esa-sceva/satcom-chunk-collection huggingface dataset:
# A Lightweight and High-Accuracy Model for Pavement Crack Segmentation
## 2 Related Work
### Semantic Segmentation  
With the advent of fully convolutional networks (FCN), the field of semantic segmentation entered a period of rapid development, leading to a surge of improved models and innovative architectures. Classic networks such as U-Net [18] introduced skip connections that directly pass high-resolution features to the decoder, effectively improving segmentation accuracy and becoming a foundation for subsequent models. The DeepLab series [19] set a benchmark for accuracy with its use of dilated convolutions and multi-scale context capture capabilities. HRNet [20], with its unique multi-resolution feature parallel processing and high-resolution preservation ability, has demonstrated exceptional performance in fine-grained segmentation tasks. However, these classic models typically involve large numbers of parameters, making them challenging to meet the demands of real-time applications.  
To address the limitations of embedded devices and real-time scenarios, the research on lightweight networks has gradually emerged. DDRNet [21] adopts an efficient dual-branch network structure, achieving real-time inference while maintaining accuracy. Fast-SCNN [22], through hierarchical feature extraction and a lightweight decoder, is well suited for resource constrained environments. PP-MobileSeg [23] uses depthwise separable convolutions and efficient attention mechanisms to reduce computational cost and memory requirements while maintaining high segmentation accuracy. The MobileNet series [24] further enhances lightweight performance through depthwise separable convolutions and efficient parameter design. EfficientNet [10] optimizes the trade-off between model size and accuracy with its compound scaling strategy.  
In summary, research on lightweight networks has become a key development direction in modern semantic segmentation. Striking the right balance between efficiency and accuracy has become the core issue in current research.
# Security Hardening and Compliance Assessment of Kubernetes Control Plane and Workloads
## 4 Security Hardening of Kubernetes Components  
This section emphasizes the architectural and operational fortification of Kubernetes clusters, outlining the essential measures necessary to secure both control plane and worker node components. Securing Kubernetes requires a layered defense strategy due to its open and dynamic architecture, encompassing network segmentation, secure API access, container runtime isolation, and node hardening. The chapter delineates essential practices, including role-based access control (RBAC), audit logging, secure communication, and optimal configuration protocols. These measures diminish the attack surface, enforce the principle of least privilege, and guarantee robust, policy-driven cluster operations in production environments.
_Article_  
S-Box Based Image Encryption Application Using  
a Chaotic System without Equilibrium  
[PERSON] 1, [PERSON] 2, [PERSON] 3, [PERSON] 4, [PERSON] 5,*,  
[PERSON] 6, [PERSON] 7 and [PERSON] 8  
Footnote 1: [[https://github.com/journals/applsci](https://github.com/journals/applsci)]([https://github.com/journals/applsci](https://github.com/journals/applsci))  
Footnote 2: [[https://github.com/journals/applsci](https://github.com/journals/applsci)]([https://github.com/journals/applsci](https://github.com/journals/applsci))  
Footnote 3: [[https://github.com/journals/applsci](https://github.com/journals/applsci)]([https://github.com/journals/applsci](https://github.com/journals/applsci))  
Footnote 4: [[https://github.com/journals/applsci](https://github.com/journals/applsci)]([https://github.com/journals/applsci](https://github.com/journals/applsci))  
Footnote 5: [[https://github.com/journals/applsci](https://github.com/journals/applsci)]([https://github.com/journals/applsci](https://github.com/journals/applsci))  
Footnote 6: [[https://github.com/journals](https://github.com/journals)]([https://github.com/journals](https://github.com/journals))  
in [26] while S-boxes based on chaotic Boolean functions was presented in [28]. Strong S-boxes were constructed by using a chaotic Lorenz systems [32]. [PERSON] et al. proposed a novel approach for designing S-boxes with a nonlinear chaotic algorithm [33]. A chaotic scaled Zhongtang system was applied to generate strong a S-box algorithm [30]. Moreover, by using a time-delay chaotic system, [PERSON] and [PERSON] introduced chaotic S-boxes [29].  
In this work, we investigate a non-equilibrium system, which attracts interest because its attractor is \"hidden\" [36; 37; 38; 39]. A hidden attractor cannot be localized numerically by a standard computational procedure [38; 39]. Because there is no limitation of equilibrium, such a chaotic system without equilibrium is appropriate for the area of information encryption [40]. Chaotic time-series obtained from the proposed system are used to construct S-boxes and develop an image encryption application. In this study, unlike the S-box based encryption algorithms in the literature, S-box production with high randomness bit sequences and strong cryptographic properties is performed using chaotic system based random number generator (RNG) with rich dynamical properties. An algorithm has been proposed that performs pixel-based encryption on image files and uses a different S-box in each cycle with random selection of S-boxes.
