esa-sceva/satcom-chunk-collection huggingface dataset:
# Tumor Microenvironment and Dermatological Conditions in Prostate Cancer
### Mechanisms of PCa Progression
Thus, for example, IL-21-induced regulatory B cells produce granzyme B and participate to tumorigenesis through the recruitment of Tregs and favor tumor progression and invasion [15; 16]  
EMT denotes the process by which tumor cells exhibiting epithelial properties shift towards a mesenchymal-like state. In relation to the advancement of PCa, EMT triggers an increase in the expression of growth factors (such as transforming growth factor-beta (TGF-\(\beta\)) and insulin-like growth factor (IGF-1)); activates signaling cascades (including mitogen-activated protein kinase (MAPK) and phosphatidylinositol 3-kinase (PI3K)); stimulates androgen receptor (AR) signaling and suppresses testosterone production; induces the overexpression of some transcription factors (SNAI1/2, ZEB1, TWIST1, and ETS,); activates tumor suppressor genes (RB1, PTEN, and TP53); inhibits the expression of epithelial cell-cell contact factors (E-cadherin and \(\beta\)-catenin); increases the expression of mesenchymal factors (vimentin and N-cadherin); promotes loss of cell-cell adhesion; and, eventually, facilitates cell migration [23]. Figure 1: Cytokine/chemokine profile in PCa progression. C and X represent cysteine and non-cysteine residue; VEGFs = Vascular endothelial growth factors; ILs = Interleukins, MMPs = Matrix metalloproteinases; FGFs = Fibroblast growth factors; TGF\(\beta\) = Transforming growth factor beta; TNF-\(\alpha\) = Tumor necrosis factor alpha; IFN-\(\gamma\) = Interferon gamma; COXs = Cyclooxygenases; miRNAs = microRNAs; HIF = Hypoxia inducible factor; TME = tumor microenvironment; EMT = epithelial-mesenchymal transition. A major role in the regulation of EMT and TEM remodeling is attributed to epigenetic mechanisms. Epigenetic changes exert their effect at the chromosomal level (DNA methylation, histone modification, nucleosome positioning, and chromatin organization) and at the RNA level (RNA changes and microRNA regulation) [23; 24]. A recent hypothesis supports the concept that genetic and epigenetic changes, clonal expansion, EMT, Wnt, Sonic Hedgehog, and Notch signaling pathways favor the emergence of cancer stem cells in PCa. PCa stem cells, capable of self-renewal, pluripotency, and plasticity, have been shown to be essential factors in the processes associated with carcinogenesis (tumor growth, invasion, and metastasis) and in the emergence of resistance to antitumor therapies [30; 31; 32]. In summary, the available evidence demonstrates that EMT and the TME orchestrate tumor progression (Figure 2).
# Patients' Perceptions of Sound and Noise Dimensions in the Dental Clinic Soundscape
## 2 Methodology  
### Study Design  
A cross-sectional study design was employed to collect data at a single point in time, capturing patients' current perceptions and preferences regarding sound environments in dental clinics. Participants were recruited from diverse dental healthcare facilities, including the undergraduate and postgraduate clinics of the Department of Dentistry of the National and Kapodistrian University of Athens, Greece. Inclusion criteria encompassed adult patients (18 years and above) who have recently undergone dental treatment or were scheduled for upcoming treatments. The participants were met at the reception halls of the clinics of the Department of Dentistry and four private dental clinics in the metropolitan area of Athens located in different parts of the town. Two private settings had up to two staff members, and the other two had three or more staff members. Otherwise, the characteristics of the private dental settings were similar concerning the number of personnel in each subgroup, the number of units and dimensions of waiting rooms and secondary visiting areas), the mean time of waiting period between appointments, and setting of the reception area. A view of the main clinical university setting used to assess patients' response to noise is seen in Figure 1 and is also described elsewhere [8].  
