esa-sceva/satcom-chunk-collection huggingface dataset:
# AxP: A HW-SW Co-Design Pipeline for Energy-Efficient Approximated ConvNets via Associative Matching
## 3 Co-Design Pipeline: Concept and Methodology
### Software Design  
To enable such associative-based implementation, we introduce a co-design framework depicted in Figure 4. It consists of two main stages: an _Optimization Stage_ and a _Simulation Stage_. The former searches for possible candidates of the memory and model configurations that satisfy the user-defined constraints. The latter does a test over the relevant configurations elaborated in the previous stage emulating the inference stage on the designed PE, providing the weight-set of the clustered ConvNet and the corresponding energy-efficient associative memory settings as main outcome. The _Optimization Stage_ is the core of the framework. It has two principal components: (i) the _Approximation Pipeline (AxP)_ that performs an iterative dual-step approximation based on a clustering & approximate flow applied on the ConvNet model; (ii) the _Activation Pattern Profiling (APP)_ stage that works in parallel extracting relevant statistics on the activation maps produced by the inner processing layers of the ConvNet model. The following sections provide a more detailed description of these components.
# On the Fidelity of NS-3 Simulations of Wireless Multipath TCP Connections
## 5 Simulation Model
### Simulator Parameters and Configuration
The breakpoint distance is defined as  
\[R_{bp}=(4\times h1\times h2)/\lambda \tag{12}\]  
The overall loss is given by  
\[Loss_{11411}=(Loss_{low}+Loss_{up})/2 \tag{13}\]  
where the loss upper bound (\(Loss_{up}\)) and loss lower bound (\(Loss_{low}\)) can be calculated  
If \(d<=R_{bp}\), as  
\[Loss_{low}=L_{bp}+20\times log_{10}(dist/R_{bp}) \tag{14}\]  
\[Loss_{up}=L_{bp}+20+25\times log_{10}(dist/R_{bp}) \tag{15}\]  
If \(d>R_{bp}\), it can be obtained from  
\[Loss_{low}=L_{bp}+40\times log_{10}(dist/R_{bp}) \tag{16}\]  
\[Loss_{up}=L_{bp}+20+40\times log_{10}(dist/R_{bp}) \tag{17}\]  
The parameter \(d\) refers to the distance between the node and the enodeB. The total loss incurred by the hybrid building propagation loss model is given by  
\[Loss_{total}=Loss_{11411}+EWL+HL \tag{18}\]  
The external wall loss (\(EWL\)) in a concrete-walled building with windows is set to a constant value of 7 dBm in ns-3. Alternately, the height loss (\(HL\)) is zero as a single-floor building is considered. The Wi-Fi network of the testbed is simulated using the yet another network simulator (Yans) [42] Wi-Fi model of ns-3. The propagation loss is based on the log distance model given by  
Figure 4:0  
where \(L\) is the path loss (dB), \(n\) is the path loss exponent, and \(d\) is the distance (m). The reference path loss \(L_{0}\) is set to 46.6777 dB, and the reference distance \(d_{0}\) is kept as 1 m.  
Table 2 shows that the parameters such as frequency, rate manager, and channel width, among others, are configured with values similar to the testbed Wi-Fi configuration. For instance,  
by employing the AarfWifiManager, the Wi-Fi channel access rate is changed adaptively the same way as in the hardware testbed. The Wi-Fi power-related parameters such as TX/RX gain, TX power levels, RX noise figure, and RX sensitivity are adjusted in a way that makes the reported RSSI (receiver signal strength indicator) at the access point and nodes' Wi-Fi interfaces in the simulator and testbed matching one another. This leads to a similar average channel access rate in the simulator and testbed.