# A Display-Adaptive Pipeline for Dynamic Range Expansion of Standard Dynamic Range Video Content
## 4 Results and Evaluation
### LDR-to-HDR Conversion: Objective Evaluation
Display parameters were set according to the specifications of the SIM2 HDR HDR47 ES6 MB display. Figure 10 shows the results from our objective assessment. The results from the objective assessment indicate that our proposed pipeline generally outperforms other methods, with the exception of Video 4 and Video 7. In several cases, the performance of the other methods was comparable to our pipeline; we conducted a detailed analysis of Video 9 and Video 10 to better understand these two exceptions. Figure 11 illustrates a sample frame from these video sequences. We observed that the SR-ITM and FMNet methods tended to produce artifacts in highly saturated areas, reducing the overall quality of the video sequences. These artifacts became more pronounced when  
Figure 10: Objective quality scores for HDR video sequences produced by our proposed pipeline and two state-of-the-art methods. Quality scores were obtained from the dynamic range video quality metric (HDRVQM) using the HDR ground truth from the test dataset as a reference. HDRVQM computes one quality score for the entire sequence. Higher values indicate better quality. Figure 9: A preview of video sequences utilized in the objective evaluation, obtained from the xDR Dataset. displayed on an HDR screen. However, as shown in the objective results, these issues are not reflected in the quality scores estimated by HDRVQM.
# An Eccentricity Based Data Routing Protocol with Uniform Node Distribution in 3D WSN
## 4 Performance Evaluation
### Simulation Results
#### 4.2.4 Number of Hops  
Figure 7 shows the number of packets (_AD_Msg_) used 3-hops to traverse from an RC/CH node to the sink. EDR reduces the number of hops significantly during the data forwarding from an RC node to the sink. It reduces a significant number of the packets of 3D EADC in consideration of using 3-hops distance from an RC node to the sink.
## 5 Performance Evaluation
### The Performance of Our Approach with Different Values of \(\alpha\) and \(T\)  
The FNs are fixed in sensing area according to the application requirement, so most of the time the collected information is same. The Figure 3a demonstrates the data forwarded to the SS with and with no similarity. In few cases with \(\alpha=0.01\) and \(T=200\) only a limited amount of without redundancy is transmitted towards the SS. From Figure 3a, we can also see that by increasing the value of \(\alpha\) and \(T\) the amount of similarity in data can be further decrease to 10%. However, this may lost the original data.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Variable** & **Value** \\ \hline Number of FNs & 100–800 \\ \hline Network area & 500 m \(\times\) 500 m \(\times\) 500 m \\ \hline Speed of sound & 1500 m/s \\ \hline Transmission range & 150 m \\ \hline Transmit power & 50 W \\ \hline Bandwidth & 80 Hz \\ \hline Width of layer & 125 m \\ \hline \(E_{int}\) & 2000 J \\ \hline \(E_{elec}\) & 50 nJ/bit \\ \hline \(A(f)\) & 1.001 \\ \hline \(d_{0}\) & 80–100 m \\ \hline \(E_{da}\) & 50 nJ/bit/packet \\ \hline Data rate & 5 Kb/s \\ \hline Data packet size & 64 bytes \\ \hline Header size & 13 bytes \\ \hline Nodes mobility & 1 m/s–5 m/s \\ \hline Acoustic pressure of layer & 101 dB \\ \hline Acoustic pressure of data transmission & 103 dB \\ \hline Total run time & 1000 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: The values of parameters selected for the simulations.  
The Figure 2(b) illustrates the amount of energy consumption with different values of \(\alpha\) and \(T\). We can also note that, the CH and the cluster member's energy usage is associated with the number of transmissions. So, as the redundant data increases the number of transmission which affects the life of all the nodes in the network. This proposed model is designed to reduce the data redundancy with an appropriate degree of confidence and only selected data can travel toward the SS. As a result, this model is proved to be energy efficient as shown in Figure 2(b).
