esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### CpxceRNA Mediates Chromatin Interactions Between Its Genomic Locus and TAD Boundaries
This is consistent with the observation that the _CpxexRNA_ genomic locus is an enhancer of _Cpx_ since its deletion using CRISPR-Cas9 (Supplementary Figure S3D) resulted in the downregulation of _Cpx_ in both undifferentiated (UMEL) and differentiated MEL (iMEL) cells (Supplementary Figure S3E,F). Together, these results show that _CpxexRNA_, the eRNA itself, regulates the expression of _Cpx_. There is a CTCF/cohesin binding site (CBS) located upstream of _CpxexRNA_ (Supplementary Figure S3A). We next asked whether this CBS site plays any role in regulating the activation of _Cpx_ by _CpxexRNA_. We performed deletion of this CBS site using CRISPR-Cas9 (Supplementary Figure S4A,B). In undifferentiated MEL cells (UMEL), the loss of the CBS did not affect _Cpx_ expression in one clone and upregulated _Cpx_ expression in another clone (Supplementary Figure S4C). However, _Cpx_ was upregulated in differentiated MEL cells (iMELs) with the CBS deletion in both clones compared to control cells (Supplementary Figure S4D). These results suggest that the induced expression of _Cpx_ is at least partially antagonized by CTCF binding between its promoter and the _CpxexRNA_ genomic location, where it may function as an insulator. Given that _CpxexRNA_ interacts with TAD boundaries, that the left-side TAD boundary is anchored by the CTCF/cohesin complex, and that there is also a CTCF/cohesin binding site at the _CpxexRNA_ locus (Figure 3C), we next examined whether _CpxexRNA_ can associate with the CTCF/cohesin complex. RNA pull-down and fCLiP-RT-qPCR experiments confirmed that _CpxexRNA_ interacts with the CTCF/cohesin complex (Figure 3D and Supplementary Figure S5A). Following _CpxexRNA_ knockdown, the protein levels of CTCF and RAD21 remained unaffected (Supplementary Figure S5B). However, RAD21 binding at the _CpxexRNA_ genomic locus increased rather than decreased (Supplementary Figure S5C). This suggests that _CpxexRNA_ and its genomic locus compete for RAD21 binding. Upon _CpxexRNA_ depletion, RAD21 can accumulate at its genomic locus. These data support that _CpxexRNA_, per se, is important for chromatin organization at the _CpxexRNA_/_Cpx_ locus. This also indicates that some of the chromatin loops mediated by CTCF/cohesin depend on RNA.
# The Impact of Pitch Error on the Dynamics and Transmission Error of Gear Drives
## 1 Introduction
(2021) observed that a spur gear pair with slight pitch errors exhibited higher TE amplitudes and reduced mesh stability, especially under multi-tooth contact conditions [14]. Notably, pitch errors appear to excite a more adverse response than certain profile errors; for instance, one study found that pitch error had a greater impact on increasing transmission error (and associated contact pressures) than a comparable profile slope deviation did. Broadly, such pitch-derived disturbances act as potent excitation sources in gear systems, modulating the gear mesh frequencies and often generating sideband noise components in the acoustic spectrum. A recent comprehensive review by [PERSON] et al. (2025) [15] further underscored that manufacturing tolerances (including tooth spacing errors) can significantly affect gear NVH performance, and it emphasized the importance of accounting for these errors in robust gear design and optimization. Collectively, these findings make it clear that minimizing pitch error is essential for improving the dynamic behavior and acoustic performance of gear drives, as pitch error directly exacerbates transmission error excitation and the resulting vibration and noise in geared systems [15]. This study differs from previous work by incorporating measured individual flank topologies and calibrated pitch error maps into a multibody simulation framework, enabling a more accurate prediction of transmission error and tonal noise components.
## 2 Methods
### The Overall Measure of the Radar Data Quality  
The overall QI of each radar bin is estimated in a multiplicative form, using the individual specific QIs (Appendix A):  
\[qi_{i}=\prod_{j=1}^{n}qi_{j,i} \tag{1}\]  
where \(qi_{i}\) is the overall QI of the target bin from the \(i\)-th radar, \(qi_{j,i}\) is the \(j\)-th specific QI of the target bin from the \(i\)-th radar, and \(n\) is the total number of the specific QIs. The final value of the radar product is then estimated as a QI-weighted average from all available radars at the given point:  
\[z=\frac{\sum_{i=1}^{N}qi_{i}z_{i}}{\sum_{i=1}^{N}qi_{i}} \tag{2}\]  
where \(z\) is the value of the resulting radar product, \(z_{i}\) is the corresponding value from the \(i\)-th radar, and \(N\) is the number of radars.  
