esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Genomic Instability, Fe-S Clusters and Iron Chelators
### Iron chelators and The Cell Cycle  
Many iron chelators exert their antiproliferative effect by targeting key molecules in the cell cycle, such as RnR, cyclins, cyclin dependant kinases (CDKs), p21, hypoxia-inducible factor-1a (HIF-1a) and N-myc downstream regulatory gene-1 (Ndrg-1) [142, 143, 144]. The cell cycle relies on the binding of cyclin molecules and the corresponding cyclin-dependent kinase for the cell cycle to progress. Many iron chelators have been shown to affect cyclins and CDKs. One of the early studies exhibiting this used the iron chelator immosine, which resulted in a marked decrease in cyclin D/CDK4 proteins in MDA-MB-453 human breast cancer cells [145]. This effect was then shown with the pyridoxal isonicotinoyl hydrazone class of iron chelators, particularly 2-hydroxy-1-naphthylaldehydedeisonicotinoyl hydrazone (311), which showed a decrease in cyclins D1, D2, and D3 in SK-N-MC neuroepithelioma cell [146]. This has more recently been demonstrated with the iron chelator 311 and 4-[3,5-bis-(hydroxymethyl)-1,2,4-triazoleol-1-yl)]-benzoic acid (ICL670), which resulted in inhibition of the cell cycle by deregulating CDK2 and CDK9 [147]. Deferasirox has also demonstrated it can induce cell cycle arrest by downregulating cyclin D1, cyclin B, and CDK4 expression in the gastric cancer cell lines of AGS, MKN-28, SNU-484, and SNU-638 [148]. Deferasirox was also shown to upregulate Ndrg-1, p21, p27, and p53 expression. Iron chelators can cause cell cycle arrest; this exact mechanism is hard to pinpoint. It could be assumed that it is a general mix of iron deprivation in crucial enzymes mentioned above or specific iron chelators causing an increase in ROS, resulting in double-strand breaks in DNA and the resulting cell cycle arrest.
## 4 Analysis
### Cellular Automata Model for Dar es Salaam Port Traffic Flow
#### 4.2.1 Data Specific to Dar es Salaam Port  
1. Road Conditions and Infrastructure:  
Dar es Salaam Port connects to key hinterland destinations via a 150 km network of primary highways. For this simulation, a critical 20 km stretch leading out of the port is modeled. The road network is largely constrained by narrow sections and frequent construction zones, with two lanes for most of the stretch but reduced to a single lane in bottleneck areas and construction sites. Poormaintenance of the roads, characterized by potholes and rough surfaces, leads to reduced speeds and increased instances of random slowdowns as shown in Figure 3.  
Additionally, traffic flow is heavily impacted by three signalized intersections within the 20 km stretch, causing stop-and-go congestion. Frequent construction zones further reduce the road's capacity by 50%, often forcing vehicles into single lanes for extended portions of the journey, exacerbating traffic delays and congestion.  
2. Vehicle Characteristics: Vehicle Types: Heavy trucks: Common for cargo transport to landlocked African countries like Zambia, DR Congo, and Rwanda. These trucks carry heavy loads and have lower speeds, causing congestion. Small trucks and passenger vehicles: These vehicles, although faster, frequently interact with slower trucks, leading to frequent lane-changing behavior. Maximum Speeds: Heavy trucks: 60 km/h (8 cells per time step). Passenger cars and smaller trucks: 80-100 km/h (12-14 cells per time step).
3. Vehicle Density: High-density conditions are common, with vehicle flow frequently exceeding 1000 vehicles per hour on key stretches. Given the infrastructure constraints, congestion quickly builds during peak hours.
4. Environmental Factors: Seasonal Rain: Heavy rainfall during the rainy season leads to flooded roads and increased vehicle delays. Potholes become worse, and the random slowdown probability increases to \(p\) = 0.5. Traffic Accidents: Accidents are common due to poor road conditions and high vehicle density. An accident can block lanes, reducing road capacity by 50% for hours.
5. Cargo Demand: High volumes of cargo leave the port each day, with 5000 to 7000 tons of cargo per day being transported from the port to destinations across East and Central Africa.  