## 1 Introduction
### Music Performance: From Sound to Gesture
#### 1.2.1 Emotional Intensity and Expressive Intents in Musical Performances  
With the recognition of emotions in music, the emotional intensity and expressive intents have been shown to be dependent on auditory cues. In their study on such information, researchers asked participants to rate the emotional expressiveness of music performances in which timing and intensity were parametrically manipulated [49]. Emotion judgments monotonically increased with performance variability, and timing changes were reported to explain more variance in emotional expressiveness than sound intensity. Changes in tempo and sound intensity in a music performance were also shown to be correlated with one another, and with real-time ratings of emotional arousal [50]. A systematic relationship between emotionality ratings, timing, and loudness was highlighted when listeners rated their moment-to-moment level of perceived emotionality while listening to music performances. Therefore, the variation of acoustic features associated with the expressive intent of the musician during a performance appears to have a crucial impact on the emotionality perceived. On the visual side, [PERSON] (2005) demonstrated that certain perceptual elements of a musician's gestures are sufficient for the audience to identify a musician's expressive intent [43]. She suggested the use of three level of expressiveness to be able to study the link between expressive gestures and musical performance: (1) without expression, labeled as \"deadpan\"; (2) with normal expression/concert-like, labeled as \"projected\"; and (3) with exaggerated expression, labeled as \"exaggerated\". Some body parts have been reported to convey more expressive information, specifically head, shoulders, arms, and torso [46; 48; 51; 52; 53]. Some motion features have also been associated with expressive motion such as the quantity of motion [54]. The use of motion features helps understand broad, unrefined body reaction and gives a first glimpse of the behavioral components of expressive gestures. It might help understand how musicians cope with the audience [28; 55; 56]. For example, musician facing an audience and playing in exaggerated expressive manners could be affected by the amount of supplementary stress caused by the difficulty of the task. All in all, as mentioned by [PERSON] (1992) [57], \"a performer can be faithful to its structure and at the same time have the freedom to shape its moods\" (p. 265). This corresponds to a phenomenon called _performance expression_. It refers to \"the small and large variations in timing, dynamics, timbre, and pitch that form the micro-structure of a performance and differentiate it from another performance of the same music\" ([58], p. 118).
# Balanced Parallel Exploration of Orthogonal Regions
## 4 Algorithms
### Perimeter Exploration
```
/* Algorithm for agents to scan around the perimeter */  Each agent tracks its work done during perimeter scan;  All agents begin together and parallel to exterior wall;  Explored perimeter points \(P\leftarrow\varnothing\); whileA perimeter point \(\delta\) is left to be scanned (i.e., \(p\
otin p\))do  Agents move together as a convoy keeping a line adjacent to the exterior wall they are against;  The front of the convoy will be on top of \(\delta\); \(P\leftarrow\delta\cup P\); ifcuroy front agent performs a turn at \(\delta=(X_{t},Y_{t})\) or there is an unused corner perpendicular to traversal directionthen // Check possible directions for closing room RoomPoints - the set of closest perimeter points \((X,Y)\in P\) such that either \(X=X_{t}\) or \(Y=Y_{t}\);  Remove from RoomPoints all the points \((X,Y)\) such that the straight line from \((X_{t},Y_{t})\) to \((X,Y)\) goes through a previously visited hole or wall that is not in \(P\); // Attempt to close a room for\(\delta^{\prime}\in RoomPoints\)do \(WallHit,Success\gets false\); \(\ell\leftarrow\) convoy front agent; whilenot (\(WallHit\) or \(Success\))do  Agent \(\ell\) moves one point closer towards point \(\delta^{\prime}\);  ifagent \(\ell\) has reached \(\delta^{\prime}\)then \(Success\gets true\);  end if if obstacle encounteredthen \(WallHit\leftarrow ObstacleCheck(\delta,\delta^{\prime})\);  end if  end if Agent \(\ell\) returns to \(\delta\); ifSuccessthen \(RoomScan(\delta^{\prime})\);  mark corner as used;  end if  Change front agent of convoy to that one with smallest perimeter scan traversal;  end if  end if  end while
```  
**Algorithm 1**PerimeterScan  
The obstacle subroutine Algorithm 2 determines if the obstacle encountered is a hole or a wall. Let the point that the obstacle is encountered be \(o\). The agent checks that the first length of the obstacle \(D_{1}\) (see Figure 2c), perpendicular to the line \(l\) between \(\delta\) and \(\delta^{\prime}\), is shorter than the aspect ratio \(a\) multiplied by the length of \(l\) which is \(Dist=|X_{t}-X|+|Y_{t}-Y|\). If this value is exceeded, then the obstacle cannot be a hole that fits between \(\delta\) and \(\delta^{\prime}\). Continuing from this point, the agent checks each dimension to make sure that the obstacle is a hole. It checks if both of the lengths \(D_{2}\) and \(D_{3}\), Figure 2c, are within the \(a\) aspect ratio, first between \(D_{1}\) and \(D_{2}\) and finally between \(D_{2}\) and \(D_{3}\). During the exploration to \(D_{3}\) the agent will pass \(o^{\prime}\) such that \(o^{\prime}\) is on the line from \(\delta\) to \(\delta^{\prime}\). After this point, it is known that the obstacle adheres the aspect ratio restrictions of a hole, but the agent must make sure that it is rectangular by returning to \(o\) around the points that are between \(D_{1}\) and \(D_{3}\) that have not been explored. If it is indeed a hole, the agent adds in its memory as a known hole, a hole that is detected in the area, and returns to \(o^{\prime}\).