Using a probabilistic approach, one can estimate the overall QI of all radars at the given point. This is simply expressed as 1.0 minus the probability of a bad measurement at the given point:  
\[qi=1.0-P^{\prime}=1.0-\prod_{i=1}^{N}{P^{\prime}}_{i}=1.0-\prod_{i=1}^{N}(1.0- qi_{i}) \tag{3}\]  
where \(P^{\prime}\) and \({P^{\prime}}_{i}\) are the probabilities of bad measurement for the resulting product and for the \(i\)-th radar, respectively.  
The method of using the resulting QI also depends on the target radar product. Currently, no volume filtering is adopted, i.e., the individual specific QIs are combined into the overall QI according to Equations (1) and (3). We could, in principle, introduce volume filtering by defining \"no-data\" in the case of QI not exceeding a pre-defined threshold. Nevertheless, different methods of combination of the specific QIs and the volume filtration methods have not been studied so far and need to be tested in the light of the chosen target radar product.
## 3 Multiple-Access Reservation Receiver Initiation MAC (MAR-RiMAC)
### Multi-Hop Topologies  
Throughout this paper, we concentrate on a single-hop topology, in which a sink collects data from many devices. The suggested mechanism can be applied to multiple-hop topologies, in which data from different devices need to travel for multiple hops before reaching the destination. Recall that RI-MAC was designed to cope with multiple-hop topologies; accordingly, the procedure suggested for a single sink collecting data directly from many devices can be applied to each relay along the way, which collects data that need to be relayed from devices or from other relays. For this procedure to work, each device or relay needs to be assigned a sequence that identifies it. Preferably, the sequences should be distributed such that there are no conflicts in a neighborhood, i.e., each device and relay in the vicinity of a relay (so that the relay can receive its signal) is assigned a unique sequence. Reuse of sequences outside a neighborhood does not pose a problem. If there are no sufficient sequences to share uniquely within a neighborhood, and different devices share the same sequence within a neighborhood, there can be conflicts that need to be resolved. For example, consider two devices (or relays) sharing the same sequence that are within communication range of a relay, yet one needs to communicate with the relay while the other needs to communicate with a different relay. In the unlikely event that the relay receives an identity sequence from the device (or relay) not associated with it (which is unlikely, as it requires two relays to send inviting RtRs at the same time), the receiving relay needs to take into account that the polling message inviting the device to transmit will not be reciprocated.  
As before, the multi-hop MAR-RiMAC enhancement is more appropriate in a data collection application in which a sink collects data from numerous devices, some of which are several hops away. A topology in which devices communicate with different devices is less suited for the MAR-RiMAC enhancement, as the likelihood that multiple devices will try to communicate with the same device is low; hence, the probability for collision between devices trying to communicate with the same device is low. In any case, it should be emphasized that MAR-RiMAC is transparent to the routing protocol and can support any routing protocol, or at least any routing protocol that relies on a receiver-initiated protocol.  
Downstream traffic can be incorporated with the upstream traffic by utilizing the same procedure as the four-way handshake suggested by MAR-RiMAC. However, if the overlay routing topology is a tree topology, in which each device or relay receives data from only one relay, downstream traffic can be handled only via a two-way handshake, similarly to RI-MAC.
# A 3D-Printed Enclosed Twist Dielectric Resonator Antenna with Circular Polarization
## 2 Dielectric Resonator Antenna Design and Parametric Studies
### Final Antenna Design  
The final design of this antenna is a twist structure that will be created with four rectangles: a structure that will be responsible for changing the behavior of the fields, creating two orthogonal modes in the structure which allows for having CP. A schematic of the antenna and a detail of the feeding network are shown in Figure 8.  
The twisted DRA is implemented using a relative permittivity of \(\varepsilon_{r1}=13\), which corresponds to the a measured relative permittivity of the filament ABS1500 from AVIENT using our printers with a 100% infill. The dimensions of the twist antenna are a height  
Figure 4: Reflection coefficient \(|S_{11}|\) as a function of the frequency for the twist implementations.  
