  
Figure 1: CADLL block diagram.  
the trial unit will be shut down by the monitor block. The monitor block checks in particular two parameters: the time difference between the delays estimated by two consecutive units,  
\[E(\hat{\tau}_{n-1}-\hat{\tau}_{n})<\delta t_{th2} \tag{1}\]  
and the amplitude of each tracked component,  
\[E(\hat{a}_{n})<\hat{a}_{thre} \tag{2}\]  
In order to warn if the amplitude is negligible and if two very close components are tracked, thus resulting in the cancelation of the useful LOS during the feedback phase. The process will not stop until there is no new multipath found or the number of enabled units reaches the maximum number \(M_{M}\), which is pre-defined according to available resources.  
Following this specially designed working procedure, CADLL actually has the ability of estimating the number of multipaths and adjusting its structure to match it. The detailed description of the CADLL working procedure can be found in [8] where the mathematical derivation and performance evaluation are presented.
# Numerical Simulation Study on Welding Process of Upper Frame of Hydropower Unit
## 4 Simulation Analysis of Welding Deformation of Upper Frame Based on ISM  
### The Theory of Inherent Strain  
In general, the total strain \(\varepsilon\) includes elastic strain \(\varepsilon^{e}\), plastic strain \(\varepsilon^{p}\), thermal strain \(\varepsilon^{T}\), and phase change strain \(\varepsilon^{tr}\), as follows:  
\[\varepsilon=\varepsilon^{e}+\varepsilon^{p}+\varepsilon^{T}+\varepsilon^{tr} \tag{3}\]  
For the welding of low-carbon steel, the effect of phase transformation and thermal strain on the final welding residual stress and deformation is very small, which can often be ignored [4]. In this case, the inherent strain is equal to the residual plastic strain.  
\[\varepsilon^{*}=\varepsilon-\varepsilon^{e}=\varepsilon^{p}+\varepsilon^{T}+ \varepsilon^{tr}=\varepsilon^{p} \tag{4}\]  
Figure 16: The calculation results of the equivalent residual stress. (**a**) Cooling time is 0 s; (**b**) cooling time is 30 s; (**c**) cooling time is 60 s; (**d**) cooling time is 90 s; and (**e**) cooling time is 0 s.  
Figure 17: \(Z\)-direction deformation.  
Currently, the primary methods for obtaining inherent strain include the experimental method and TEP-FEM. However, due to the difficulty in accurately measuring welding shrinkage through experiments, this paper employed the TEPM to obtain inherent strain. The inherent strain value was extracted from the finite element model of the typical T-joint of the upper frame and then applied to the whole model of the upper frame model. The whole welding deformation can be predicted by a single elastic calculation.
# Coalition Formation Game for Cost-Efficient Multiparty Payment Channel in Payment Channel Networks
## 4 Multiparty Payment Channel
### MPC State Synchronization  
#### Full Synchronization  
The MPC state is updated immediately by broadcasting the new transaction block or using a central repository.  
Figure 4: Example of 4 users for a multiparty payment channel.  
* Broadcast: SPC users unicast the new block data directly to all MPC members, \((N-2)/2\) peers per SPC user. Otherwise, a spanning tree for the MPC is maintained for block propagation.
* Repository: the MPC can designate one of the members as a server for the local chain repository. Once one of the SPC users uploads a new block to the repository, other members can query the update.  
The broadcast requires additionally a node discovery and routing protocol to build up a minimum spanning tree, while the repository can suffer from a single point of failure and congestion.
# A MSA-YOLO Obstacle Detection Algorithm for Rail Transit in Foggy Weather
## 4 Experiments
### Experimental Results
#### 4.3.2 Object Detection Experimental Results  
To validate the effectiveness of the object detection algorithm proposed in this paper, a comparative analysis was conducted between IA-YOLO [33] and MSA-YOLO. Figure 3 illustrates the object detection performance of IA-YOLO and MSA-YOLO under different \(\beta\) values and various foggy conditions. As shown in the figure, when \(\beta\) < 0.1, both IA-YOLO and MSA-YOLO effectively detect cars. However, when \(\beta\)\(\geq\) 0.1, IA-YOLO fails to detect cars in foggy environments, whereas MSA-YOLO continues to perform effectively. This indicates that MSA-YOLO excels at removing dense fog while preserving and enhancing critical obstacle information in the images.  
The MSA module improves the network's ability to detect objects of varying scales by integrating high-resolution detail information with low-resolution global information. High-resolution detail information allows the system to capture fine features of target objects, while low-resolution global information provides a comprehensive view, enabling the system to recognize a wider range of target objects in complex environments. This multi-scale information fusion allows MSA-YOLO to perform exceptionally well in various conditions, particularly in complex, foggy weather. The model can accurately identify and locate obstacles in the images, thereby enhancing the system's robustness and detection accuracy.  
Figure 4 shows the obstacle detection results of MSA-YOLO and IA-YOLO under varying fog densities. In the first column, IA-YOLO misses a bus. In the second column, IA-YOLO detects a section of the road as a bus. In the third column, IA-YOLO identifies a tree trunk as a pedestrian. However, MSA-YOLO correctly identifies the objects in the images. These examples demonstrate MSA-YOLO's excellent performance in identifying various objects of different sizes, shapes, and orientations in complex scenes. This achievement proves the high sensitivity and accuracy of our algorithm, further validating the advancement and practicality of MSA-YOLO in object detection. These visual examples showcase the broad potential and value of MSA-YOLO in real-world applications.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **IA** & **MSA** \\ \hline PSNR & 15.8945 & 17.4097 \\ \hline SSIM & 0.7735 & 0.8044 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Defogging results of railway images.  