## 3 System Model and Problem Formation
### Assumptions and Calculations
#### 3.3.3 Residual Link Capacity  
Channels are time-slotted and multiple users can share a single channel. Thus, the residual capacity of any link/channel can be calculated by subtracting the total aggregated used capacity from its total assigned capacity given as below:  
\[RC_{e_{i,j}}=CC_{e_{i,j}}-\sum_{f_{o}=1}^{r_{e_{i,j}}^{\prime}}f_{o} \tag{1}\]
# A Survey of Planning and Learning in Games
## Planning and Learning in Games
### Planning applied to Learning
#### 5.1.1 Speeding-up Learning  
Reward Shaping  
Learning techniques such as RL do not make any assumptions about the environment in which they must operate or, in other words, they are not given any prior domain knowledge. However, as the state (and or action) space of the problem increases, the learning process may become much slower and even fail to learn the task within a practical time frame [21]. A reason for this is that during exploration RL may spend too much time performing suboptimal steps since it is not given any guidance on where to explore. Knowledge-based RL (KBRL) addresses this issue by incorporating domain knowledge into RL as a way to guide the learning process away from suboptimal regions of the state space and towards more promising ones in order to find the optimal policy more quickly.  
Work in the field of KBRL has demonstrated that reward shaping, the process of using additional rewards as a way of providing prior knowledge to the learning process, can significantly speed-up the learning process [21]. Of particular interest to the discussion is plan-based reward shaping, an instance of KBRL in which prior knowledge is encoded as a high-level STRIPS plan used to derive the additional rewards that will help guide the learning process. Examples of applications of plan-based reward shaping include research focused on scaling RL to more complex scenarios, such as macromanagement in the RTS game StarCraft: Broodwar [21], and on improving the convergence speed and optimality (i.e., the quality of the policies obtained) of RL techniques such as Sarsa and Dyna-Q [227].
## 1 Introduction
These genetic variations, including the 80A>G SNP, may increase susceptibility to pregnancy loss in certain populations [15; 16]. Folate transport from the maternal intestine to the embryo depends on both transporters and folate receptors (FRs) [9]. The FR family comprises four isoforms: _FR\(\kappa\)_ (257 aa, 30 kDa), _FR\(\beta\)_ (255 aa, 29 kDa), _FR\(\gamma\)_ (245 aa, 28 kDa), and _FR\(\delta\)_ (250 aa, 28.6 kDa), encoded by the genes _FOLR1_ (Gene ID: 2348), _FOLR2_ (Gene ID: 2350), _FOLR3_ (Gene ID: 2352), and _FOLR4_ (Gene ID: 390243), respectively. These proteins, also called folate-binding proteins (FBPs), bind folic acid, 5-mTHF, and folate-conjugated compounds with high affinity, facilitate cellular uptake via receptor-mediated endocytosis. _FR\(\alpha\)_, _FR\(\beta\)_, and _FR\(\delta\)_ are GPI-anchored membrane proteins, whereas _FR\(\gamma\)_ lacks the GPI anchor and is secreted. Among these isoforms, _FR\(\alpha\)_, encoded by _FOLR1_, is the most extensively studied [17; 18; 19]. Research in mice highlights FOLRI's critical role during early development, as Folr1 knockout embryos exhibit severe defects and do not survive [20]. The _FOLR1_ gene spans 6.7 kb and contains seven exons [21]. The human folate receptor genes and _FOLH1_ (Gene ID: 2346), which encodes glutamate carboxopeptidase II (GCPII, EC 3.4.17.21), are located on chromosome 11. GCPII facilitates folate absorption by converting folylpolyglutamate into folymonoglutamate. The C\(>\)T variant (rs61886492) in _FOLH1_, resulting in the substitution of histidine with tyrosine at position 475 (H475Y), reduces GCPII activity, leading to lower blood folate levels and elevated homocysteine concentrations. Intracellular folate homeostasis is regulated by the combined actions of two key enzymes: folylpolyglutamate synthase (FPSS, Gene ID: 2356) and gamma-glutamyl hydrolase (GGH, Gene ID: 8836). These enzymes work together to ensure proper intracellular folate concentration and distribution. FPSS retains folates within cells by adding glutamate residues, while GGH (EC 3.4.19.9) breaks down polyglutamylated folates into monoglu-tamylated forms, facilitating their export from the cell [22]. A variant in the _GGH_ gene (rs3758149 C allele) has been shown to increase transcriptional activity and enhance binding affinity for nuclear proteins in the CEM/C1 T lymphoblast cell line when compared to the T allele [23]. Therefore, this study investigated the relationships between the frequency of selected gene variants related to folate uptake and distribution (_FOLR1_ rs2071010, rs630074, _FOLH1_ rs61886492, _GGH_ rs11545078, rs3758149 and _SLC19A1_ rs1051266) in a group of women with RM and IUFD in the Polish population (Figure 1).