Further, in conducting the research on human participants to explore their preferences regarding sound and noise factors in the dental office, this study took No 569/2-2-2023 approval from the ethics committee of the Department of Dentistry as part of a bigger protocol studying sound issues in the university clinics [8]. Then, we obtained informed consent from all participants, ensuring a comprehensive understanding of this study's objectives and potential implications. Consent was obtained either in a written form or through a secure digital platform, allowing participants to make an informed decision about their involvement in this study. Moreover, the research prioritized the privacy and confidentiality of participant information throughout the entire research process. Stringent measures were implemented to safeguard the sensitive data collected through the questionnaires, ensuring that participants' identities and responses remained confidential, as described in Appendix A.  
Figure 1: The undergraduate clinic of the Department of Dentistry of the National and Kapodistrian University of Athens, Greece, where 21.6% of the participants were treated.
# Vulnerability Analysis of LTE-R Train-to-Ground Communication Time Synchronization
## 2 Network Architecture and Basic Knowledge  
### GSM-R and LTE-R Network Architectures  
As the next generation of high-speed railway wireless communication, LTE-R has an adjusted network architecture compared with GSM-R, and it has adopted a flatter network structure. LTE-R combines the base station controller (BSC) and base transceiver station (BTS) in GSM-R into evolved Node B (eNodeB), making the networking architecture of LTE-R flat. GSM-R's and LTE-R's specific network architectures are shown in Figure 1.  
Compared with GSM-R, LTE-R is a typical broadband system, which has a significantly improved transmission rate, transmission delay, service firing, and performance. In terms of transmission rate, GSM-R only has a transmission speed of 2400 bit/s to 9600 bit/s under the bandwidth of 200 kHz, while LTE-R has a downlink peak transmission rate of 100 Mbit/s and an uplink peak transmission rate of 50 Mbit/s under the bandwidth of 20 MHz [15]. In  
Figure 1: The network architecture of (**a**) GSM-R and (**b**) LTE-R.  
terms of technology, LTE-R uses orthogonal frequency division multiplexing (OFDM) and multiple-input multiple-output (MIMO) technology, which not only solves the problem of serious frequency shortage but can also provide a high transmission rate.
# Flow Analysis of PM/NOX Aftertreatment System for Emergency Generator
## 3 Results
### Difference by SCR Diffuser Angle  
The diffuser provided at the front end of the SCR is provided for even distribution of ammonia by maximizing static pressure, which is a pressure acting in a direction perpendicular to the flow of the fluids. However, as the flow phenomenon occurs due to the difference in area between the inlet of the diffuser and the front end of the SCR catalyst, the flow velocity decreases rapidly, and in this process, secondary loss of flow occurs. Therefore, it is important to find an optimal angle to maximize ammonia flow uniformity while minimizing flow loss by analyzing the flow distribution according to the angle of the diffuser angle. To see the effect of the diffuser angle on the efficiency of the system, the total length of the entire system was fixed, and the magnifying tube angle was changed to \(25^{\circ}\), \(30^{\circ}\), \(35^{\circ}\), and \(40^{\circ}\). Figure 9 shows the enlarged diffuser angle model for numerical analysis, and the overall length of each model is the same.  
Figure 10 shows the change in flow uniformity and back pressure according to the enlarged tube angle. The change in flow uniformity according to the increase in angle appeared irregularly, but in the case of back pressure, it gradually improves. The diffuser angle showing the highest flow uniformity is \(30^{\circ}\).  
Figure 8: Correlation between uniformity, back pressure, and urea injector locations.  
The reason why the pressure drop due to the increase in the angle of the magnifying tube is not significant is that the recirculation flow of local ammonia occurred due to the flow-peeling phenomenon, leading to an inverse pressure gradient phenomenon. Figure 11 shows the difference in streamline for the 25\({}^{\circ}\) and 40\({}^{\circ}\) magnifying tube models, respectively. The area marked with the red circle is the area where the recirculation flow is generated, which appears stronger as the magnifying tube angle increases. If the angle of the enlarged tube is more than 40\({}^{\circ}\), the reverse flow rate occurs due to flow delamination, and recirculation occurs from the reactor area, and even after some urea is converted into ammonia, it does not flow into SCR and stagnates. As a result, the average flow rate also decreased.  
