esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatially-Precise Video Editing with Reference Imitation: A Region-Aware Cross-Modal Framework
## 2 Related Works
### Diffusion for Video Editing  
In recent years, the diffusion-model-based video editing technology has been advancing at a remarkable pace. [PERSON] et al. [37] present a text-driven video editing method based on diffusion models. At its essence, this approach involves editing keyframes and then spreading the appearance alterations among them. Nevertheless, because of the limitations imposed by NLA [38; 39], this method may not be able to manage the learning process for non-rigid objects with evident structural deformations. [PERSON] et al. [40] maintain the consistency between frames by fine-tuning the pre-trained text-to-image diffusion model and introducing the temporal dimension. However, its local editing capacity is restricted. Precisely controlling the modifications of specific areas is challenging. AnyV2V [41] is a novel training-free framework. By leveraging off-the-shelf image editing models [42; 43], it modifies the first frame. After that, it uses an image-to-video generation model for DDIM inversion and feature injection. However, its ability to process videos with fast or complex motions is limited. I2 VEdit [21] also depends on the editing of the first frame and uses Motion LoRA [44] to extract the approximate motion. However, its strong reliance on the first-frame quality means that an unsatisfactory first frame may result in subpar subsequent outcomes. Unlike these methods, our goal is to enable the diffusion model to carry out local and refined editing while maintaining excellent temporal consistency, thus generating high-quality processed videos.
# myDIG: Personalized Illicit Domain-Specific Knowledge Discovery with No Programming
## 7 Discussion: Implications for Future Design  
Based on the results and analyses in the previous sections, we summarize some important implications uncovered over the course of testing myDIG in five different domains.  
_Automatic data acquisition is the new bottleneck._ Many, if not most, of the users expressed satisfaction with the facilities offered by myDIG and felt that the potential of myDIG would have been more fully realized if the quality of the data ingested had been better. Unfortunately, we had no control over this issue, although the DARPA MEMEX program used state-of-the-art data acquisition, relevance modeling, and crawling systems. One area of future work currently being considered is the integration of domain discovery and knowledge discovery. While such an integration would introduce new complexities and dependencies into the system, it could potentially improve data acquisition quality by incorporating richer user feedback into the domain discovery process.  
_Users should be empowered to specify their own vocabularies._ In the AI literature, there are powerful data models and representations for specifying fine-grained ontologies [11; 49; 50]. These have merits in certain fields, such as biomedicine, but are not suitable for users who do not explicitly deal with vocabularies, ontologies, or data models on a regular basis [51]. Instead, systems like myDIG allow users to crystallize their internal vocabularies by exploring the data and specifying fields as they go along; for example, many of our users interleaved Inferlink annotations while defining new fields. This methodology is in sharp contrast to the more traditional view of specifying a domain model upfront before data acquisition, processing, or modeling begins.  
_Provenance, going all the way to the original source, is key to fostering trust._ We are, by no means, the first to make this observation, but believe that it is important to (re-)state owing to the rapid progress in deep learning technologies for information extraction, without the same progress in interpreting or explaining the results. Users expressed trust in our system because of the provenance offered both at the web page and extraction levels. We note that, recently, the AI community has recognized this issue and is tackling the explainability problem with vigor.
# PDCA 4.0: A New Conceptual Approach for Continuous Improvement in the Industry 4.0 Paradigm
## 3 Industry 4.0 and Continuous Improvement
### Approaches towards the Applicability of I4.0 to LM/CI
#### 3.2.1 Conceptual Approaches  
Among the 23 publications on the conceptual application of I4.0 technologies to LM/CI found in the literature, 19 of them do not mention their applicability towards CI in a direct and structured way. After analyzing the content of these 23 papers, the findings were assigned according to their relevance to each of the CI activities. As an example, Sim&Virt, through augmented/assisted reality (AR/AsR), enables real-time remote support in manual operations [8; 12], relevant to the documentation management, implementation, and follow-up and standardization activities. The result is exposed in Tables A1 and A2 (Appendix B).  