# Development and Field Validation of Wireless Sensors for Railway Bridge Modal Identification
## 2 Description of the Wireless Monitoring System  
The proposed monitoring system is made up of a gateway and wireless sensor nodes, which are mounted on the structure. The sensor nodes acquire vibration data and transmit them wirelessly to the gateway, which has the role of a central acquisition unit (Figure 1). The communication protocol chosen for the wireless side is the BLE, as already pointed out in Section 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature** & **Present Paper** & **Xnode [30]** & **iMote2 [31]** & **Microstrain [32]** \\ \hline Sampling Rate & 8 kHz & 16 kHz & 100 kHz & 1 kHz \\ ADC Resolution (bits) & 16 & 24 & - & 20 \\ Accelerometer Noise Density & 70 ug/Hz & 50 ug/Hz & - & 25 ug/Hz \\ Communication protocol & BLE & 2.4 GHz radio & 802.15.4 radio & LXRS\({}^{\text{\textregistered}}\) \\ Communication range & \textgreater{}200 m & 1 km & 300 m & 800 m (typical) \\ On-board processing & yes & yes & yes & no \\ Energy Harvesting & yes & no & no & no \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between different wireless sensors available in literature.
## 2 Related Works and State of the Art Review
### Related Works
#### 2.7.3 RAM Data Acquisition  
[PERSON] and [PERSON] [48] presented a technique for analyzing Java objects data by the extraction of RAM from devices with ARM 32-bit architecture, but with the flexibility to be adapted to other architectures (including 64-bit). The authors disregarded the data of large objects or allocated by native code libraries present in the Android operating system, version 5.0, run in an emulator. For the analysis process, the Volatility framework was used. The authors also created a set of extensions for Volatility that enabled the retrieval of information about the execution environment and the recovery of allocated Java objects, as well as mappings for interpreting the data in the ART, OAT, and DEX files, which allowed the recovery of the execution environment data structures. For experimental validation, they emulated a Nexus 5 device with Android OS Version 5.01 using _goldfish_. For data analysis, they used version 0.4 of the Linux Santoku Distribution, specialized in mobile device forensics. As a result, the authors were able to extract and analyze Java objects with an understanding of storage structures, overcoming traditional techniques based on detecting artifacts intrinsic patterns.  
[PERSON] and [PERSON] [49] describe a transition case resulting from a new version of Android Runtime (ART). They improved the technique previously developed [48], validating the tools proposed in emulated and real devices with Android OS version 5.0.1, with the physical device rooted, illustrating the difficulties related to forensic analysis due to the different specific implementations by several manufacturers of mobile devices.  
In Table 2 we present a summary of the different techniques and methodologies employed in related works.
###### Abstract  
Plant microbiomes represent a reservoir of adaptive traits that can enhance plant resilience and productivity. In this study, we investigated the potential of a synthetic microbial consortium (SynCom), composed of five nitrogen-fixing _Bacillusae_ derived from the phyllosphere of _Pistacia lenticus_ L, to promote the growth of _Lactuca sativa_ L under nutrient-limiting availability. The short-term effects of the SynCom were evaluated in a hydroponic system, and four inoculation treatments were compared. The root and leaf inoculation (RL) significantly increased total plant biomass, driven by higher above-ground (+33%) and below-ground (+31%) biomass. The number of leaves per plant and leaf mass per area were also significantly enhanced in RL (+12% and +34%, respectively). While root-only inoculation (R) did not alter plant biomass, structure, or leaf traits, foliar spraying (L) significantly decreased the total leaf area and increased root biomass and the root-to-shoot ratio compared to non-inoculated plants, suggesting a direct influence of microbial metabolites on root growth and nutrient uptake. Compared to the individual R and L treatments, the synergistic effect observed in RL highlights the complex interplay between plant-microbe interactions in the rhizosphere and phyllosphere and the importance of ensuring adequate nutrient availability to nitrogen-fixing bacteria to achieve their growth-promoting potential fully. These findings suggest the potential for utilizing SynComs as bioinoculants to promote plant growth, emphasizing the need to optimize application strategies, considering both the SynCom composition and the host plant's nutritional status to ensure efficacy.  