Figure 3: _Cont_.  
Figure 2: Defogging effects of different algorithms.  
Figure 3: Detection performance of IA-YOLO and MSA-YOLO at varying fog densities.
The TOPSIS method was employed to select key behavioral features, improving the authentication system's performance by focusing on the most impactful data. 4. The system's resilience was enhanced by applying data perturbation techniques, including noise injection and temporal scaling, to simulate real-world variations. 5. The system was tested against four types of cybersecurity attacks--spoofing, lighting variations, orientation changes, and noise injection--to assess its robustness and security. The key novelty of this research lies in its integrated framework that combines behavioral biometrics, multi-criteria decision-making (TOPSIS), and ensemble machine learning models (RF, GBM, and KNN) for mobile authentication. Unlike prior works that utilized either traditional classification techniques or isolated biometric traits, this study introduces an iterative feature-ranking mechanism that refines the input space using TOPSIS and feeds only the most impactful features into multiple ML models. The proposed system also enhances robustness through real-world-inspired perturbation techniques (e.g., noise injection, temporal scaling), which are often overlooked in previous research. This combination enables the development of a real-time, lightweight, and resilient authentication system that outperforms existing models, achieving an accuracy of 95.2% with enhanced spoofing resistance and environmental adaptability. The rest of the paper is organized as follows: Section 2 gives a brief overview of previous works, including behavioral biometrics for authentication systems, ML techniques in authentication systems as well as decision-making methods, and current challenges in security and usability. Section 3 presents our proposed technique in detail. The experimental results, including the decision and analysis, are given in Section 4. Our conclusion and future works are explained in Section 5.
## Materials and Methods
### Trend Analysis and Significance Test  
Linear regression analysis was done to simulate the trend of vegetation index variation on the pixel scale from 2000 to 2019. The formula is as follows:  
\[slope=\frac{n\sum_{i}^{n}\left(i\times Var_{i}\right)-\sum_{i=1}^{n}i\times \sum_{i=1}^{n}Var_{i}}{n\sum_{i=1}^{n}i^{2}-\left(\sum_{i=1}^{n}i\right)^{2}} \tag{1}\]  
where the slope is the inter-annual variation rate of relevant variables i, \(\text{Var}_{i}\) the value of the relevant variable in the ith year (season), and n represents the length of the dataset (n = 20). Slope > 0 indicates an increasing trend of the \(\text{Var}_{i}\) during the study period, and slope < 0 indicates a negative trend of the \(\text{Var}_{i}\) during the study period.  
\[\sum_{i=2}^{n}\sum_{j=1}^{i=1}sign(X_{i}-X_{j}) \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Data** & **Data** & **Spatial** & **Temporal** & **Temporal** & **Acquisition** \\
**Type** & **Sources** & **Resolution** & **Resolution** & **Span** & **Time** \\ \hline NDVI & [[https://lpdaac.usgs.gov/](https://lpdaac.usgs.gov/)]([https://lpdaac.usgs.gov/](https://lpdaac.usgs.gov/)) & 500 m & 16-day & 2000–2019 & 2021.10.11 \\ GPP & [[https://lpdaac.usgs.gov/](https://lpdaac.usgs.gov/)]([https://lpdaac.usgs.gov/](https://lpdaac.usgs.gov/)) & 500 m & 8-day & 2000–2019 & 2021.10.19 \\ LAI & [[https://lpdaac.usgs.gov/](https://lpdaac.usgs.gov/)]([https://lpdaac.usgs.gov/](https://lpdaac.usgs.gov/)) & 500 m & 8-day & 2000–2019 & 2021.10.23 \\ TEM & [[https://data.tpdc.ac.cn/](https://data.tpdc.ac.cn/)]([https://data.tpdc.ac.cn/](https://data.tpdc.ac.cn/)) & 1 km & monthly & 2000–2019 & 2021.10.15 \\ PRE & [[https://data.tpdc.ac.cn/](https://data.tpdc.ac.cn/)]([https://data.tpdc.ac.cn/](https://data.tpdc.ac.cn/)) & 1 km & monthly & 2000–2019 & 2021.10.05 \\ SSD & [[https://data.cma.cn/](https://data.cma.cn/)]([https://data.cma.cn/](https://data.cma.cn/)) & 2400 stations & daily & 2000–2019 & 2021.12.23 \\ Land Cover & [[https://www.resdc.cn/](https://www.resdc.cn/)]([https://www.resdc.cn/](https://www.resdc.cn/)) & 500 m & yearly & 2000–2019 & 2022.4.3 \\ (Type 1) & Default.aspx & & & & \\ \hline \end{tabular}
\end{table}
Table 2: Main data used in this study.  
\[sign(X_{i}-X_{j})=\left\{\begin{array}{l}-1,X_{j}-X_{i}<0\\ 0,X_{j}-X_{i}=0\\ +1,X_{j}-X_{i}>0\end{array}\right. \tag{3}\] \[Var(s)=n(n-1)(2n+5)/18\] (4) \[\left\{\begin{array}{l}Z=\frac{S-1}{\sqrt{Var(s)}},S>0\\ Z=0,S=0\\ Z=\frac{S+1}{\sqrt{Var(S)}},S<0\end{array}\right. \tag{5}\]  
At the same time, the Mann-Kendall test by Equations. (2)-(5) was used to check the significance level of the vegetation trend.