esa-sceva/satcom-chunk-collection huggingface dataset:
# River Discharge and Saltwater Intrusion Level Study of Yangtze River Estuary, China
## 1 Introduction
The improvements which have been made in this study over previous investigation attempts include the following: (1) an unstructured mesh with a high resolution was applied which better fits the shoreline areas; (2) the influences of Hangzhou Bay were considered; and (3) the use of spatially varying bottom roughness was included in the experimental process. In this study, the main focus was on the effects of the river discharge on the saltwater intrusions in the YRE as well as the nearby offshore sea areas. First, a hydrodynamic and salinity transport model of the YRE was setup with a MIKE21 model. Then, the tidal levels which had been measured in situ were employed to validate the model. The characteristic averaged river discharge during the dry seasons from 1957 to 2015 were then applied to simulate sea-level changes and salinity transport process. Next, the influences of the river discharge variations on the salinity measurement stations in the YRE were studied, and the correlation between the salinity and sea discharge was analyzed. The current study also analyzed the spatial distribution of the salinity, as well as the salinity amplification lines. Finally, the effects of the river discharge on the freshwater reservoirs were quantificationally analyzed.
## 4 Results and Discussion
### Analysis of Orthogonal Experiment Results  
Results of the orthogonal experiment are shown in Table 4, and the analysis of the extreme deviation of the surface roughness of the workpiece after machining is shown in Table 5. Here, K1 denotes the sum of the results (\(R_{\text{a}}\)) when the machining parameters were n1, d1, and f1, and K4 denotes the average results when the machining conditions were n1, d1, and f1; the values of K2, K3, K42, and K43 could be calculated in the same way, thereby obtaining the max-min value. The results show that the degree of influence of various factors on the surface roughness of the workpiece in descending order was as follows: feeding rate > grinding depth > spindle speed. The best combination of process parameters was a spindle speed of 900 rpm, feeding rate of 8 \(\mu\)m/min, and grinding depth of 16 \(\mu\)m.  
Figure 4: Force–displacement curves: (**a**) gel abrasive tool; (**b**) hot-pressing abrasive tool.  
Figure 5: (**a**) The influence of PI resin content on the mechanical properties. (**b**) A comparison of the mechanical properties of the gel abrasive tool and hot-pressing tool.  
represent the residuals of the data, and the line segments represent the confidence intervals. It can be seen that each point was relatively close to the zero line, and the confidence intervals all included the zero-point line, indicating that the model could predict the surface roughness of sapphire. The indices calculated using the regress function were as follows: \(C=1.0857\), \(b_{1}=-0.0890\), \(b_{2}=0.1580\), and \(b_{3}=0.4052\); therefore, the prediction model could be expressed as  
\[R_{a}=1.0857N^{-0.0890}\;D^{0.1580}\;V^{0.4052}\]  
Figure 7 compares the data points in the curve obtained according to the prediction model and the actual measured data points. It can be seen that the prediction model was basically consistent with the actual measured values.
## 3 Results and Discussion
### Mechanical Properties
The microcracks observed in the HC (Figure 5b,c), which appear more easily on the surface of the samples, can explain that the wear performance of the mortars activated with carbonate was not better than that of the OPC despite their lower porosity (Figures 3 and 4). The microcracks can favor sand detachment from the surface and the subsequent effect of the detached hard particles as an additional abrasive agent. For HN mortars, hydrates with low-mechanical properties (Figure 6), whose precipitation the nitrate addition favors [38; 52], can explain this worse wear performance. The fast precipitation of the nitrate-AFm during the hydration consumes calcium and limits the formation of gels with good mechanical properties such as C-S-H or C-(A)-S-H [38]. Those gels are capable of creating good bonding with sand particles. The anisotropic nature of nitrate-AFm and its layered structure with weak chemical bonds in certain directions can also negatively affect the wear performance of mortars that comprises this phase. All these factors can lead to a weaker sand-binder interface, thus affecting wear performance. The wear resistance of AAM can be higher due to the lower porosity of these materials and better sand-binder bonding in comparison with hybrid mortars [42].
# Generation of High Resolution Vegetation Productivity from a Downscaling Method
## 2 Materials and Methods
### Methods
#### 2.3.2 Estimation of GPP and NPP
Growth respiration \(R_{g}\) was the proportional to the difference between GPP and maintenance respiration \(R_{m}\):  
\[R_{g}=\gamma(GPP-R_{m}), \tag{11}\]  
where \(\gamma\) is the growth respiration coefficient and is defined as 0.25 [5; 44].