Figure 3: Traffic flow visualization for Dar es Salaam Port.
## 5 Addressing the Research Questions
### RQ1: What Are the Primary Fields of Application Where VR, AI, and IoT Are Used in Combination?
#### 5.2.5 Art & Culture  
Less but detectable were the applications concerning the art & culture field were they were found to be limited to enhancing the interactive experience of museum tours through the use of IoT location tag sensors to track visitor position and AI to personalize content delivery through voice interactions in order to limit screen time in visits [73]. Additionally, [74] showcased how, through IoT sensors and spatial data, AI was able to create immersive, contextually relevant AR scenes. Even though there have been attempts, the field remains under-explored and has yet to develop significant systems integrating the triad of AI-VR-IoT.  
RQ3: What Methodologies, Tools and Architectures Are Commonly Employed in Studies Combining VR, AI, and IoT?  
Through this systematic review, several core methodologies, tools, and architectures that facilitate the convergence of VR, AI, and IoT were identified. This section discusses the most common ones across the set of eligible papers. The sections is broken down to individual subsections.
## 4 Training and Detection Using Deep Learning
### Training Model
#### 4.3.3 Training Process  
Figure 8a shows the results of using set A of Table 3 for training. Initially, the loss value rapidly decreased. However, there was a sudden fluctuation in the loss value and it rebounded sharply at the 2500 training time. This is because there is a problem with the parameters, resulting in overfitting. Figure 8b shows the results using set B of Table 3. The loss value decreases gradually and is considered to be a good parameter that can be used for detection. Further, Figure 8c shows the results for the parameters of set C of Table 3. The loss curve exhibits great changes in the initial stage and is stable in the later stage; however, the validation curve has two peaks. Finally, Figure 8d shows the results using set D in Table 3. The parameters can be considered appropriate. The loss curve of training and validation in the training process decreases rapidly in the initial stage and tends to be stable in the last stage.  
Parameters must be determined before model training and cannot be updated manually during training. Currently, there is no consensus on the selection of appropriate parameters. However, according to the various situations of loss curve and the user's  
Figure 8: Loss value in the training and validation. (**a**) the training and validation loss using set A of Table 3. (**b**) the training and validation loss using set B. (**c**) the training and validation loss using set C. (**d**) the training and validation loss using set D.  
personal preference, certain judgment conditions can be set for selection [26]. The judgment condition used in this study was that the loss value reached less than 0.2 within 6000 times without obvious rebound. According to this benchmark [26], set D was the best parameter, although set B can also be used according to personal preference.
## 2 Experimental Dataset Construction
### Axial Compression Test  
To ensure the richness and diversity of experimental data, 180 specimens of different specifications were designed by changing the geometric, steel and concrete properties of square CFST short columns. Three specimens of each specification were made to reduce the influence of the dispersion of axial compression test data, and a total of over 540 specimens were made. Before performing a series of axial compression tests, the multi-mechanical properties of steel and concrete in CFST specimens of different specifications were measured based on GB/T 228.1-2010 and GB/T 50081-2002, respectively.  
The test consists of five main steps: (1) design of specimens; (2) selection and preliminary processing of steel tube; (3) selection and production of concrete mix; (4) pouring and curing of specimens; (5) loading and measurement. The axial compression tests of all specimens were performed on a hydraulic loading system with sufficient loading force. The schematic diagram of the experimental device is shown in Figure 1. A cushion plate is placed between the specimen and loading plate to ensure that the CFST specimen is under uniform stress during loading. The preloading should be carried out before the formal loading, and all specimens should bear the central load. The preloading force cannot exceed 30% of the expected ultimate axial capacity. The multi-stage loading was adopted in the test process [35; 36] and the automatic data acquisition system was employed to record the axial load until the specimen failure. After the completion of all tests, the arithmetic mean of the ultimate axial capacity of the three specimens of each specification was taken as the final ultimate axial capacity \(N_{u}\).