Figure 5: Axial ratio as a function of the frequency for the twist implementations for different rotation angles.  
of \(h_{DRA}=24\) mm, a width of \(w_{DRA}=7\) mm, and a length of \(l_{DRA}=17\) mm, while the top part of the DRA is rotated \(\theta_{r}=~{}60^{\circ}\) to achieve Left Handed Circular Polarization (LHCP). The dimensions of the antenna are set to radiate at 5.8 GHz.  
Figure 8: Schematic of the proposed antenna. (**a**) Twist DRA with enclosure. (**b**) Feeding network detail.  
Figure 6: Schematic of the proposed antenna twist with box.  
Figure 7: Parametric study of the twist with box using different lateral dimensions. (**a**) Reflection coefficient \(|S_{11}|\) as a function of the frequency. (**b**) Axial ratio as a function of the frequency.  
As this antenna is intended for UAV applications, it is important to make it as robust as possible in terms of mechanical stability. For that, the twist antenna itself can be hard to implement as a stand-alone, as it will need an additional supporting structure. For that purpose, a box that completely encloses the twisted DRA is designed, which affects the original parameters as little as possible in terms of the radiation of the antenna. For this box, a relative permittivity of \(\varepsilon_{r2}=2\) is used. This permittivity will be achieved by varying the infill percentage of the filament [24], in order to make just a one-piece implementation. The lateral dimensions of the box are a height \(h_{box}=25\) mm, a width \(w_{box}=19\) mm and a length \(l_{box}=19\) mm.  
Finally, for the feeding network, an aperture slot-coupling is used. The substrate used is a ROGERS RO4003 (\(\varepsilon_{r}=3.38\), \(\tan\delta=0.0027\)) with a height of \(h_{sub}=1.52\) mm. The slot dimensions are a width \(w_{slot}=9\) mm and a length of \(l_{slot}=4\) mm. Finally, the 50 \(\Omega\) microstrip line used for the coupling has a width of \(W_{ms}=3.4\) mm and a stub length of \(l_{stub}=7\) mm.
## 2 PV Modeling and Problem Formulation
### The Model of a Solar Cell
#### 2.1.2 Ddm  
Although it is widely employed to simulate PV cells, the SDM ignores the recombination current in the depletion region. As shown in Figure 1b, by combining the photo-generated current source, the shunt resistance, two rectifying diodes, and the series resistance, the DDM can solve the problem.  
Figure 1: Equivalent representation of the (**a**) SDM, (**b**) DDM, and (**c**) PVMM.  
Using KCL, one can calculate the output current in a DDM as follows:  
\[I_{o}=I_{p}-(I_{sh}+I_{D2}+I_{D1}) \tag{6}\]  
\[I_{D1}=I_{1}\left[\exp\left(\frac{V_{o}+R_{s}I_{o}}{u_{1}v}\right)-1\right] \tag{7}\]  
\[I_{D2}=I_{2}\left[\exp\left(\frac{V_{o}+R_{s}I_{o}}{u_{2}v}\right)-1\right] \tag{8}\]  
Current flows through the first and second diodes (i.e., \(I_{D1}\) and \(I_{D2}\), respectively) as described by the Shockley diode equations in Equations (7) and (8). Diodes also have two ideality factors known as \(u_{1}\) and \(u_{2}\). The diffusion and saturation currents are \(I_{1}\) and \(I_{2}\), respectively. Thus, by substituting Equations (3), (7), and (8), Equation (6) can be rewritten as follows:  
\[I_{o}=I_{p}-\frac{V_{o}+R_{s}I_{o}}{R_{sh}}-I_{2}\left[\exp\left(\frac{V_{o}+R_ {s}I_{o}}{u_{2}v}\right)-1\right]-I_{1}\left[\exp\left(\frac{V_{o}+R_{s}I_{o}} {u_{1}v}\right)-1\right] \tag{9}\]
# Ultra-Wideband Compact Fractal Antenna for WiMAX, WLAN, C and X Band Applications
## 2 Antenna Design
### Effect of the position Lg1  
Figure 6 shows the parametric analysis of WL. It is noticeable that the modification of 'WL' has no effect on the first frequency band ([2.30, 4.10] GHz). While it has a major effect on the second band, it moves away from the proposed value. In conclusion, to provide wideband operation, excellent impedance, low prices, and compact size to cover the latest wireless communication systems, the proposed value of WL = 17 mm is the best choice.  