# Integrating Autonomous Vehicles (AVs) into Urban Traffic: Simulating Driving and Signal Control
## 3 Methodology
### Research Methodology Overview  
Figure 2 presents a comprehensive overview of the research methodology. The data collection phase involves gathering various traffic metrics, including average travel time, queue lengths, speed observations, and traffic volume with signal timing. The methods section outlines the use of the PTV VISSIM model, focusing on car-following and lane-changing behavior. Model calibration includes adjustments for lane changes and intersection links, followed by validation to ensure accuracy. The validated model is then used for traffic metrics analysis, signal control optimization, and safety assessments. The results are compared before and after optimization to draw conclusions.  
Figure 1: Geographical depiction of the signalized traffic intersection in Balgat, Ankara. Source: PTV VISSIM model.  
Figure 2: Flowchart of research methodology. Source: processed by authors.
## 1 Introduction
Potential applications of automated negotiation range from e-commerce [13] to task distribution problem solving, resource sharing or cooperative design [14; 15; 16; 17]. One of the most important advantages of automated negotiation is that it takes into account the conflict of interests from the beginning. This enables finding more stable solutions (agreements), which make participating agents less prone to deviating from the socially-optimal solution to favor their privately-optimal solution. Particularly, this work is, to the best of our knowledge, the first one that deals with the frequency assignment problem by using non-linear negotiation techniques, which have been extensively studied by the authors [18, 19]. This work contributes to achieving this objective in the following ways:  
* We present the problem of frequency assignment in WSSNs. * We model the problem of frequency assignment in WSSNs by means of the mathematical problem of graph coloring. * We present a utility model for frequency assignment that takes into account the influence of interferences in terms of the signal-to-noise ratio in a receiver at a given time. * We solve the frequency assignment problem in WSSNs with different negotiation techniques and, also, for comparison purposes, with other techniques: a random assignment, an optimization technique, and a heuristic technique based on LCCS. The paper is structured as follows. In Section 2, we model the problem as a graph, and we also model the propagation, interferences and utility of the solutions. In Section 3, we describe in detail the automated negotiation techniques proposed to solve the frequency assignment problem. Finally, in Section 4, we describe the scenarios under study and show the results, while in Section 5 we conclude and discuss the paper.
## 3 New Data Structure
### Table Lookup  
#### 3.2.1 Verbatim Lookup vs. Broadword Computation  
In the original [PERSON] and [PERSON] version as described in Section 2.3, the lookup table stores the relative rank or select results for each of the \(2^{b_{s}}\) possible small blocks and each of the \(b_{s}\) possible queries in \(\log(b_{s})\) bits requiring \(2^{b_{s}}\cdot b_{s}\cdot\log b_{s}\) bits of storage. To keep the table small, \(b_{s}\) had to be chosen very small. [PERSON] [6] bypasses the lookup table and instead calculates its entries on the fly via _broadword computation_. According to [PERSON] [6], \"broadword programming uses large (say, more than 64-bit wide) registers as small parallel computers, processing several pieces of information at a time.\" This is efficient as long as blocks fit into processor registers. In our approach bottom-level blocks are encoded only based on their frequency, and not on their content. Thus we inevitably need a decoding table. After looking up the appropriate table row it needs to provide rank information. This can be stored directly within the table, turning it into a combined decoding and lookup table.
## 4 Joint Test of _Sta_ and _Sttp_ in the City of Vienna
### Added Value of STTP for STA and Its Limits  
According to the developed methodology and test, the main added value of _STTP_ for _STA_ consists in its capacity to focus explicitly (and exclusively) on the performance of the transport system. Although this allows _STTP_ to point out the accessibility differences that are strictly linked to the transport system, it also makes individual differences less evident (as demonstrated by the test results). Indeed, other aspects such as the daily amount of free time, the distance daily travelled, and the availability of discretionary opportunities are excluded from the computation. This confirms that _STTP_ should be seen as a complementary (and not alternative) measure to be used together with _STA_ to deal with the broad topic of accessibility equality. On the one hand, _STA_ measures individual accessibility from a broad perspective by taking into account spatial, transport, individual and temporal factors and by holding the potential dimension of accessibility. On the other hand, _STTP_ narrows the focus down, by isolating the performances of the transport system in allowing individuals to carry out their schedule of daily fixed activities.  