# Blockchain Technology for Intelligent Environments
## 1 Introduction  
Intelligent Environments [1; 2] are becoming increasingly relevant to our daily lives, as we are witnessing the digital and the physical worlds gradually blending into each other over the last few years. This is evident not only in industrial settings (e.g., smart offices [3], supply-chain provenance tracking [4], industrial automation [5]), but also in leisure activities (e.g., smart homes [6; 7], augmented reality games [8]), in healthcare (e.g., autonomous patient monitoring and assisting [9]), in urban planning (e.g., smart cities [10]), and more.  
The proliferation of the _Internet of Things_ (IoT) [11; 12; 13] is a key component of this evolution, as IoT devices are becoming pervasive in our physical environment. Although fundamental technologies that enable this trend, such as diverse types of networking, communication protocols, data analytics, and machine learning algorithms, have received extensive attention by the research community, and major challenges remain unresolved when issues such as _reliability_, _accountability_, _trust_, and _transparency_ are at stake.  
Blockchains [14; 15] constitute a relatively new technology, with no more than a decade of history. Despite their recent conception, blockchains have attracted tremendous attention in a number of diverse fields, most notably in computer science, cryptography, finance, economy, civil law, healthcare, rights management, real estate, auctions, gambling, and generally all industries for which one or more of the aforementioned challenges (_reliability_, _accountability_, _trust_, _transparency_) play key business roles. Some people claim that blockchains will bring to asset management as big of a revolution as the Internet brought to communication.  
In particular, for Intelligent Environments and the emerging field of the Internet of Things, blockchains can be a game changer [14]. Through _smart contracts_, blockchains enable for the first time ever provable trust in the interaction between sensors, actuators, and processors that belong to different owners, jurisdictions, and administrative domains. This implies that blockchains can offer a new level of reliability, transparency, and trust to existing or upcoming intelligent environment designs.  
This paper explains the principles on which blockchains operate and the most noteworthy implementations of this disruptive new technology, and studies existing blockchain-based systems targeting IoT applications.  
More specifically, the paper is organized as follows: Section 2 explains blockchain systems and their inner workings, while Section 3 describes the most notable blockchain implementations. Section 4 presents the challenges of interfacing blockchain smart contracts with the external world and lays out the most notable oracle services deployed to date. Section 5 explores the role of blockchains for intelligent environments and provides an extensive survey of the state-of-the-art blockchain-based systems targeting IoT applications. Finally, the paper concludes in Section 6.
## 4 Discussion
### Primary Part
The findings of the exploratory part of PRATER were not aligned with literature trends. The number of new NSCLC cases remained relatively stable, while in terms of staging, a significant increase in Stage I NSCLC was observed. The latter observation could be speculatively partially attributed to an increase in chest CT imaging taking place during the pandemic for suspected or documented COVID-19 infection [80; 81; 82]. When assessing the impact of COVID-19 in cancer care, diagnosis and treatments should both be considered. Although use of ST-alone among Stage I patients in PRATER was statistically significantly reduced during the pandemic, this difference is not considered of clinical importance given the small proportion of patients belonging to this treatment group. Furthermore, based on literature, a decline in surgical interventions was observed during the pandemic [47; 48]. This was not the case in PRATER. Lastly, the statistically significant increase in ICI among Stage III-IV patients, coupled with a statistically significant decrease in ChT-alone, reflects the continued approval and adoption of new ICIs, rather than a pandemic-related cause [18; 26; 28; 29; 30; 31]. Consistently, higher 1L ICI usage among Stage IV patients during COVID-19 has previously been documented in other European countries as well, most likely arising from the evolving treatment landscape as stated by the authors [83]. The overall ST rate (79%) and ICI rate (53%) among Stage IV patients observed during the pandemic in PRATER closely follows 2020 ESMO-based benchmarks for Stage IIIB/IV of \(\sim\)75% and \(\sim\)40%, respectively (based on guidelines and timing of EMA approval) [29]. ICIs are becoming the cornerstone 1L therapy for non-oncogene addicted Stage IIIB-IV patients, while ChT-alone is reserved as an option for patients with low/absent PD-L1 expression, poor performance status, and ICI contraindications, among other clinical factors [26; 83; 84]. As such, the low utilization of ChT-alone among metastatic NSCLC patients (9%) during the pandemic in PRATER indicates that treatment selection is very much aligned with temporal trends, more so than another EU5 study (37% of 1L regimens in Stage IV were ChT-alone) [83]. In addition to optimal treatment decision-making, compulsory social insurance [85], and EMA-based centralized authorization [86; 87] allowing fast access to breakthrough therapies, have likely contributed to the observed Austrian trends. Overall, lung cancer care in Austria was not significantly affected by the COVID-19 pandemic in healthcare encounters, while new pharmacologic therapies are being successfully implemented in clinical practice.
