esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Agriculture Applications Using Deep Learning Technologies: A Survey
## 6 Discussion
### Advantages and Disadvantages of Deep Learning  
The advantage of deep learning is that features are automatically deduced and optimally tuned for the desired outcome. Another benefit is that the same neural network-based approach can be applied to several different applications and data types. Furthermore, the deep learning architecture is flexible and can be adapted to new problems in the future. Deep learning also offers good generalization performance. While it takes more time to train deep learning models than other traditional approaches, its efficiency in testing is quite fast. One of the notable drawbacks of deep learning is that it requires many datasets to give better performance, even if data augmentation techniques are used. Few publicly available datasets exist in agriculture, and researchers need to develop their own sets of images in many cases. This can take many hours or days of work.
## 3 Experimental and Calculation Procedures
### Investigation Methods
#### 3.3.4 Fourier Transformed Infrared/attenuated Total Reflection Analysis (FTIR/ATR)  
Fourier Transformed Infrared connected with Attenuated Total Reflection (FTIR/ATR) analysis was conducted by using IR spectrometer TENSOR 27 (Brucker, Germany) equipped with the diamond crystal. The spectra were recorded in the spectral range of 400-4000 cm\({}^{-1}\).
# Mode-Matching-Based Sound Field Recording and Synthesis with Circular Double-Layer Arrays
## 1 Introduction
### Kirchhoff-Helmholtz Integral  
Let \(S\) be a two-dimensional volume without sound sources, bounded by surface \(\delta S\). Sound pressure \(P(\mathbf{r},k)\) at point \(\mathbf{r}\) for wavenumber \(k=2\pi f/c\) is uniquely defined from sound pressures \(P(\mathbf{r}_{0},k)\) and particle velocities \(\partial P(\mathbf{r}_{0},k)/\partial\mathbf{n}\) at boundary \(\delta S\) by the KH integral [40]:  
\[\alpha P(\mathbf{r},k)=\int_{S}\left\{\frac{\partial P(\mathbf{r}_{0},k)}{\partial\bm {n}}G(\mathbf{r},\mathbf{r}_{0},k)-P(\mathbf{r}_{0},k)\frac{\partial G(\mathbf{r},\mathbf{r}_{0},k )}{\partial\mathbf{n}}\right\}d\mathbf{r}_{0}, \tag{1}\]  
where \(\alpha=1\) if \(\mathbf{r}\) is inside \(S\), \(\alpha=1/2\) if \(\mathbf{r}\) is on \(\delta S\) and \(\alpha=0\) if \(\mathbf{r}\) is outside \(S\). \(f\) and \(c\) are the temporal frequency and the speed of sound, respectively. The two-dimensional free-field Green's function is given:  
\[G(\mathbf{r},\mathbf{r}_{0},k)=\frac{j}{4}H_{0}(k|\mathbf{r}-\mathbf{r}_{0}|), \tag{2}\]  
where \(j=\sqrt{-1}\) and \(H_{m}\) is the \(m\)-th order Hankel function of the first kind. In the conventional LS and GSVD methods for recording a primary sound field and synthesizing it without exterior propagation, double-layer receivers and secondary sources are, respectively, introduced to achieve monopole and approximated dipole distributions on \(\delta S\) and simultaneously control both the sound pressures and their derivatives based on the KH integral.
## 3 Experiments and Results
### Classification with Random Forest  
In this experiment, the Random Forest model was used with the aid of the MATLAB method called \"TreeBagger\". The model was tested by varying two parameters: number of Cross-Validation and number of trees generated. The type of Cross-Validation applied for this first model was the CV Holdout with a rate of 70-30%, meaning that the 70% of data was used for model training and the 30% was used for the testing phase. To validate the model, the Holdout Cross-Validation technique was applied with a number of iterations equal to 50. The number of trees of RF varied as 1, 5, 10, 100, 1000, and 10,000. Table 1 shows the experimental results obtained.  
The Random Forest model that obtained the best results is the one with the highest number of trees, namely 10,000, which scored 94.32% for the Accuracy metric, 96.79% for the Sensitivity, and 91.85% for the Specificity, respectively. However, it is worth noting that in any case the metrics values are above 90% and that, with a sensibly lower number of trees, 1000 instead of 10,000, results are almost comparable. This suggests that 1000 trees can be considered as a good parameter to tune up the algorithm. Finally, as to the Specificity, the score is almost 91% from 5 trees to the highest number.