# A Computational Platform for Automatic Signal Processing for Bender Element Sensors +
## Appendix A Porodynamic Coefficients  
As mentioned in Section 3.2.3, the material model implemented in the beta release of GeoHyTE is based on [PERSON]'s theory. While some of the the material properties present in this model are well known ([PERSON]'s modulus, [PERSON]'s coefficient and acceleration of gravity), others may require further explanation for analysts less acquainted with [PERSON]'s theory. To support the straightforward use of GeoHyTE, the material properties specific to porous media and needed for the definition of the model (GUI 2, see Figure 4) are reviewedhere. However, some intricacies of [PERSON]'s theory are avoided by adopting two simplifying hypotheses, valid for most cohesionless geomaterials, as detailed below.  
The porous material consists of a solid phase with grain density \(\rho_{s}\) and permeability \(\kappa\), permeated by a fluid phase with density \(\rho_{w}\) and bulk modulus \(K_{w}\). The material properties that need to be input in GUI 2 are as follows:  
\begin{tabular}{l l} \(\rho_{w}\) & fluid density; \\ \(n^{w}\) & fluid volume fraction, the ratio between the volume of fluid in the pores and the total volume of the material; \\ \(\rho\) & mixture density, \(\rho=\rho_{s}(1-n^{w})+\rho_{w}n^{w}\); \\ \(k\) & hydraulic conductivity, \(k=\kappa\,\rho_{w}\,g\), where \(g\) is the acceleration of gravity; \\ \(a\) & the tortuosity factor, measuring the tortuosity of the pore space. In cohesionless geomaterials, it is commonly taken as equal to unity, meaning that the fluid is able to flow in nearly straight lines; \\ \(\alpha\) & Biot's coefficient, \(\alpha=1-\frac{K_{s}}{K_{s}}\), where \(K_{s}\) is the bulk modulus of the solid grain and \(K_{d}\) is the bulk modulus of the dry porous material. A common simplifying hypothesis is to assume that the solid grain is much less compressible than the dry porous material, meaning that \(K_{s}>>K_{d}\) and thus \(\alpha=1\); \\ \(M\) & Biot's modulus, the pressure needed to force the fluid phase into the porous medium without changing the volume. Under the assumption that leads to setting \(\alpha=1\), Biot's modulus can be computed as \(M=\frac{K_{w}}{n^{\alpha}}\). \\ \end{tabular}
## 1 Introduction
Unlike traditional metallic substrates, polymers are non-conductive, requiring specialized pre-treatment or primary metallization (PM) methods before electroplating. These metallization processes have seen improvements recently that allow for advancements in combining electrodeposition with 3D-printed mandrels to achieve solid, freeform, metallic parts [16; 17]. In this review, the fundamental electrochemical concepts of electrodeposition of nickel are explored, with a focus on the advancements in electroplating techniques used for polymeric substrates. Specifically, this paper delves into the properties of the Watts electrolyte bath due to its foundational role in most modern nickel electrodeposition processes. We present an overview of key mechanical properties (such as hardness, ductility, and tensile strength) of electrodeposited nickel onto non-conductive polymers, due to their ubiquitous nature. We also discuss how these properties may be tailored to be used in advanced structural applications, where traditional manufacturing methods are costly and time-taking. These novel methods open new avenues for rapid prototyping and cost-effective medium run production.