plant growth-promoting rhizobacteria (PGFR); synthetic microbial consortium (SynCom); marginal areas; _Pistacia lenticus_ L; phyllosphere; hydroponics +
Footnote †: journal: Applied sciences
# Application of Mammalian Nudix Enzymes to Capped RNA Analysis
## 7 Discussion
Among identified mammalian NUDIX proteins with in vitro decapping activity towards canonical methylated and unmethylated cap structures are four additional enzymes: Nudt3, Nudt15, Nudt17, and Nudt19 [116; 117]. These represent an as-yet-unexplored field in the study of decapping of RNAs with modified cap analogues. As Nudt3 was shown also to be active in vivo toward a subset of mRNAs involved in cell migration regulation [118], it can be considered, next to the Nudt proteins described above, in future assay(s) aimed at the stability of RNA transcripts.
## 1 Introduction
The relationship between those two factors affects the release of wine odor-active compounds. As sugar increases and acidity decreases, the aroma of wine changes from \"herbal\" to \"fruity\" [9]. However, higher sugar content in berries resulting in higher ethanol production can decrease the volatility of odor-active compounds in wine, and fruity aromas change to alcohol-associated aromas [10]. Grapes are typically harvested when pH levels are between 3.2 to 3.4 for Brianna [11] and around 3.0 for Frontenac gris [12]. Brianna fruit has \"grapefruit, tropical\" and slight \"floral\" characteristics [11]. Frontenac gris fruit has aromas of \"peach, apricot\" and \"tropical fruits\" [13]. These cold-hardy cultivars were introduced to the public in 2001 (Brianna) [11] and 2003 (Frontenac gris) [12]. The cultivars are advantageous in cold climates, where _V. vinifera_ will not survive the extreme low temperatures. Brianna was shown to be a top yielding cultivar among select cold-hardy cultivars with the lowest average titratable acidity [14]. Figure 2: The increase in wine production (hectoliters) and a number of wineries in Iowa [2]. There is a need to characterize aromas from these new cold-hardy cultivars in order to understand and improve the potential of the final product. The objective of this study was to preliminarily associate odor-active compounds in Brianna and Frontenac gris white wines with different stages of grape berry ripening (i.e., with increasing sugar content and pH). This was completed by analyzing odor-active compounds in the headspace of wine using solid-phase microextraction (SPME) and simultaneous chemical and sensory analysis using gas chromatography-mass spectrometry-olfactometry (GC-MS-O) [15; 16]. In our previous research, we developed an automated headspace SPME-GC-MS-O method for aroma profiles of seven cold-hardy wines [15]. The effects of the SPME fiber type (7 coatings), the headspace SPME extraction time (10 distinct times from 10 s to 1 h), the extraction temperature (6 set points from 35 to 80 \({}^{\circ}\)C), the incubation time (5 set points for headspace equilibration from 0 to 20 min), the sample volume (4 set points from 1 to 4 mL in a 10 mL vial), the desorption time (6 set points from 30 to 300 s), and the salt addition (5 set points) were tested. We used the optimized SPME conditions from previous research [15] in this current work. A multivariate analysis was used to illustrate the effects of harvest time on wine odor-active compounds. There is a need to characterize aromas from these new cold-hardy cultivars in order to understand and improve the potential of the final product. This is the first report of odor-active compounds in wines made from Frontenac gris and Brianna grapes at different levels of maturity. Information from this study can guide growers and winemakers in optimizing winemaking techniques and harvest decisions. This (GC-O) technique has been used in wine aroma analysis in Chardonnay [17], Muscat [18], Cabernet Gernischt, Cabernet Sauvignon, Cabernet Franc, Merlot [19], and native American grapes (Vitis) [20].