From this study, it can be stated that IoT and Cloud have the potential to cover all CI activities, with IoT having the main role of enabling data transmission and access [9; 47], and Cloud the sharing of information [9; 12] and cloud-based data storage [12; 48], as well as cloud computing capabilities [35; 48; 49]. Sim&Virt is also quite overarching, allowing the use of AR/AsR to aid in manual operations [8; 12] and observe the current state of a process [11], and the use of virtual reality (VR) to facilitate training [8]. Additionally, simulation technologies can be used to test countermeasures before their real life implementation [12; 31; 50]. For this, a factory digital twin coupled with simulations can also be used for CI [51]. Big Data also possess a high degree of applicability, associated with data analytics [9; 12], predictive analysis [11; 12; 52; 53], data mining [53], correlation analysis [47], root cause analysis [11; 53], and machine learning [11; 43; 53], as well as advanced analytics for planning [8]. CPS, on the other hand, has relevancies to the collection and access to real-time data [9; 54], and can predict machine failures [52; 55]. AutRob, besides improving manufacturing flexibility and productivity [9] through the automation of routine tasks [11], also makes the automatic detection of machine failures possible [1; 9], as well as automated logistics systems [8; 9; 12]. It can also be used in collaboration with operators [9]. Sens&Act is a fundamental technological concept, as it is used to collect production data, including machine performance and object location [12]. MES is mainly used to collect data, as well as to display KPIs and data charts [56; 57]. On the other hand, eVC allows for the connectivity between stakeholders of the value chain and the information exchange along the supply chain, with on-demand access to value chain information through digital platforms [43]. 3 DP is essentially connected with mass customization [12] and smart product development [9], permitting the test of product designs.
## 3 Compensator Design
### Bus Voltage Control Loop Compensator Design  
Based on the small-signal model shown in Figure 3, the uncompensated bus voltage control loop gain (i.e., with gain = 1) is given by Equation (25), and its bode plot is represented by the dashed curve in Figure 8. With a compensator given by Equation (26), the compensated bus voltage loop gain  
Figure 7: The bode plot of uncompensated (red-dashed curve) and compensated (blue-solid curve) SOC balancing control loop.  
achieves a control bandwidth of 8.12 kHz and a phase margin of 52.3\({}^{\circ}\), as shown on the solid curve in Figure 8.  
\[L_{bus-uncomp}(z)=Z[ZOH_{v-bus}(s)\cdot Delay_{w}]=\frac{0.2555z-0.1798}{z^{2}-1.97 7z+0.9789}, \tag{25}\]  
\[G_{bus}(z)=\ \frac{1.2\text{z}-1.8}{\text{z}-1}, \tag{26}\]  
where \(ZOH_{v-bus}(s)=\ \frac{1-e^{-s\tau_{s}}}{s}\).
###### Abstract  
Low-light conditions often lead to severe degradation in image quality, impairing critical computer vision tasks in applications such as surveillance and mobile imaging. In this paper, we propose a lightweight deep learning framework for low-light image enhancement, designed to balance visual quality with computational efficiency, with potential for deployment in latency-sensitive and resource-constrained environments. The architecture builds upon a UNet-inspired encoder-decoder structure, enhanced with attention modules and trained using a combination of perceptual and structural loss functions. Our training strategy utilizes a hybrid dataset composed of both real low-light images and synthetically generated image pairs created through controlled exposure adjustment and noise modeling. Experimental results on benchmark datasets such as LOL and SID demonstrate that our model achieves a Peak Signal-to-Noise Ratio (PSNR) of up to 28.4 dB and a Structural Similarity Index (SSIM) of 0.88 while maintaining a small parameter footprint (~1.3 M) and low inference latency (~6 FPS on Jetson Nano). The proposed approach offers a promising solution for industrial applications such as real-time surveillance, mobile photography, and embedded vision systems.  
Keywords:low-light image enhancement; deep learning; lightweight neural networks; real-time image processing; UNet architecture; edge devices +
Footnote †: journal: Applied sciences
## 4 Performance Metrics
### Performance Metrics for State Switching and Energy Consumption  
The state switching rate \(\epsilon\) is the amount of times the CH state switches in a unit time slot, expressed as following two situations.  
1. If \(H=0\), \(\epsilon\) is expressed as follows. \[\epsilon{=}\pi_{0,0,0}(1-\bar{p}_{1}\bar{p}_{2}){+}\pi_{1,0,1}s_{2}\bar{p}_{1} \bar{p}_{2}+\pi_{1,1,1}s_{1}\bar{p}_{1}\bar{p}_{2}\] (27)If \(1\leq H\leq K\), \(\epsilon\) is expressed as follows.  
\[\epsilon{=}\sum_{x=0}^{H-1}\pi_{x,0,0}p_{1}+\pi_{H,0,0}\big{(}1-\bar{p}_{1}(\bar{ p}_{2}+p_{2}\frac{H}{\alpha})\big{)}{+}\pi_{1,0,1}s_{2}\bar{p}_{1}\bar{p}_{2}+ \pi_{1,1,1}s_{1}\bar{p}_{1}\bar{p}_{2} \tag{28}\]  
The definition of energy consumption \(E\) is the energy consumed by the CH per unit time slot. Considering that the state switching of the CH requires a certain amount of energy to complete, the energy consumption \(E\) of the CH is expressed in the following two situations.  