In other words, when designing at an appropriate angle, the diffuser can induce static pressure to increase flow uniformity, but if the flow rate decreases by more than a certain angle, and recirculation of the flow occurs around the dead zone due to the flow phenomenon, this causes the flow uniformity to decrease again.  
Figure 10: Correlation between uniformity, back pressure, and diffuser angle.
# Additive Manufacturing in Underwater Applications
## 3 Materials
### Polymers  
Organic polymers represent the group that is most often used for 3D printing purposes [1]. A similar situation is seen in underwater applications, where polymers represent a wider and more diverse group. However, while there are a number of 3D-printed polymer products for underwater applications, the actual underwater printing technology was developed recently [36]. Moreover, except for some manuals on popular websites, no information on scientific research in this area is available in the literature.  
Among the polymers used for underwater applications, it is worth mentioning polyurethane (PU) [37], acrylonitrile butadiene styrene (ABS) [23; 38; 39; 40], polylactic acid (PLA) [38; 41; 42], high-density polyethylene (HDPE) [43], polyethylene terephthalate glycol (PET-G) [41; 44], polycarbonate (PC) [44], and different bioplastics used for artificial reefs [35]. Most of these materials are thermoplastic polymers. Less frequently, for underwater applications, thermosets such as EPX82 resin [45] or special resins such as photosensitive resins are used, with the excellent dispersion properties of Cu\({}_{2}\)O antifouling agents [46]. Moreover, this group of materials is very often modified by other components, including glass micro-balloons (GMB) [43], or by the foaming process [43; 47], or they are protected by using coatings with special hydrophobic or hydrophilic properties [48].  
During the usage of polymer materials in underwater applications, some interesting results have been provided. One of them was delivered by [PERSON] et al. [20]. The team investigated biofouling on common 3D printing polymers: polycaprolactone (PCL), printed using fused deposition modeling (FDM), and two commercial polymer materials: Visjlet(r) SL Clear, printed by stereolithography, and VeroClear(tm), printed by PolyJet [20]. The 3D-printed samples were also compared with two materials manufactured by traditional methods: polydimethylsiloxane (PDMS) and glass [20]. The obtained results showed the best antifouling properties for PCL and the worst for glass. In general, 3D printing polymers have less microalgae and more encrusting bryozoans than reference materials [20]. This research confirms the potential of PCL as a material for marine applications [20].
## 1 Introduction  
Intrusion Detection Systems (IDS), specifically Host based Anomaly Detection Systems (HADS), have received a great deal of attention over the past decade due to their capability to detect zero-day and stealth attacks at the OS level [1, 2]. There are three main components of a HADS: a data source (DS); feature selection and construction (FSC); and a decision Engine (DE) [3]. For an IDS design and evaluation, it is critical to have a comprehensive and public data set (e.g., one that can be related to a DS) commensurate with the targeted OS. In the IDS research community, for nearly two decades, relevant IDS algorithms have been generally tested using the publicly available benchmark data set KDD 98. Unfortunately, the KDD data set was compiled in 1998 which has lost the comprehensiveness and qualitative factors [4, 5]. To tackle this, an ADFA-Linux Data Set (ADFA-LD) has recently been created [4]. However, both these data sets (e.g., KDD 98 and ADFA-LD) are LINUX oriented; and not applicable for a Windows based IDS.  
In order to protect the largest OS market share (i.e., Windows OS) from the highly sophisticated threats such as zero-day attacks, stealth attacks, data exfiltration and Distributed denial of service (DDoS) attacks [6], following the _\"defence in depth philosophy\"_, the first step is to develop a comprehensive windows based IDS data set [7]. Although Windows OS has protection methods such as signature-based anti-viruses (AV), Address Space Layout Randomization (ASLR), and DataExecution Prevention (DEP) [3], AVs are logically unable to detect zero-day attacks and stealth attacks can bypass these protection mechanisms [8; 9]. Moreover, data exfiltration and DDoS attacks are acknowledged that the recent wave of cyber threat which can be encounter through anomaly detection mechanism [10; 11].  