Figure 7 illustrates the surface current distribution of the suggested antenna at 2.8 GHz, 3.51 GHz, 6.53 GHz, and 9.37 GHz simulated by HFSS. The surface current distribution of a fractal antenna can be characterized by its multifractal properties, where the current density varies at different points on the antenna surface. The self-similar nature of fractal antennas results in a uniform distribution of the surface current over the antenna structure, which helps to reduce the inhomogeneity of the electromagnetic field. Moreover, the fractal nature of these antennas allows the creation of multiple resonant frequencies, which makes them suitable for multi-band applications. The surface current distribution of a fractal antenna is critical in determining its radiation properties, such as directionality and polarization, and can be optimized for specific performance requirements. This analysis is performed to understand the wideband operation of the patch. In Figure 7a,b,d, it is observed that the current distribution is mostly concentrated at the slot of the partial ground, feed line, and the lower edge of the patch. This is because the slots create additional paths for current flow and enhance the current density in those regions. At a frequency of 3.51 GHz, the current is also distributed at the 'L' shape of the ground, which causes the wideband properties of the antenna. The 'L' shape creates an additional resonator that adds to the existing ones, thereby increasing the bandwidth of the antenna. When the slots are integrated into the antenna, the main current flow splits and creates new paths. This causes the antenna to become electrically larger in size and creates different resonators, which subsequently leads to the wideband operation of the antenna. The wideband operation is due to the presence of multiple resonant modes that contribute to the radiation of the antenna over a wide range of frequencies.  
Figure 6: Parametric analysis (WL).
# Discerning Reality through Haze: An Image Dehazing Network Based on Multi-Feature Fusion
## 3 Multi-Feature Fusion Image Dehazing Network  
Deep-learning models are essentially comprised of a complex set of nonlinear functions, with the core objective being the gradual optimization of model parameters to minimize the value of the loss function. The task of image dehazing is formalized as an optimization problem of a nonlinear function \(F\). Through the analysis of a vast array of data samples, a set of optimal parameters is sought. These parameters are capable of effectively transforming hazy images \(X\in\mathbb{R}^{H\times W\times C}\) into their corresponding clear images \(Y\in\mathbb{R}^{H\times W\times C}\), where \(C\) is the number of channels in the image and \(H\) and \(W\) denote the height and width of the image, respectively. This problem is modeled in the following form in this paper:  
\[\theta^{*}=argmin\_\theta L(F(X;\theta),Y) \tag{4}\]  
where \(\theta^{*}\) is the set of parameters that minimize the loss function, \(F(X;\theta)\) is the predicted output of the haze-free image by the function \(F\) when given the parameter set \(\theta\), and \(L\) is the loss function, utilized to quantify the difference between the predicted haze-free image and the actual haze-free image.  
Based on the FFA-Net architecture, we propose the multi-feature fusion image dehazing network (MFID-Net) as the prediction function \(F\) to approximate the clear image \(Y\) with high fidelity; the differences between FFA-Net and MFID-Net are illustrates in Figure 2. In Figure 3, MFID-Net takes the \(X\in\mathbb{R}^{H\times W\times C}\) as input and extracts shallow features through a convolution operation layer. Subsequently, these features are fed into three consecutive Group Blocks for further feature extraction. Depth fusion across different levels is achieved through residual connections, resulting in the feature map \(g(X)\), a process detailed in Equation (5). Specifically, each Group Block is composed of 15 multi-feature fusion (MF) modules and a convolutional layer, with residual connections also employed within the Group Block to enhance the transmission and fusion of features. Next, lightweight gated channel attention (LGCA) and gated pixel attention (GPA) are utilized to re-calibrate the features at the channel and pixel levels to enhance the dehazing effect.  