1. If \(H=0\), \(\rho\) is expressed as follows. \[\begin{split} E&=\pi_{0,0,0}\bar{p}_{1}\bar{p}_{2} E_{1}+\pi_{0,0,0}(1-\bar{p}_{1}\bar{p}_{2})E_{2}{+}(\pi_{1,0,1}s_{2}\bar{p}_{1} \bar{p}_{2}+\pi_{1,1,1}s_{1}\bar{p}_{1}\bar{p}_{2})E_{3}\\ &+\big{(}\pi_{1,0,1}(1-s_{2}\bar{p}_{1}\bar{p}_{2})+\pi_{1,1,1}(1 -s_{1}\bar{p}_{1}\bar{p}_{2})+\sum_{x=2}^{K+1}\big{(}\pi_{x,0,1}+\pi_{x,1,1} \big{)}\big{)}E_{4}\end{split}\] (29)
2. If \(1\leq H\leq K\), \(\rho\) is expressed as follows. \[\begin{split} E&=\sum_{x=0}^{H-1}\pi_{x,0,0}\big{(} \bar{p}_{1}+\pi_{H,0,0}\bar{p}_{1}(\bar{p}_{2}+p_{2}\frac{H}{\alpha})\big{)}E_ {1}\\ &+\Big{(}\sum_{x=0}^{H-1}\pi_{x,0,0}p_{1}+\pi_{H,0,0}(1-\bar{p}_{1 }(\bar{p}_{2}+p_{2}\frac{H}{\alpha}))\Big{)}E_{2}\\ &+(\pi_{1,0,1}s_{2}\bar{p}_{1}\bar{p}_{2}+\pi_{1,1,1}s_{1}\bar{p }_{1}\bar{p}_{2})E_{3}\\ &+\big{(}\pi_{1,0,1}(1-s_{2}\bar{p}_{1}\bar{p}_{2})+\pi_{1,1,1}( 1-s_{1}\bar{p}_{1}\bar{p}_{2})+\sum_{x=2}^{K+1}(\pi_{x,0,1}{+}\pi_{x,1,1}) \big{)}E_{4}\end{split}\] (30) where \(E_{1}\) is the energy consumption in a unit time slot for keeping the CH in the sleep state; \(E_{2}\) is the energy consumption for changing the CH from the sleep state to working state; \(E_{3}\) is the energy consumption for changing the CH from the working state to sleep state; and \(E_{4}\) is the energy consumption for keeping the CH in the working state.
# An Unequal Clustering and Multi-Hop Routing Protocol Based on Fuzzy Logic and Q-Learning in WSNs
## 5 Results and Discussion
### Performance Evaluation in Scenario 2
#### 5.3.3 Stability Period  
Table 4 and Figure 28 display the network stability period outcomes for EEUC, CHEF, and FQ-UCR in Scenario 2. With 100 nodes, FQ-UCR's stability period exceeded that of EEUC and CHEF by 180.9% and 389.7%, respectively. In a network of 200 nodes, FQ-UCR achieved a stability period that was 136.6% longer than that for EEUC and 95.7% longer than that for CHEF. This demonstrates the significant advantage of FQ-UCR, particularly in high-density networks, where it effectively utilized inter-cluster communication and optimized cluster routing strategies to enhance network energy efficiency.
## 4 Frequency Selective Surfaces Based on Metasurfaces
Multi-layered structures have also been demonstrated to reduce the size of the individual elements of FSSs, an inductive grid of wires fabricated on one side of the substrate with the other side having a loop grid in which every element is connected to its neighbours with a capacitor. This structure showed a relatively stable frequency response to the changes in the angle of incidence [82]. A multi-layered square patch complementary structure has been utilized to improve the gain of an antenna over a wide bandwidth by using Fabry-Perrot resonance [83]. Multi-layered periodic structures designed using a genetic algorithm have been used to construct an artificial magnetic conductor [84]. A similar approach has also been employed in the area of metasurfaces. A three-layered efficient metasurface for controlling the phase and magnitude of the transmitting beam using horizontally and vertically aligned split-rings has been presented in [85].
# Interface Direct Shear Tests on JEZ-1 Mars Regolith Simulant
## 1 Introduction  
The National Aeronautics and Space Administration (NASA) launched the Mars 2020 Perseverance Rover mission on 30 July 2020, the Perseverance Rover landed on Jezero Crater, Mars on 18 th February 2021. The main job of Mars 2020 Perseverance Rover on Jezero Crater is seeking signs of ancient microbial life, and gathering samples of rock and regolith (i.e., broken rock and soil) for a possible return mission to Earth in the future for detailed analysis. The Perseverance rover is equipped with a drill to collect core samples of Martian rock and soil, then store them in sealed tubes to be brought back to Earth by a future mission.  