Beside this key added value of _STTP_ for _STA_, it is important to mention also some limits of _STTP_ that need to be addressed in future applications. First, _STTP_ results highly depend on the estimation of _VTT_, which has to be as accurate as possible and performed at the individual level. However, to estimate _VTT_ at the individual level with the wage rate method, personal income data has to be collected. This is not always feasible since people tend to be not willing to answer income-related questions [53]. An alternative may be to estimate _VTT_ through stated and revealed preference methods [44]. However, this approach is very data demanding and time-consuming and it requires the availability of individuals to answer a lengthy questionnaire that should include (a) preliminary socio-economic and demographic questions; (b) the travel diary for _STPA_ computation; and (c) stated-preference questions to estimate _VTT_. Second, regarding the test, there are some computational limits to be refined. First, the opening/closure time of the discretionary opportunities should be integrated to properly select those to be counted in _STA_. Second, discretionary opportunities could be weighed according to the importance assigned by individuals to different categories of opportunities such as groceries, shopping facilities, or post/bank offices. However, the inclusion of these elements in the computation highly depends on the data at disposal in the study area and the availability of individuals to answer longer questionnaires. Third, the walking speed assigned to individuals (km5/h in our test) could be differentiated in specific cases, e.g., for elderly people with physical hindrances that make them walk slower. These limits need to be addressed to highlight precisely accessibility difference issues at individual level (e.g., refs. [54; 55; 56]).
## 4 Discussion
### Instrumental Color Parameters and Nitrosylmyoglobin Content  
Color is one of the foremost sensory attributes for meat and meat products consumers since it is associated with freshness and quality [42,43]. All sausages suffered a lightness and yellowness reduction from Day 1 to Day 30. On the last day, both the control sausage and the sausages with black cumin showed similar values of L* parameter, being significantly lower than the sausages with chia seed addition. The redness (a* parameter) remained invariable in every sample during the whole processing period. The control sausage and sausages with chia seed exhibited the highest b*, whereas the black cumin addition provided the lowest value. Our results differed from chia frankfurters [24], which presented lower a* and L* than control frankfurters at Day 21. Moreover, while L* decreased by the time, a* and b* showed similar values at Days 0 and 21 in the case of chia samples. During fermentation, the proliferation of Gram-positive microorganisms provide color stabilization due to their ability to form a nitrosylmyoglobin complex through the reaction of nitric oxide with myoglobin. This compound is responsible for the unique color in cured meat [44,45]. In our study, the nitrosylmioglobin content recorded increased broadly along the time. Sausages with seeds added provided the greatest results, especially those with higher proportion of black cumin added. [PERSON] et al. [46] pointed out that the lactic acid bacteria contribute to the synthesis of nitrosyl pigments in cured poultry meat. Other researchers explained the same phenomenon [42,43]. Therefore, our results are well-correlated with the acid lactic bacteria since the largest values for nitrosylmyoglobin were observed in the sausages with the highest lactic acid bacteria content.
# Weed Detection and Classification with Computer Vision Using a Limited Image Dataset
## 3 Results
### Hu Features  
Hu moments, which consist of seven features, were initially considered for our analysis. However, due to the complex shapes of weeds, Hu moments proved less effective, as they are better suited for simpler shapes. Consequently, the recognition rate with Hu moments remained slightly above 50%, even when the number of training images was increased to 160, as shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Training Image Number** & & **5** & **10** & **20** & **40** & **80** & **160** \\ \hline \multicolumn{2}{c}{**Test Image Number**} & & **495** & **490** & **480** & **460** & **420** & **340** \\ \hline \multirow{2}{*}{**SVM**} & precision & 40.68 & 41.03 & 38.76 & nan & nan & 48.02 \\  & recall & 25.42 & 24.46 & 33.54 & 34.09 & 37.50 & 41.47 \\ \hline \multirow{2}{*}{**RF**} & precision & 31.76 & 41.38 & 40.29 & 44.97 & 48.49 & 50.92 \\  & recall & 29.23 & 38.47 & 39.27 & 44.06 & 48.10 & 49.75 \\ \hline \multirow{2}{*}{**KNN**} & precision & nan & 35.93 & 40.58 & 42.00 & 43.12 & 47.68 \\  & recall & 26.03 & 32.31 & 36.42 & 40.91 & 42.54 & 46.47 \\ \hline \multirow{2}{*}{**ANN**} & precision & 40.14 & 46.86 & 42.13 & nan & 51.17 & 52.94 \\  & recall & 34.55 & 40.61 & 39.31 & 41.81 & 47.22 & 48.68 \\ \hline \multirow{2}{*}{**NB**} & precision & 39.18 & 43.88 & 42.45 & 44.20 & 39.46 & 42.46 \\  & recall & 35.56 & 37.55 & 35.69 & 37.86 & 36.67 & 33.14 \\ \hline \multirow{2}{*}{**GBM**} & precision & 29.67 & 36.57 & 38.33 & 43.02 & 46.61 & 49.99 \\  & recall & 27.41 & 34.49 & 36.98 & 41.81 & 45.87 & 48.63 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hu moment results.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Training Image Number** & & **5** & **10** & **20** & **40** & **80** & **160** \\ \hline \multicolumn{2}{c}{**Test Image Number**} & & **495** & **490** & **480** & **460** & **420** & **340** \\ \hline \multirow{2}{*}{**SVM**} & precision & 32.80 & 37.08 & 34.57 & 41.82 & 41.87 & 43.06 \\  & recall & 28.25 & 34.39 & 35.24 & 40.87 & 40.63 & 42.50 \\ \hline \multirow{2}{*}{**RF**} & precision & 40.99 & 43.33 & 46.72 & 49.50 & 51.63 &
## 2 Materials and Methods
### Evaluation of Application Potential
#### 2.4.5 Determination of the Film Bioactivity  
* Antioxidant activity  
The antioxidant activity of each film was assessed using the ABTS and DPPH radical scavenging assays, following the methodology described by [PERSON] et al. [38]. For the ABTS assay, the solution was diluted with water to achieve an initial absorbance of 0.700 \(\pm\) 0.020 at 734 nm. The DPPH working solution (90 \(\upmu\)M) was prepared in methanol to obtain an absorbance of 0.600 \(\pm\) 0.100 at 515 nm (Synergy H1, Biotek, Winooski, VT, USA).  
Each film sample was cut into 1 mg pieces and placed into test tubes, into which 2 to 8 mL of either the ABTS or DPPH solution was added. The tubes were kept at room temperature (25 \({}^{\circ}\)C), shielded from light. The reaction times were 6 min for ABTS and 30 min for DPPH. Absorbance readings were taken at 734 nm for ABTS and 515 nm for DPPH.  
Results were expressed as micromodes of Trolox equivalents per milligram of film (\(\upmu\)M TE/mg film). All measurements were conducted in quadruplicate.  
* Antimicrobial activity  
The antibacterial activity of the films was evaluated using the viable cell count method, as outlined by [PERSON] et al. [38]. An overnight liquid culture of each selected bacterial strain was prepared in Mueller-Hinton broth, and the optical density was adjusted to 0.2 at 610 nm, corresponding to approximately \(10^{8}\) CFU/mL. The culture was subsequently diluted in Mueller-Hinton broth to obtain an inoculum concentration of \(10^{5}\)-\(10^{6}\) CFU/mL.  
Film samples were cut into 1 cm discs (approximately 10 mg) and sterilized by exposure to UV light for 10 min on each side. The sterilized discs were then placed into sterile tubes, and 200 \(\upmu\)L of the bacterial inoculum was added to each one. The films were incubated with the inoculum at 37 \({}^{\circ}\)C for designated time intervals (0, 4, 8, 16, and 24 h). Following incubation, 1.8 mL of peptone water was added to each tube, and the mixture was homogenized until the film was fully dissolved.  
A series of four ten-fold serial dilutions was carried out in sterile peptone water. From each dilution, 20 \(\upmu\)L aliquots were plated on Mueller-Hinton agar and incubated at 37 \({}^{\circ}\)C for 24 h. The number of colonies was then recorded.  
Results were expressed as log CFU/g of film, with the detection limit set at 1.6 log CFU/g of film. Each film was tested in duplicate per time point, and every dilution was plated in duplicate, resulting in an average of four colony counts per film per time point.  
The bacterial strains used, sourced from the CBQF collection, included clinical isolates from rabbits and humans. The Gram-negative strain tested was _Salmonella enterica_ serovar Enteritidis ATCC 13076, while the Gram-positive strain was _Listeria monocytogenes_ NCTC 10357.