In this paper two Windows OS-based IDS data sets targeting zero-day and stealth attacks are provided, which are being released publicly for the IDS research community. These data sets are available at the link [12]. In the following sections, firstly, we provide descriptions of these data sets, including their design purposes, rationales for their generation environments, their logic for audit data selection, and reasons for the selection and construction of attacks. Secondly, their structures and formats, and a way of using them to design HIDS for windows OS, are discussed. Finally, we provide a preliminary analysis of both data sets by the use of following two methodologies:  
1. The evaluation of the complexity of the data sets through frequency distribution method (see Section 5.1), which identify the frequency distributions of audit data (i.e., DLL calls) among normal and attacked data. Its purpose is to identify the natural similarity between attacked and normal data that is likely to assist in the design of an HIDS decision engine.
2. Evaluating the complexity of the data sets through anomaly detection frame work by the use of several machine learning algorithms integrated with the novel feature selection scheme (see Section 5.2).
## 5 Discussion
### Magnitude Estimation of the 1303 Hongtong Earthquake  
[PERSON] et al. (2018) determined the surface rupture length of the 1303 Hongtong earthquake as 98 km through surface geological investigations and paleo-trench studies [33]. They estimated the magnitude of this earthquake, as between _Mw_ 7.1 and _Mw_ 7.6 [33]. The three-dimensional model of the main active faults in the Linfen Basin reveals that the Huoshan Fault has a total length of \(\sim\)85 km, a downward depth extent of \(\sim\)35 km, and an area of \(\sim\)2655 km\({}^{2}\) (Figure 10).  
We estimated the seismic magnitudes using the empirical formula that relates fault rupture area to seismic magnitude:  
\[M_{W}=4.07+0.98*log(RA) \tag{2}\]  
\[M_{W}=\frac{4}{3}log(RA)+3.07\pm 0.04\text{RA}\ >\ 537\text{km}^{2} \tag{3}\]  
where \(RA\) is the fault rupture area. We estimated the maximum earthquake magnitude caused by the rupture of the Huoshan Fault. Equation (2) [74] shows that the maximum rupture magnitude of the Huoshan Fault can reach _Mw_ 7.4; Equation (3) [75] calculates the maximum rupture magnitude as _Mw_ 7.6-7.7.  
The results indicate that the previously estimated magnitude of \(M\) 8 [19; 76] is relatively high. The previously estimated magnitude was determined based on historical earthquake intensity and casualty reports, lacking direct observational data for validation. Notably, the magnitudes of the 1556 Huaxian, 1793 Pingluo, and 1920 Haiyuan earthquakes have all been overestimated [77; 78; 79].  
In terms of future seismic hazard, the reduced magnitude estimate indicates that an earthquake smaller than previously thought caused the intensity of shaking and amount of damage that was observed in 1303. In addition, the reduced magnitude estimate implies a higher frequency of such earthquakes; a shorter recurrence interval is needed to accommodate a given strain rate across the Huoshan Fault with _Mw_ 7.7 events than with _Mw_ 8 events.
## Materials and Methods
### Multi-Component Fusion  
In this section, we discuss how to integrate the four main parts of the model. Since the first three spatiotemporal components model the recent, daily-periodic, and weekly-periodic spatial-temporal correlations, respectively, the impact of the three parts on different locations is various. For example, we intend to predict the traffic speed at 08:30 on Monday morning. For some places with obvious morning peaks, the output of the daily-periodic and weekly-periodic component have significant impacts on prediction performance, and for some places where there is no obvious periodic pattern, the output of the daily component and weekly component will be useless. Above all, the different locations are all affected by short-term neighbors, long-term period, and trend, but the degrees of impact may be diverse [25]. Therefore, the impact weights of different spatiotemporal components on each node are constantly changing, and these weight values should be learned from historical traffic data. Thus, we fuse the three components of Figure 4 as follows:  
\[Y_{res}=W_{h}\circ Y_{h}+W_{d}\circ Y_{d}+W_{w}\circ Y_{w} \tag{11}\]  
where \(\circ\) is Hadamard product. \(W_{h}\), \(W_{d}\), and \(W_{w}\) are all learned parameters. These parameters indicate the degree to which the outputs of the three spatiotemporal components affect the forecasting target.  