## 2 Study Area and Datasets  
The study area, the Sonipat district, extends into the NCR (Figure 1). The Sonipat district covers an area of 2213 km\({}^{2}\) and borders the states of Delhi and Uttar Pradesh. The district is part of the Indo-Gangetic Plains, formed during the Pleistocene epoch of the Quaternary period. The elevation across Sonipat varies, with an average altitude of approximately 224 m. Sonipat's unique blend of rapid urbanization, agricultural dependency, and diverse landscape typologies within the NCR exemplifies challenges faced by many developing regions globally. Studying these dynamics allows for the generalization of findings to similarly urbanizing and agrarian areas, thereby informing sustainable land management practices across diverse contexts. In Haryana, the Yamuna River and irrigation canals flowing from it make up the district's main water supply system. The district does not have perennial rivers, and different areas have different underground water resources. The Khader area along the Yamuna has the lowest water table depth, at 10 feet. Some western and southeastern parts of the district have depths of 30 to 40 feet. Certain areas have brackish and saline groundwater. In some places, the area is not level, but overall, it is a continuous part of the Haryana-Punjab plain. The soil is fine loam with rich color throughout the district. However, some areas have sandy soil, while others have Kallar. The plain slopes gently to the south and east. The district can be divided into three distinct regions: khadar, upland plain, and sandy region. The district's climate is dry in summer and cold in winter, and the monsoon comes in July and remains until September. December marks the beginning of the winter season. The month with the lowest temperatures is January, while the months with the highest temperatures are May and June. In December, January, and February, there is light rainfall.
# Smart Handoff Technique for Internet of Vehicles Communication using Dynamic Edge-Backup Node
###### Abstract
Many strategies are used for improvements of this issue. We also outline the handoff research challenges that need to be addressed for better handoff performance. Handoffs can be categorized on the basis of different factors; i.e., types of network, elements involved in network or live connections, and kind of traffic the network provides. In order to familiarize the reader with the main types of handoff, a list of these types is given below along with the discussion. 1. Hard handoff;
2. Soft handoff;
3. Horizontal handoff;
4. Vertical Handoff. In a hard handoff channel, the source cell will be out of coverage and only the channel within the specified cell is involved [9]. Therefore the connection between source and destination will breakdown before or as the connection to target gets created; for this purpose, handovers are also called hard handoffs. When a node is between BS, then the node can move with any BS. So, BS springs up the link with the node back and forth. This mechanism is also called ping-ponging. On the other hand, when a new connection is established before the previous connection is broken, this defined as a soft handoff [9]. In a soft handoff, the channel in a source area is reserved and used in parallel with channel in target area. In this scenario the connection to the target is established before the connection to source is breakdown. Therefore, this handoff is known as make-before-break. Soft handovers may include connections to more than two cells. As another scenario, when nodes switch between networks of the same infrastructure type while changing their connection in order to maintain active connection, this switching process is called horizontal handoff. E.g., from one GSM ne work to another or from one WiFi AP to another. In contrast, as a way to maintain an active connection while nodes switch between networks of different types while changing the connection, the process is known as vertical handoff. Figure 3: Handoff scenario. Vertical handoff is the process of changing the mobile active connection between different wireless te technologies. Vertical handoffs can be further distinguished into downward vertical handoff (DVH) and upward vertical handoff (UVH). In DVH, the mobile user hands off to the network that has higher bandwidth and limited coverage, while in UVH the mobile user transfers its connection to a network with a lower bandwidth and wider coverage. The structure of rest of the paper is as follows. Section 2 includes a literature review of some related research and a comparative analysis of the existing handoff techniques. Section 3 includes proposed methodology. In Section 4, we discuss the performance evaluation while Section 5 concludes the paper. All abbreviations and mathematical notations are listed in Appendix A, Tables 10 and 11 accordingly.
