esa-sceva/satcom-chunk-collection huggingface dataset:
# DisA Limits RecG Activities at Stalled or Reversed Replication Forks
## 3 Results
### DisA Competes with RecG for Binding to the cssDNA Substrate  
The helicase assays showed that the inhibitory effect of DisA is higher when pre-bound to the DNA. We can envision that DisA bound to the cssDNA substrate counteracts the loading of RecG and thereby inhibits RecG-mediated ATP hydrolysis. To test this hypothesis, the effect of order of protein addition was tested. When RecG was pre-incubated with cssDNA (RecG + cssDNA) (5 min at 37 \({}^{\circ}\)C), and then DisA (24 nM, 1 octamer DisA/416-nt) and ATP were added, ATP hydrolysis was almost not affected (Figure 4D brown vs. dark blue line, Table 1). By contrast, when DisA pre-bound the cssDNA (DisA + cssDNA) (5 min at 37 \({}^{\circ}\)C), and then RecG and ATP were added, ATP hydrolysis was significantly inhibited by -9-fold (\(p\) < 0.01) (Figure 4D, red vs. dark blue line, Table 1), as when both proteins were simultaneously added (Figure 4C, brown line). This suggests that DisA binds cssDNA (or formed secondary structures [e.g., flayed structures]) and counteracts RecG loading, inhibiting RecG-mediated ATP hydrolysis. Since the inhibition of the RecG ATPase occurs in the presence of limiting DisA (1 DisA/416-nt) we have to assume that DisA might bind to discrete sites in the cssDNA, probably regions with secondary structures, which are also the binding site for RecG.  
To confirm that DisA competes with RecG for cssDNA binding, DisA was replaced by DisA \(\Delta\)C290, a mutant variant which lacks the C-terminal DNA-binding domain [24]. The presence of a saturating DisA \(\Delta\)C290 concentration (36 nM, 1 DisA/277-nt) did not inhibit RecG-mediated ATP hydrolysis (Figure 4E, Table 1). The same was observed when HJ DNA was used as the effector (Figure 4F, Table 1).  
DisA could also inhibit the ATPase activity of RecG by the consumption of the ATP substrate through its conversion to c-di-AMP. Moreover, c-di-AMP itself could inhibit the ATPase activity of RecG. This is probably not the case because c-di-AMP synthesis is inhibited when DisA is bound to HJ DNA (Figure 1C) and DisA \(\Delta\)C290 does not inhibit RecG but still synthesizes c-di-AMP [24]. To further discard these hypotheses, DisA was replaced by DisA D77N, a mutant variant with a point mutation in the DAC active site that does not synthesize c-di-AMP [21; 24]. In the presence of cssDNA, RecG-mediated ATP hydrolysis was inhibited in a DisA D77N concentration-dependent manner, to a similar extent as when wt DisA was used (Figure 4G, Table 1). As shown for wt DisA, higher DisA D77N concentrations were required to observe a similar rate of inhibition of ATP hydrolysis in the presence of HJ DNA (Figure 4H, Table 1). This proves that the inhibition of RecG-mediated ATP hydrolysis is a genuine activity associated with DisA bound to cssDNA or HJ DNA, and independent of c-di-AMP and of a reduction of the ATP pool.
# Extended Reality Application Framework for a Digital-Twin-Based Smart Crane
## 2 Materials and Methods
### \"Ilmatar\" Crane  
\"Ilmatar\" is the name of an industrial overhead crane installed in the Aalto Industrial Internet Campus (AIIC) in late 2016. It is a Konecranes CXT family crane with several smart features, such as target positioning, sway control, load floating, and snag prevention [45]. Figure 2 illustrates the main components of the crane. The crane contains three main moving subsystems, the bridge for moving forward and backward through Axis Z, the trolley for moving left and right through Axis X, and the hoist for moving up and down with the hook block through Axis Y. Table 1 displays the basic functional properties of the crane, which keeps consistency with the model in ROS and the model in Gazebo.  
In addition, the crane is equipped with a Siemens PLC (Programmable Logic Controller) that handles crane sensors and control data, and the PLCs are linked to an OPC UA (Open Platform Communication Unified Architecture) server to provide external access to the crane data. Meanwhile, the crane is connected to the Internet, which can send data to a third partly IoT platform, \"MindSphere\" by Siemens, via a cloud vendor.  
Figure 1: Framework and workflow of the applications for a DT-based crane. The whole framework contains four layers: the perception layer with the “Ilmatar” crane and its simulation mode; the machine communication layer assuring the standard data acquisition; the network layer focusing on communication with the external users; and the service layer providing various digital services.  