Then, we further merge the fusion result \(Y_{res}\) of the three spatiotemporal components with the output of the external component \(Y_{ext}\) to generate the final prediction result \(\hat{\mathbf{Y}}\), as illustrated in Figure 4. The output of the entire model is:  
\[\hat{\mathbf{Y}}=tanh(Y_{res}+Y_{ext}) \tag{12}\]  
where tanh is a function to map the prediction result to the range of \([-1,1]\) and makes the model converge faster.  
Our model predicts the future \(T\) timestamps speeds of all sensors based on the historical \(T^{\prime}\) traffic conditions. We choose the L2 loss as the training target of GSTGCN, which is defined by:  
\[L(\hat{\mathbf{Y}}_{t+1},...,\hat{\mathbf{Y}}_{t+T};\Theta)=\sum_{t}\sum_{i=1}^{T}|| \hat{\mathbf{Y}}_{t+i}-\mathbf{Y}_{t+i}||^{2} \tag{13}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Description** \\ \hline is\_weekend & whether it is weekend, dimension: 1 \\ is\_weekday & whether it is weekday, dimension: 1 \\ is\_holiday & whether it is holiday, dimension: 1 \\ hour & hour of the time, dimension: 1 \\ minute & minute of the time, dimension: 1 \\ weather & e.g., clear-day,rain,cloudy, dimension: 10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: External factors.  
where \(\Theta\) are all learnable parameters in the GSTGCN, \(\mathbf{Y}_{t+i}\) is the ground truth, and \(\mathbf{\hat{Y}}_{t+i}\) is the model's prediction result.
## 3 Component Selection and Criteria
### Estimation of \(C_{L},C_{D}\), and Moment of Inertia
#### 4.3.3 Estimation of Moment of Inertia  
The moment of inertia is one of the primary factors contributing to the stability of a UAV and its effects. The moments of inertia of the motor, propeller, and entire octocopter are crucial for the stability calculations for all maneuvers. In this regard, the required moments of inertias were evaluated [3] as follows: the moment of inertia of the octocopter was I\({}_{\text{oG}}\) = 0.079 kgm\({}^{2}\), I\({}_{\text{oG}}\) = 0.084 kgm\({}^{2}\), I\({}_{\text{oG}}\) = 0.109 kgm\({}^{2}\), the moment of inertia of the propeller was I\({}_{\text{oG}}\) = 0.000002493 kgm\({}^{2}\), I\({}_{\text{oGyG}}\) = 0.00007081 kgm\({}^{2}\), and I\({}_{\text{oG}}\)G = 0.00007103 kgm\({}^{2}\).
# State Estimation of Over-Sensored Systems Applied to a Low-Cost Robotic Manipulator
## 2 Models
### Inertial Measurement Unit
Those without special equipment needed to find a way to calibrate their sensors without a ground truth. This necessity brings up several new calibration techniques that rely on the properties of the measured quantities. For example, when stationary, the accelerometers should measure a vector that has a constant norm. This property gives rise to Equation (6), which can be used to find the parameters:  
\[\|G\times M+B\|_{2}=g \tag{6}\]  
This equation is not linear, which means least squares cannot be used directly in this case. Alternatives include using other optimization methods or redefining the variables to make the problem linear [4]. In the absence of magnetic field disturbances, this method can also be used to calibrate the magnetometers. Alternatively, after calibrating one of the sensors, the others could be calibrated by assuming the dot product of their reference vectors (magnetic field and symmetry of the acceleration of gravity) is constant in the working region [5]. This method has the added bonus of correcting misalignments between the frames of the accelerometers and the magnetometers. The gyroscope does not have a reference vector to measure while it is standing still. The rotation of the Earth could be used; however, it is negligible when compared to the magnitude of the noise of low-cost MEMS gyroscopes. This absence of a reference makes it difficult to independently calibrate a gyroscope based on its static measurements. However, once the other sensors are calibrated, they can be used as an acceptable ground truth. When reading a reference vector from another sensor before and after a rotation, the rotation matrix obtained by integrating the gyroscope measurements should match those vectors [6]. This method, much like the dot product one, corrects for misalignments between sensor frames since it is using another sensor as the ground truth.