## 2 Actuator Structure and Fabrication
### Consideration of Actuator Design Parameters
#### 2.2.1 Whole-Finger Thumb Actuator: Three-Chamber Actuator  
The 3C-ACT has the same geometrical structure and dimensions as the semicylindrical three-chamber actuator presented in [27] (Figure 1a). The middle chamber for thumb flexion was tapering up, with the maximum cross section area at the tip and the minimum at the root, matching the maximum flexion RoM requirement at the IP joint and the minimum at the CMC joint. The two side-chambers for abduction-adduction were in a contrary structure (tapering down) because thumb abduction only occurs at the CMC joint. Reinforcement fiber was coiled in two patterns: 21 loops of two-way hitching at the tip to facilitate deformation in flexion direction and 13 sets of double-helix at rest for permitting both abduction and flexion.
###### Abstract  
With the increasing number of Connected and Autonomous Vehicles (CAVs), the heterogeneous traffic flow on highways now consists of a mix of CAVs and Non-networked Autonomous Vehicles (NAVs). The current deployment of Roadside Units (RSUs) on highways is mostly based on uniform or hotspot locations. However, when the permeability of CAVs on the road varies, the communication network may face challenges such as excessive energy consumption due to closely spaced RSU deployments at high CAV permeability or communication interruptions due to widely spaced RSU deployments at low CAV permeability. To address this issue, this paper proposes an improved D-LEACH clustering algorithm based on vehicle clustering: analyzes the impact of RSU and vehicle communication radius, mixed traffic density, and different CAV permeabilities in the heterogeneous traffic flow on the RSU deployment interval; and calculates the rational and effective RSU deployment interval schemes under different CAV permeabilities on highways in the heterogeneous traffic flow. When the heterogeneous traffic flow density is stable and CAV continues to penetrate, the RSU communication radius and deployment interval can be adjusted to ensure that the network connectivity is maintained at a high level. When the RSU and vehicle communication radius are stable, the mixed traffic density is 0.05, and the CAV permeability is 0.2, the RSU deployment interval can be set to 1235 m; when the mixed traffic density is 0.08 and the CAV penetration rate is 0.7, the RSU deployment interval can be set to 1669 m to ensure that the network connectivity is maintained at a high level.  
heterogeneous traffic flow; vehicle clustering; roadside unit deployment; D-LEACH algorithm 2023  
15  
11112
## 2 Results
### RNA-Seq Analysis  
To further determine the molecular basis of the phenotypes observed in R-HJH, global gene expression profiling of R-HJH albedo compared with the wild-type HJH albedo was carried out. The number of raw reads ranged from 41,725,292 to 52,446,264 per cDNA library. A total of 41.30-51.95 million clean reads were obtained after stringent quality checks and data cleanup. The percentages of clean reads and Q20 reads were more than 98% and 97%, respectively. A total of 86.5-90.4% of the clean reads were mapped uniquely to the _C. grandis_ reference genome, and only a small proportion of them (2.37-2.88%) mapped multiple regions on the genome (Supplementary Table S1). These data indicated that the read number and sequencing quality were sufficient for further analysis. A total of 14,852 and 14,739 genes were expressed (average TPM \(\geq\) 1) in R-HJH and wild-type HJH, respectively (Supplementary Figure S2). [PERSON]'s correlation coefficients among the three biological replicates were high (R2 > 0.99). To identify differentially expressed genes (DEGs) between these two samples, DEG screening was conducted based on a threshold of two-fold change (FDR < 0.05). Compared to HJH, 3948 DEGs were identified in R-HJH, with 1668 upregulated and 2280 downregulated genes (Supplementary Table S2). The number of downregulated genes was higher than the number of upregulated genes. To obtain the functional information for the DEGs and the related biological processes they participate in, GO and KEGG enrichment analyses were conducted. A total of 70.14% (2769) of the unigenes were identified and annotated to GO terms (Supplementary Table S3), and 37.03% (1462) of them were assigned to KEGG pathways (Supplementary Table S4). Based on their GO classifications, DEGs were grouped into three functional GO categories (Figure 2). The most significantly enriched GO terms in the biological process category were related to metabolic processes and cellular processes. For molecular function, catalytic activity was the most highly represented GO term, followed by binding. In the cellular component category, most DEGs were enriched in four categories: cell part, membrane part, membrane and organelle.