The crane is mainly used for research, co-innovation, and education in the university lab environment instead of regular high-load and high-frequency production line tasks [45]. The crane is equipped with various intelligent sensors and integrated with DTs based on the previous work by [PERSON] et al. [46]. Many digital assets were developed, such as OSEMA (Open Sensor Manager) [47], OPC UA-GraphQL Wrapper, and DT Core, making the \"Illatar\" crane a suitable target for DT-based research.
# Crop Classification for Agricultural Applications in Hyperspectral Remote Sensing Images
###### Abstract  
Hyperspectral imaging (HSI), measuring the reflectance over visible (VIS), near-infrared (NIR), and shortwave infrared wavelengths (SWIR), has empowered the task of classification and can be useful in a variety of application areas like agriculture, even at a minor level. Band selection (BS) refers to the process of selecting the most relevant bands from a hyperspectral image, which is a necessary and important step for classification in HSI. Though numerous successful methods are available for selecting informative bands, reflectance properties are not taken into account, which is crucial for application-specific BS. The present paper aims at crop mapping for agriculture, where physical properties of light and biological conditions of plants are considered for BS. Initially, bands were partitioned according to their wavelength boundaries in visible, near-infrared, and shortwave infrared regions. Then, bands were quantized and selected via metrics like entropy, Normalized Difference Vegetation Index (NDVI), and Modified Normalized Difference Water Index (MNDWI) from each region, respectively. A Convolutional Neural Network was designed with the finer generated sub-cube to map the selective crops. Experiments were conducted on two standard HSI datasets, Indian Pines and Salinas, to classify different types of crops from Corn, Soya, Fallow, and Romaine Lettuce classes. Quantitatively, overall accuracy between 95.97% and 99.35% was achieved for Corn and Soya classes from Indian Pines; between 94.53% and 100% was achieved for Fallow and Romaine Lettuce classes from Salinas. The effectiveness of the proposed band selection with Convolutional Neural Network (CNN) can be seen from the resulted classification maps and ablation study.  
band selection; CNN; NDVI; hyperspectral imaging; crops; agriculture +
Footnote †: journal: Applied sciences
## 5 Experiments
### Experiments in an Industrial Scenario
#### 5.3.2 Packet Delivery Ratio (PDR)  
In order to evaluate the reliability and adaptability of the protocol to different environments, the packets sent and received during 100 packet transmissions between two nodes (without including potential retransmissions) are compared in Figure 23. For the indoor environment, the protocol achieved a 100% PDR in the first three positions (i.e., all packets were delivered successfully) and 99% in the fourth one, thus demonstrating a solid performance in stable short-range scenarios. For the industrial environment, the obtained results were decisive in clarifying whether the protocol is actually valid for large  
Figure 21: Latency for node at position 1.  
Figure 22: Latency for node at position 2.  
industrial scenarios. The result obtained for the first position at 40 m was similar to the one for the fourth indoor spot, with a 99% PDR. However, for the second tested spot, where the distance exceeded 100 m, PDR decreased to 95%, which is expected, since being in a Non-Line-of-Sight (NLOS) industrial area, the longer the distance, the higher the signal propagation loss, and the larger the number of obstacles that can be found on the communications path between the emitter and the transceiver. In any case, despite this 5% loss in the PDR (which, for instance, could be reduced by adding more retransmissions to the protocol), it can be concluded that the proposed system can be used in industrial environments similar to the ones tested (i.e., with long distances and with the presence of multiple large metallic obstacles).
# Mining Pool Selection under Block WithHolding Attack +
## 5 Numerical Results
### Simulation Algorithm  
We simulate the pool selection process based on Algorithm 1. This algorithm shows how each miner selects the pool to join based on the pairwise proportional imitation protocol. At the beginning of the simulation, each miner joins a pool randomly. After the initialization, each miner \(i\) randomly chooses a pool \(j\) (\(j\in\mathcal{M}\)) for payoff comparison and decides to move to pool \(j\) from its current pool \(k\) (\(k\in\mathcal{M}\)) with a certain probability \(\rho_{k,j}\), if the expected payoff of pool \(j\) is larger than the expected payoff of pool \(k\). Otherwise, the miner will stay in its current pool \(k\). These steps will be repeated until the population states \(x\) converges.  