# Medium Access Control Protocols for Cognitive Radio Ad Hoc Networks: A Survey
## 4 Discussion
### Efficient Topology Management Is Required  
In CRAHNs, different users experience a non-uniform channel availability and diverse PU movement in their regions. Thus, sensing results in different sets of available channels and the functions of topology discovery and management become difficult tasks. Apart from this, in distributed CRAHN, the CR users monitoring the earlier channel and waiting for the length expected to finish the information exchange on the possessed connection before starting their own transmission are ignorant of the spectrum change on that particular link, which leads to inefficient spectrum use. Therefore, having some sort of synchronization mechanisms among the CR users would help to have a more efficient network with better end-to-end performance. Clustering can be an option where the cluster head can coordinate different CR users in each cluster. It may also facilitate better cooperative spectrum sensing schemes.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & **MAC Protocol** & **Transceiver** & **CCC** & **Hop** & **Multi-Channel** & **Main Function** \\ \hline \multirow{4}{*}{**Centralized**} & \multirow{2}{*}{CM-MAC [164]} & \multirow{2}{*}{Single} & \multirow{2}{*}{Global} & \multirow{2}{*}{Single} & \multirow{2}{*}{Yes} & \multirow{2}{*}{\begin{tabular}{l} Spectrum selection based on PU and SU data traffic and \\ mobility support \\ Interference-dependent contention resolution and \\ fairness among SU \\ \end{tabular} } \\  & & & & & & & \\ \cline{1-1}  & \multirow{2}{*}{\
\begin{tabular}{l} **AV-MAC** [168]** \\ **Dual receive radio MAC [69]** \\ **QoS provisioning MAC [70]** \\ \end{tabular} } & \multirow{2}{*}{Single} & \multirow{2}{*}{Dedicated} & \multirow{2}{*}{Multi} & \multirow{2}{*}{Yes} & \multirow{2}{*}{Using dual-receive capability of radios and doesn’t} \\  & & & & & & & \\ \cline{1-1}  & & & & & & & \\ \cline{1-1}  & & & & & & & \\ \hline \multirow{4}{*}{**Centralized**} & \multirow{2}{*}{
\begin{tabular}{l} **P-MAC [73]** \\ **CAD-MAC [74]** \\ **CA-MAC [75]** \\ **CA-MAC [76]** \\ **Caster-based MAC [77]** \\ **A MAC for CRAHN [78]** \\ **C-MAC [79]** \\ **C-MAC [79]** \\ **C-MAC [78]** \\ **O-MAC [79]** \\ **O-MAC [77]** \\ **O-MAC [79]** \\ **O-MAC [78]** \\ **O-MAC [79]**
## 3 Results and Discussion
### Mechanical Properties
#### 3.5.1 Microhardness
However, the SS304L/IN718 PWHT dissimilar joints showed the maximum hardness (430 \(\pm\) 30 HV) compared to the other three conditions (ST, STA, and PWHT) at the TMAZ on the IN718 side of the weld. This was owing to the grain refinement during the welding and the formation of strengthening precipitates because of the PWHT process [27]. [PERSON] et al. [27] found a similar influence of PWHT on the microstructural and mechanical characteristics of a friction-welded IN718 alloy. On the IN718 side, lower hardness values in the HAZ/TMAZ zone were observed in all three conditions (ST, STA, and PWHT) because of the grain coarsening (Table 4) [33; 34]. On the SS304L side, the hardness profile showed a decreasing trend from the FDZ to the HAZ in all three conditions (ST, STA, and PWHT). The FDZ hardness was higher than the TMAZ and BM for all the weldments on the SS304L side, owing to the grain refinement during welding. The hardness variation among zones, including FDZ, TMAZ, and base metal, on the SS304L side of both the SS304L/IN718 ST and SS304L/718 STA samples was  
Figure 14: Hardness distribution across the weld interface of dissimilar SS304L/IN718 rotary friction welded joints in the as-welded ST, STA, and PWHT conditions. identical. However, the softening of SS304L was due to the high temperatures experienced during PWHT, which resulted in a lower hardness than that of the weldments in the ST and STA conditions. In conclusion, the dissimilar SS304L/IN718 PWHT welds displayed the highest hardness (486 \(\pm\) 4 HV) among all three conditions (ST, STA, and PWHT) at the weld interface on the IN718 side, whereas the HAZ on the SS304L side had the lowest hardness (193 \(\pm\) 2 HV).