Finally, the predicted haze-free image \(F(X)\in\mathbb{R}^{H\times W\times C}\) is generated through two convolution operations, a process that can be represented by Equation (6).  
\[g(X)=Concat(g_{1}(Conv(X)),g_{2}(g_{1}(Conv(X))),g_{3}(g_{2}(g_{1}(Conv(X))))) \tag{5}\]  
\[F(X)=Conv(Conv(GPA(LGCA(g(X)))))+X \tag{6}\]  
where \(Conv\) is a convolution operation that is characterized by a kernel size of 3 and a stride of 1. \(g_{i}\) is the Group Block that maintains a constant channel size during computation, \(i=1,2,3\). \(LGCA\) and \(GPA\) represent the lightweight gated channel attention mechanism and gated pixel attention mechanism proposed in MFID-Net, respectively, which are components of the MF module.
# Application of Machine Learning in Electromagnetics: Mini-Review
## 3 ML and EM
### Fault Detection Systems  
The failure of elements in an antenna array causes sharp field variations across the array aperture, thus degrading the radiation pattern of the antenna system. Therefore, identifying defective elements is vital for correcting array failures. The Woodward-Lawson method [125], Case-Based Reasoning systems [126], GA [127], and ML techniques have been used earlier to locate the fault element(s) in an antenna array [128]. [PERSON] et al. [129] used an ANN to locate a maximum of three defective elements in a 16-element array, from its distorted radiation pattern. The results were in excellent agreement with the simulation results. In another study [130], the SVM classifier was used to detect defective elements in a four-element array, using a different SVM for each combination of defective elements. However, this approach was not feasible with moderate or large arrays, as the number of possible combinations increased. The training and detection of the SVM were conducted on measured radiation data with a signal-to-noise ratio (SNR) in the range of 0-25 dB. In shipborne antennas, it is arduous to detect faults due to the unfriendly working environment and loud background noises, so DL can play an important role byusing multiscale analysis with a multi-layered network [131]. EM can be used in power transmission anchor rods to detect faults. The procedure is noninvasive and nondestructive due to the absence of physical human touch. However, the acquired signal is complex and ML techniques must be used to recognize the pattern of the radiation and thus detect anomalies [132]. The uncertainty of sudden antenna failure in textile applications was characterized using a Bayesian optimization-adopted possibility theory in a prior article [133]. A defect in the base station often creates sleeping cells resulting in network failures that are hard to detect. The naive bayes method provided excellent results in detecting this kind of failure with precision [134].
# A Tale of Many Networks: Splitting and Merging of Chord-like Overlays in Partitioned Networks
## 1 Introduction
### Characteristics of Network Partitioning  
As we know, autonomous systems (ASs) in the Internet are connected by Internet exchange points (IXPs) and controlled by different Internet service providers (ISPs). Although a geographical region holds no information about the topology of the Internet, we use the term region to refer to separated parts of the Internet. Using this term, we express the affiliation of an autonomous system, controlled by an ISP, to a country, a government, and other geographical or political regions. In this context, we denote the participants of an overlay which are able to communicate with each other after a network partition as part of a group.  
While the separation of single nodes in peer-to-peer networks, i.e., churn, is a common challenge that researchers address regularly, the separation of whole groups is rarely considered. This happens when a set of nodes, which is associated with an autonomous system in the Internet, is separated from the existing overlay. This scenario could occur if all failing nodes are assigned to the same administrative domain which departs from the common network, e.g., if an autonomous system or a corresponding geographical region is suddenly disconnected from the Internet. Next, we discuss the two specific characteristics of overlay partitioning.  
When two regions are isolated from each other and no routing between them is possible, it may be that more than two (Chord) rings will arise after a certain stabilize period. Although nodes of individual rings in the same region might be connected on network level, they could be incoherent in the overlay. Hence, during isolation, only those nodes are formed to a group, which are represented in the same set of finger entries and successor or predecessor pointers. Nevertheless, if the separated regions are reconnected again on network level, and both regions are capable of routing to each other, both regions will not converge to a common ring. The reason for this behavior is that no node in any separated overlay maintains any routing information about at least one contact node in the other separated overlay. Consequently, routing in a global overlay with both regions as participants is impossible. Both groups will remain independent from each other. In Figure 1, two connected regions are shown. In Figure 2, one region is isolated so that several rings are formed.  
The functionality to merge other rings is not given in the original Chord and many related overlays. This functionality is highly desired as it would allow to use overlays in a broad range of scenarios, ranging from robust infrastructures to opportunistic networks.