Martian simulants are widely used for testing rovers and other instruments for Mars exploration, due to the fact that no rock or soil has been returned to Earth from Mars. Several typical Martian soil simulants have been designed and reported in the literature. For instance, Johnson Space Center JSC Mars-1 is the most remarkable Martian simulant [1]. Other important Mars simulants are Mojave Mars Simulant (MMS) [2] and its updated version MMS-1. However, the three simulants are no longer available outside of NASA [3]. Mars Global Simulant-1 (MGS-1) was developed by the University of Central Florida as an open mineralogical standard to precisely represent Martian global basaltic regolith [3]. Johnson Space Center-Rocknest (JSC-RN) is a new Martian simulant that was manufactured and utilized by NASA's Advanced Exploration Systems (AES) ISRU project, for experiments of water extraction from Martian soil [4]. However, these simulants reported above tend to be suitable for certain uses, but inappropriate for others [3].  
This research mainly investigated the interface direct shear responses of a Martian simulant named JEZ-1, to provide the first insight into understanding the geotechnical characteristics of the Martian soil. It is a Jezero Crater simulant developed by the CLASS Exolith Lab at the University of Central Florida, in preparation for the Mars 2020 Perseverance Rover mission. JEZ-1 is specifically designed for the scientists and engineers looking to learn more about the landing site (Jezero Crater) of the Perseverance Rover and the scientific research that can be done there. The direct shear test is applicable to understand the mechanical behavior of Martian regolith, especially under different stress or strain conditions (e.g., large strains or critical state), according to [5; 6; 7; 8]. The parameters of cohesion/adhesion and (interface) friction angle obtained from the direct shear test are fundamental information for the design of rover (e.g., landing systems and wheels) that is in contact with the Martian soil [7; 9]. The test results in this paper will help pave the way for future human exploration of Mars.
## 3 Method
### Example Scenario for CTDI  
To illustrate the design of CTDI, an essential wireless feature, wireless LAN frame control type (wlan.fc.type), is given to illustrate the usage of CTDI.  
CTFD requires calculating the frequency of the repeating feature values. For example, the \(i\)th feature value, \(\text{FV}_{i}\), is equal to 1. This feature value repeats in 10,000 samples, of which 8000 belong to the normal ones and 2000 belong to the attack ones. Hence, \(\text{TF}_{i}\) is 10,000, CTFD\({}_{i}^{0}\) is 8000, CTFD\({}_{i}^{1}\) is 2000, and \(\text{Diff\_CTFD}_{i}\) is 6000. Thus, this feature value contributes 6000 samples to CTFD\({}_{\text{Score}}\) for the feature wlan.fc.type. Clearly, this feature value has high differences between normal and attack samples. When other feature values also contribute high \(\text{Diff\_CTFD}_{i}\) to CTFD\({}_{\text{Score}}\) becomes very high. This reveals that the feature, wlan.fc.type, presents high differences between normal and attack samples for  
Figure 2: Auto Encoder neural network.  
each of the wireless LAN type values. Thus, it this feature is valuable for detecting the attack and should be selected by CTFD.  
For CTMD and CTDD, they require FCM to generate the cluster center, C\({}^{\rm k}\), for the cluster k and the membership of the cluster k, \(\mu_{i}^{\rm k}\), for FV\({}_{i}\). When FV\({}_{\rm i}\) is 1, because FV\({}_{i}\) is close to cluster 0, \(\mu_{1}^{0}\) is high (such as 85%) and \(\mu_{1}^{1}\) is low (such as 15%). Thus, Diff\(\_\)CTMD\({}_{\rm i}\) is also high (70%) to make CTMD\({}_{\rm Score}\) of the feature high.  
Besides, because FV\({}_{i}\) is also close to C\({}^{0}\) and far away from C\({}^{1}\), Diff\(\_\)CTDD\({}_{\rm i}\) becomes high. Therefore, CTMD\({}_{\rm Score}\) of the feature also becomes high because of the many high contributions of Diff\(\_\)CTDD\({}_{\rm i}\). As a result, this feature is selected by both CTMD and CTDD, and CTDD selects this feature based on the agreement of the three indexes.  
For Auto Encoder, this feature, wlan.fc.type, is transformed into the extracted feature. Auto Encoder preserves the essential distribution of the feature values in the given feature, so the distribution difference between normal and attack samples should be highly related to the original feature. If the distribution difference in the original feature is large, the distribution difference in the extracted feature should be consistent. Thus, this extracted feature can have high scores for the three indexes and be selected by CTDI.