```
1:Initialize:\(t\gets 1\)
2:while\(x\) is not converged and\(t<\) MAX_COUNTER do
3:for all\(i\in\mathcal{N}\)do
4:\(k\leftarrow\) The current pool of miner \(i\)
5:\(j\leftarrow\mathsf{rand}(1,M)\)\(\triangleright\) Choose a pool \(j\) for payoff comparison at random
6: Move from pool \(k\) to \(j\) with probability
7:\(\rho_{k,j}=x_{j}\max(y_{j}(x,\mathbf{\omega},s_{j},\mathbf{a})-y_{k}(x,\mathbf{\omega},s_ {k},\mathbf{a}),0)\)
8:endfor
9:\(t\gets t+1\)
10:endwhile
```  
**Algorithm 1** Mining Pool Selection Algorithm
# Assessing Ecosystem and Urban Services for Landscape Suitability Mapping
## 2 Materials and Methods
### Study Area  
The model was developed and tested in a 1007 km\({}^{2}\) wide study area located in the Umbria region, Italy (43.077837\({}^{\circ}\) N, 12.420829\({}^{\circ}\) E). On average, the area is 332 m.a.s.l., has a transitional Mediterranean climate (sub-coastal in the western part and sub-continental in the eastern part, according to the Koppen classification), with hot, sunny summers and relatively cold winters. It includes the city of Perugia and its surroundings, covering seven different municipalities: Perugia, Magione, Passignano sul Trasimeno, Corciano, Umbertide, Torgiano, and Derruta (Figure 1). This area encompasses an urban and productive landscape of high territorial complexity that, in recent decades, has been characterised by high rates of urbanisation and related rural transformations. Land use patterns in the area are typical of central Italy, consisting of 58% agricultural land, 28% forested and semi-natural land, 8% built-up land, and less than 6% wetland and water bodies (personal elaboration from CORINE land-use and land-cover data [44]). The area is characterised by various landscape types, including natural areas as well as the arable, vineyard, and olive yard-dominated landscapes. A large proportion of the study area is characterised by various mixed transitional landscapes [5]. In such heterogeneous areas, landscape planning proves to be highly challenging, as it involves addressing conflicting needs of the human populations living and working in the area (e.g., nature conservation, economic development, agricultural production, housing, etc.).  
Eleven landscape classes (Table 1) were identified in a previous study based on unsupervised classification of different LULC density features [5; 45]. The methodology was aimed at detecting the landscape typologies along a gradient generated by a sliding level of anthropogenic influence on LULC. In this vein, the landscape classes can be considered homogeneous in LULC composition and can be ordered according to landscape sequences [46]. Their interpretation is highly informative, as it highlights different characteristics of the landscape [47]. For this reason, they were used as known reference areas for a better understanding of the variations of the SUSAM outputs.  
Figure 1: Study area location with landscape classes identification [5]. Landscape codes are described in Table 1.
# A Single Image Enhancement Technique Using Dark Channel Prior
## 4 Experimental Results And Analysis
### Subjective Visual Evaluation
This approach also makes the transmittance estimation of the sky area inaccurate, which will cause undesired defogging in the sky area. The first and second images in Figure 7e are defogged images obtained by using our improved method. It can be seen from the first image that not only the dense fog in the original image is removed, but also the small villages next to the mountains can be seen. From the second picture, we can see that the fog on the reflective water surface has been removed. This is mainly because the defogging method we used for large areas of high lightness is not dark channel prior, but a new method. First, the sky segmentation algorithm is used to separate the sky region from the non-sky region. Then, an optimized dark channel prior algorithm is used for the non-sky region. Finally, an enhancement algorithm based on sequential decomposition is used for the sky region. Our method does not perform noise cancellation and sky enhancement separately, but processes them at the same time. In this sequence of suppressing noise, we perform spatial smoothing for each component, and use the weighting matrix to suppress noise. Based on the above described subjective analysis, the sky enhancement algorithm based on sequential decomposition can effectively overcome the problem of failure of defogging in large areas with high lightness. Our method can not only remove the fog subjectively, but also enrich the details of the fogged image.
## 3 Results
### Sensory Properties  
Control and supplemented corn-based extruded snacks were sensory evaluated, and the results are presented in Figure 2. The sensory test showed that samples enriched with BPP at 6% obtained a higher score than those with a lower concentration of BPP. Samples with 2% and 4% BPP addition had significantly (\(p<0.05\)) lower scores in color and appearance as well as general acceptability compared with the control sample. The golden-yellow color of control snacks turned into non-attractive colors at 2% and 4% BPP addition. However, the addition of 6% BPP induced a noticeable change in color, which turned into an attractive purple (Figure 2).  