## 4 Experimental Research  
In order to verify the effectiveness of the MSPDDE algorithm in this paper, the following firstly gives the information about the experimental environment and parameter settings, then introduces the experimental cases and experimental statistical methods, and finally evaluates the performance of the MSPDDE algorithm and conducts ablation experiments for the pool of mutation strategies and probabilistic discretization strategy.
## 2 Very Compact Waveguide OMT
### Design Description
For test purposes, the conical horn design in [24] is reused here, thus constraining the cross-section of the common waveguide at the interface relative to the conical horn (square waveguide of side \(a=9.5\,\mathrm{mm}\)), which is labeled port 3 in Figure 1. The horn comprises a square-to-circular waveguide transition with a cylindrical section of \(12.5\,\mathrm{mm}\) in diameter and \(6.5\,\mathrm{mm}\) in length followed by a flare that is \(12\,\mathrm{mm}\) long, leading to a circular aperture of \(25\,\mathrm{mm}\) in diameter. The horn was designed to have a reflection coefficient below \(-30\,\mathrm{dB}\) over the band of interest and is, thus, expected to have little impact on the performance of the OMT. The two single-mode ports, labeled port 1 and port 2 in Figure 1, require a transition to the standard waveguide WR42 (\(10.668\,\mathrm{mm}\times 4.318\,\mathrm{mm}\)) for test purposes. This transition is simplified here by setting the side of the probing cross-section equal to the width of the WR42 standard waveguide. All design parameters are defined in Figure 1.  
A full-wave model was implemented in Ansys HFSS and optimized over the frequency band of \(17.3\)-\(20.2\,\mathrm{GHz}\), corresponding to the range allocated to broadband communication satellite's downlink in the K-band. The design targets include reflection and coupling coefficients below \(-20\) dB and an XPD that is higher than 30 dB over the operating band. The optimized values for all design parameters are reported in Table 1. These values take into account a milling radius of 1.1 mm applied to outer edges in the full-wave model in order to be representative of the manufactured design. The simulated reflection coefficients are better than \(-23.2\) dB over the operating band for port 1 and 2, while the simulated port-to-port coupling coefficient is below \(-23.3\) dB. The XPD is better than 30.3 dB over the nominal band.
# A Dynamic Risk Assessment and Mitigation Model
## 4 Methodology
### Dynamic Risk Assessment
#### 4.3.1 Dynamic Vulnerability Severity Calculator  
One of the core components of our methodology is the dynamic vulnerability severity calculator. At the heart of the dynamic vulnerability severity calculator lies the Fuzzy  
Figure 3: Multiple pathways—risk scenarios.  
Figure 2: Multiple vulnerabilities—risk scenarios.  
Cognitive Map (FCM), a powerful tool widely used for analyzing complex systems governed by causality. FCMs have been extensively applied across domains such as modeling, decision-making, analysis, prediction [31], and cybersecurity [32]. They offer a robust framework for representing relationships among various factors within a system.  
In our methodology, FCMs are employed to model dependencies among vulnerabilities within each risk scenario, specifically evaluating how the vulnerability values of one asset influences another. This application incorporates several key characteristics of FCMs, which have been adapted to meet the requirements of our methodology. These characteristics are outlined as follows:  
1. **Signed Causality:** In our model, signed causality captures the directional sequence of potential attacker movements, illustrating how an attacker might transition between assets by exploiting vulnerabilities. This analysis is based on the identification of all possible risk scenarios, which encompass detected vulnerabilities, the network topology, and the attacker's perspective.
2. **Weights of Relationships:** The weights correspond to normalized exploitability scores derived from CVSS metrics (module 1). For each detected CVE, the CVSS 3.x framework is used to extract the exploitability subscore, which is then normalized to a scale of 0-1 for compatibility with the FCM model (given the maximum original exploitability score of 3.9).
3. **Equilibrium Point:** The equilibrium point provides dynamic vulnerability severity values for all assets within the examined risk scenario, representing the system's stabilized state and offering a comprehensive assessment of vulnerability severity.