# RECRP: An Underwater Reliable Energy-Efficient Cross-Layer Routing Protocol +
RECRP includes routing table updating and routing phases. The updating phase updates the routing table information. According to the routing table, the routing phase dynamically selects communication frequency, transmission power, and the next hop to achieve energy-efficient forwarding. We chose the minimal forwarding capability between the node \(i\) to its neighbor \(j(k,h...)\) and the node \(j(k,h...)\) to its next hop to determine the minimal forwarding capability \(C_{j}\) (\(C_{k}\), \(C_{h}\),...) within two hops (shorter board in Barrel principle), then selected the maximal among all minimal forwarding capabilities (\(C_{j}\), \(C_{k}\), \(C_{h}\),...) to balance the energy consumption. The following are the contributions of this paper:  
* We propose a location-free single-copy routing protocol, RECRP, to deal with the high energy consumption and imbalanced energy cost in underwater sensor networks. We use the mixed integer linear programming (MILP) model with a max-min method to formulate the underwater routing forwarding problem to achieve energy efficiency. The protocol also handles the communication void problem, which is a common problem in greedy routing protocols. * We adopt the information of physical layer such as Doppler scale shift measurement and RSSI to estimate the relative velocity and distance without extra hardware to make RECRP suitable for many underwater scenarios. * We conducted simulations to verify the performance of RECRP in a randomly deployed sparse network. Simulation results show that in terms of packet delivery rate, energy consumption, and end-to-end delay, RECRP performed better than depth-based routing (DBR) and hop-by-hop vector-based forwarding (HH-VBF). The rest of the paper is organized as follows. Previous work on underwater energy-efficient routing is summarized in Section 2. In Section 3 we describe RECRP in detail. Section 4 illustrates simulation results. Finally, Section 5 concludes the paper.
# The Clinical and Economic Value of Triclosan-Coated Surgical Sutures in Abdominal Surgery
###### Abstract  
Surgical site infection (SSI) is a frequent complication of surgical procedures. The aim of this study was to analyze the clinical evidence for SSI prevention with triclosan-coated sutures (TCS) in abdominal surgery and to investigate the economic impact of TCS in this type of procedure compared with conventional absorbable sutures (CS). A literature review was carried out to identify meta-analyses that were published between 1990 and 2019 that assessed the use of TCS in abdominal surgery. A budget impact analysis was performed from an Italian hospital perspective based on the most recently published evidence to simulate the financial impact of TCS in a general surgery unit. Uncertainty was explored through scenario analysis, as well as deterministic and probabilistic sensitivity analyses. Nine meta-analyses and two additional randomized clinical trials were retrieved. All meta-analyses described a reduction (range 19%-44%) in the risk of SSI when TCS were used. The use of TCS was associated with an overall annual net saving for the general surgery unit of \(\xi\)14,785 and a reduction of 3.2 SSIs compared with CS. Sensitivity analyses resulted in a positive annual saving associated with TCS in 98% of scenarios. TCS are a valuable, cost-saving SSI prevention strategy. TCS additional costs would be offset by the reduction in SSIs.  
t 11 December 2019; Accepted: 30 January 2020; Published: 6 February 2020
# Design of a Heterogeneous-Based Network Intrusion Detection System and Compiler
## 3 Materials and Methods
### FPGA-Based Pre-Filtering Design
Given that port numbers often map to multiple rules (making retrieval operations potential performance bottlenecks), the resource allocation ratio between port matching and protocol/IP matching modules is set to 2:1. If the number of output rules exceeds 10, the packet is classified as high-probability anomaly and directly forwarded to the fast-pattern matching module for string detection under \"any\" type processing. Five-tuple matching performance primarily depends on packet quantity. Theoretical calculations indicate the following: at 200 MHz FPGA clock frequency, a single port matching module achieves approximately 20 Mpps processing capacity. For 100 Gbps traffic with 700 B average packet length, the theoretical packet rate is 17.86 Mpps, which a single port module can handle. However, extreme packet lengths (e.g., 64 B) in real-world scenarios may cause instantaneous packet rate surges. Full-speed port matching could generate rule queries exceeding the protocol/IP module's capacity (66.7 M rules/s, with single ports potentially associating with multiple rules). Therefore, a dual-port matching module architecture is adopted, each port module equipped with two protocol/IP processing units for traffic balancing. Post protocol/IP module processing, matched source/destination IP-associated rule IDs are extracted, and corresponding packets are transferred to the fast-pattern matching module for payload anomaly detection.