# Muscle Activity and Biomechanics of Sprinting: A Meta-Analysis Review
## 2 Materials and Methods
### Outcome Measures
* Electromyographic (EMG) activity of key lower limb muscles, including biceps femoris, semitendinosus, gluteus maximus, rectus femoris, and gastrocnemius.
* Kinematic and kinetic variables, such as joint angles (hip, knee, ankle flexion/extension during stance and swing phases), ground reaction forces (GRF), propulsion and braking forces, step length, stride frequency, and ground contact time.
* Changes in muscle recruitment patterns under fatigue conditions or in response to sprint-specific training adaptations.
## 3 Controller Synthesis
### Control Strategy
For position control, since the UAV is operating around hovering, the small-angle assumption (\(\psi=0;\theta=0;\varphi=0\)) is applied:  
\[\dot{x}_{2}=\frac{u_{1}}{m}[\cos(\psi)\theta_{d}+\sin(\psi)\varphi_{d}]-\frac {C_{dx}}{m}x_{2}+w_{k} \tag{37}\]  
\[\dot{y}_{2}=\frac{u_{1}}{m}[\sin(\psi)\theta_{d}-\cos(\psi)\varphi_{d}]-\frac {C_{dy}}{m}y_{2}+w_{k} \tag{38}\]Let the tracking errors of \(x\) and \(y\) be defined as:  
\[e_{x}=x_{1}-x_{1}^{\ *};\ e_{y}=y_{1}-y_{1}^{\ *} \tag{39}\]  
Therefore, a virtual control signal is proposed to control the \(x\) and \(y\) positions using the nonlinear decoupling equations:  
\[\varphi_{d}=u_{x}sin(\psi)-u_{y}cos(\psi) \tag{40}\]  
\[\theta_{d}=u_{y}sin(\psi)+u_{x}cos(\psi) \tag{41}\]  
and can be implemented with:  
\[u_{x}=K_{px}e_{x}+K_{ix}\int e_{x}+K_{dx}\dot{e}_{x} \tag{42}\]  
\[u_{y}=K_{py}e_{y}+K_{iy}\int e_{y}+K_{dy}\dot{e}_{y} \tag{43}\]  
The PID coefficients are determined empirically by tuning the proportional, integral and derivative gains \(K_{p},\ K_{i}\) and \(K_{d}\) based on the observed system responses. This process involved a trial-and-error approach, where the parameters were adjusted one at a time while monitoring the quadcopter's behavior to achieve balance between performances.
## 3 Simulation and Results
### Simulation and Performance Measurements of the EKF Algorithm  
The objective of this section is to measure the performance of the EKF algorithm compared with the original datasets, _i.e._, the EUR-USD exchange rates using the same methods employed by Section 3.2.  
Similar to Figure 7, Figure 9 shows three plots, _i.e._, the original datasets (Y2), the \"original datasets \(+\) noise\" (Y1) and EKF (Y1). We analysed the plots and found that the EKF curve resided within the curve of \"the original datasets \(+\) noise\" except a spike occurred at the beginning of the \(x\)-axis. The trend of those three plots was in the same direction. In terms of estimation, Tables 1-6 indicate that all loss estimators, namely; MSE, MAE, MAPE are relatively low ranging from 0.00406-0.01870, 0.05546-0.12765 and 4.69540-10.7989, respectively. The R\({}^{2}\) is in the ranging from 0.6637-0.9017, whereas, AIC and BIC are high, up to \(-\)6429.89 and \(-\)6412.64, respectively, and the Accuracy count showed 47.13%-51.66%. As a result, the performance of the EKF algorithm is unacceptable.  
Figure 8: Plots of the original datasets, original datasets \(+\) noise, and the Wavelet Transform (WT) algorithm.  
Figure 9: Plots of the original datasets, original datasets \(+\) noise, and the EKF algorithm.