The observed color changes are due to the high anthocyanin content of BPP (28.66 \(\pm\) 0.48 mg CGE/g) and depend on the addition level and the pH of the product. The score for appearance significantly (\(p<0.05\)) decreased as a result of BPP addition at 2% and 4%, mainly due to the negative color changes as well as to the slight decline in the expansion ratio of these formulations.  
\begin{table}
\begin{tabular}{l c c c c c} \hline  & **BPP** & **CES 0** & **CES 2** & **CES 4** & **CES 6** \\ \hline Total phenolic content & \(37.52\pm 0.42\) & \(88.82\pm 1.33\)  & \(162.73\pm 1.89\)  \\ DFPH radical & \(2.61\pm 0.11\) & \(0.22\pm 0.01\)  & \(0.41\pm 0.02\)  \\ \hline \end{tabular}
\end{table}
Table 5: Effect of BPP addition on total phenolic content and DPPH radical scavenging activity of corn-based extruded snacks.  
Corn-based extruded snacks with BPP addition exhibited a light, typical bilberry flavor, which increased with the increasing addition level. As a result, the 6% BPP-enriched snacks recorded the highest score for aroma. The evolution of taste as a result of BPP addition was similar to that of aroma; the taste improved slightly but constantly as the addition level of BPP increased, as a slightly delicate sour and sweet taste was added to corn extrudates by enrichment. The highest average score for texture was recorded for the 6% BPP enriched extruded snacks, in close connection to their highest expansion ratio, as well as with their lowest cohesiveness, resilience, chewiness, and puncture force revealed by the results of the instrumental texture analysis. The corn-based extruded snacks enriched with 6% BPP were the most liked in all the sensory attributes, with significantly (\(p<0.05\)) higher scores than the other formulations.  
In the present study, samples were manufactured without topping or flavoring, both the control and the enriched ones. For the control snacks, it is customary to use a salty topping, but for the snacks enriched with BPP, it would be recommended to create a sweet topping that is able to blend and complement harmoniously with the bilberry flavor of the extrudate and to impact positively on the sensory attributes.
## 3 Results
### Evaluation of TPC and TAC  
Table 4 presents the estimated mean values for TPC and TAC before and after in vitro digestion, including their predicted bioavailability.  
Prior to the in vitro digestion process, the mean TPC value determined by the Folin-Ciocalteau assay was 0.95 mg of GAE/g dw, with a standard deviation of 0.06. Post-digestion, the mean TPC of the vegetable spread decreased significantly (\(p<\) 0.05) to 0.27 mg of GAE/g dw, with a standard deviation of 0.03 (Table 4).  
The mean TAC of the vegetable spread before digestion was found to be 0.016 mmol of Fe\({}^{2+}\)/g dw, with a standard deviation of 0.00. After subjecting the sample to the in vitro digestion process, the TAC of the vegetable spread decreased significantly (\(p<\) 0.05) to 0.005 mmol of Fe\({}^{2+}\)/g dw with a standard deviation of 0.00 (Table 4). The observed reduction in FRAP values indicates a substantial alteration in the TAC of the product following the simulated digestion.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Microbiological** & & & & & & & & \\
**Parameter (Unit)** & **Day 1** & **Day 5** & **Day 25** & **Day 45** & **Day 90** & **Day 120** & **Day 150** & **Day 180** \\ \hline TVC 1 (log cfu/g) & 2.04 \(\pm\) 0.06 & 2.60 \(\pm\) 0.06 & 2.48 \(\pm\) 0.06 & 3.52 \(\pm\) 0.06 & 5.45 \(\pm\) 0.06 & 5.32 \(\pm\) 0.06 & 5.54 \(\pm\) 0.06 & 5.49 \(\pm\) 0.06 \\ Aerobic spores 2 (log cfu/g) & \(<\)1.00 & \(<\)1.00 & \(<\)1.00 & \(<\)1.00 & \(<\)1.00 & \(<\)1.60 & \(<\)1.60 & \(<\)1.60 \\ Anaerobic spores 2 (log cfu/g) & \(<\)1.60 & \(<\)1.00 & \(<\)1.00 & \(<\)1.60 & \(<\)1.00 & \(<\)1.60 & \(<\)1.00 & \(<\)1.00 \\ _E. coli_ 3 (log cfu/g) & \(<\)1.00 & \(<\)1.00 & \(<\)1.00 & \(<\)1.00 & \(<\)1.00 & \(<\)1.00 & \(<\)1.00 & \(<\)1.00 \\ Staphylococcus 4 (log cfu/g) & \(<\)1.00 & \(<\)1.00 & \(<\)1.00 & \(<\)1.00 & \(<\)1.00 & \(<\)1.00 & \(<\)1.00 & \(<\)1.00 \\ SR anaerbees 5 (log cfu/g) & \(<\)1.00 & \(<\)1.00 & \(<\)1.00 & \(<\)1.00 & \(<\)1.00 & \(<\)1.00 & \(<\)1.00 & \(<\)1.00 \\ _C. perfirgens_ (log cfu/g) & \(<\)1.00 & \(<\)1.  
The predicted bioavailability for TAC of the product was 30.58%, indicating that approximately 31% of the initially measured antioxidant compounds was retained after the simulated digestion. Similarly, the predicted bioavailability for TPC of the product was 28.03%, signifying the preservation of approximately 28% of the initial phenolics through the digestion process (Table 4). The observed reduction in TPC and TAC values after the in vitro digestion process along with the predicted bioavailability for the components of the product with antioxidant capacity are also schematically presented in Figure 5.
# Heterogeneous Parallel Implementation of Large-Scale Numerical Simulation of Saint-Venant Equations
## 4 Performance Optimization and Testing
### Work Group Optimization  
First of all, a few definitions are briefly described. Each OpenCL device has one or more compute units, and each compute unit is composed of one or more processing elements. The basic unit of executing a kernel in OpenCL is called a work-item, and a collection of several work-items is called a work-group. A work-group executes on a single compute unit. The work-items in a given work-group execute concurrently on the processing elements of a single compute unit. There are two ways to specify the number of work-groups. In the explicit model a programmer defines the total number of work-items to execute in parallel and also how the work-items are divided among work-groups. In the implicit model, a programmer specifies only the total number of work-items to execute in parallel, and the division into work-groups is managed by the OpenCL implementation. We used the implicit model before, and the optimization method is to use the explicit model. In order to choose an appropriate work-group size, we conducted tests and the results are shown in the Figure 8. Finally, the default maximum work-group size is different for different devices. For example, the device limit we use is 256, which is the same as most devices. At this point, in the kernel code, add _attribute_((amdgpu_flat_work_group_size(<min>,<max>))) after the kernel function so that the kernel can be launched on when the working group is greater than 256. It can be seen from the test results that the best results can be achieved when the work-group size is 256.  
Figure 7: Extended test performance graph for overlapped version.
## 3 IoT Standards and Protocols for Precision Agriculture
### IoT Protocols in Precision Agriculture (RQ2)
#### 3.2.4 IoT Protocols for Precision Agriculture Requirements  
Table 8 presents a comparative analysis of IoT protocols for precision agriculture requirements. Each protocol listed in the table is evaluated against the fundamental re  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Application** & From network process to application \\ \hline
**Presentation** & Data representation and encryption \\ \hline
**Session** & Inter-host communication \\ \hline
**Transport** & End-to-end connection and reliability \\ \hline
**Network** & Path determination and logical addressing \\ \hline
**Data link** & Physical addressing (MAC and LLC) \\ \hline
**Physical** & Media, signal, binary transmission \\ \hline \hline \end{tabular}
\end{table}
Table 7: Open Systems Interconnection (OSI) model  
Figure 1: LoRaWAN architecture in precision agriculture.  
quirements for IoT sensor standards in the precision agriculture sector. SigFox, with its lightweight data flow and long-range coverage (10-40 km), supports simplicity and geolocation but may be limited by its low data rate (100 bps) and constrained duty cycle, making it less suitable for high-frequency data transmission. NB-IoT provides high security, adaptability, and support for diverse data types due to its integration with LTE networks, while maintaining low power consumption, making it highly versatile for precision agriculture. LoRaWAN, with its open platform and availability of open-source implementations, excels in supporting geolocation and lightweight data flow, but its lower data rate (50 kbps) might restrict use in high-bandwidth scenarios. WiFi, while offering high data rates and bandwidth, has higher power consumption and limited range (10-100 m), which may hinder its practicality in expansive agricultural areas. Finally, Bluetooth, with its simplicity and support for multiple use cases, is suitable for short-range applications but is constrained by its limited range and lower bandwidth compared to WiFi. By analysing these protocols, LoRaWAN and NB-IoT emerge as strong candidates for addressing the specific needs of the precision agriculture sector, such as geolocation, open platforms, and energy-efficient long-range connectivity.