# Ant Colony Optimization Task Scheduling Algorithm for SWIM Based on Load Balancing
## 3 SWIM Load Balancing Requirements and Related Definitions  
### SWIM Load Balancing Requirements  
SWIM is a large-scale network system with high integration, which integrates and manages internal resources. In order to improve the utilization rate of system information and the sharing rate of resources, it is necessary to solve the problem of system task scheduling load balancing and find suitable nodes to deal with user needs. This makes SWIM load balancing and task scheduling more complicated and difficult. The selection of a resource node with a smaller load to complete user requirements is a very challenging task.  
The SWIM infrastructure has a huge amount of computing and storage resources. Existing and future civil aviation applications require these resources to have the ability to perform large-scale, real-time interactions. This can result in a huge load on the data center, and it is easy to produce load imbalance. Different from load balancing in small database systems, SWIM--as a large-scale data center network--needs to respond to a high throughput of concurrent requests. Resource task scheduling and allocation become key, which puts forward higher requirements for load balancing methods [17].  
(1) Large-scale information network systems have a huge amount of data resources, so a traditional load balancer cannot meet the application requirements. It is necessary to design a new load balancing method.  
(2) Although the complex load balancing algorithm can achieve a good balancing effect, it occupies too much of the system's own resources. Especially in the case of a large number of system resource nodes, there will be risks of algorithm dead-cycle or paralysis. Therefore, it is also necessary to design a lightweight load balancing algorithm that can achieve a good load balancing effect and has low algorithm complexity.  
(3) The load balancing method must work under conditions of a large amount of data, in the form of concurrent requests, as well as satisfy the requirement of reasonable load allocation in the case of resource competition.
# Green Production and Biotechnological Applications of Cell Wall Lytic Enzymes
## 3 Degradation of Lignocellulose by CWLEs
### Cellulases and Lytic Polysaccharide Mono-Oxygenases
In order to maintain enzyme activity, the copper-containing active site of LPMO must be reduced after each cycle of reaction (e.g., microbial cellobiose dehydrogenase (CDH) can restore electrons to LPMO by oxidizing cellobiose units) [26]. Recently, [PERSON] and collaborators [27] demonstrated that phenolic molecules released from lignin by the action of lacases work as electron donors of LPMOs as well, indicating that lignin decomposition can boost cellulose oxidative cleavage. Moreover, lignin, if not efficiently removed, adsorbs to the CBMs of endoglucanases and cellobiohydrolases in an irreversible manner and reducing their loading in the enzymatic mixture [28]. However, further studies are required for isolating novel polyphenol-oxidases  
Figure 2: Schematic representation of cell wall polysaccharides and their degradation by CWLEs. (**a**) Degradation of (left) amorphous and (right) crystalline cellulose. Intermediate (yellow) and end-products (red) from each enzymatic reaction are shown. Oxidized glucose (i.e., gluconic acid) is represented as a rectangle. Cellobiose oxidation by cellobiose dehydrogenase (CDH) restores electrons (e-) to the lytic polysaccharide monooxygenase (LPMO). (**b**) Degradation of arabinylan, here reported as representative hemicellulose polysaccharide. Arabinose residues are in grey. Intermediate (turquoise) and end-products (red) from each enzymatic reaction are shown. (**c**) Degradation of substituted homogalacturonan, here reported as representative pectin polysaccharide. Intermediate (green) and end-products (red) of each reaction are reported. Acetyl and methyl groups are indicated as white and black circles, respectively. As a reference, the reducing end (RE) is indicated on the oligosaccharides in the white rectangle. \(\alpha\)AF: \(\alpha\)-arabinofuranosidase, \(\beta\)G: \(\beta\)-glucosidase, \(\beta\)X: \(\beta\)-xylosidase, CBH(I): nonreducing-end cellobiohydrolase, CBH(II): reducing-end cellobiohydrolase, CDH: cellobiose dehydrogenase, EG: endoglucanase, eG: exoglucanase, EP: endopolygalacturonase, eP: exopolygalacturonase, EX: endoxylanase, LPMO C1: C1-oxidizing LPMO, LPMO C4: C4-oxidizing LPMO, PAE: pectin-acetylesterase, and PME: pectin-methylesterase. and lacases that may synergistically cooperate with LPMOs and cellulases to efficiently convert cellulose into fermentable sugars [14].
# The Limited Impact of Running Performance on Football Success in the Turkish Super League
###### Abstract  
Given that performance in football depends on tactical, technical, physical, and mental skills, the purpose of this study was to investigate whether there are differences in running performance between winning and non-winning teams in the Turkish League, taking into account the influence of game location and the comparative quality of the team and its opponents. Utilizing a dataset from the 2021-2022 season provided by InStat Fitness, an optical tracking technology platform certified by FIFA, the analysis included 185 matches after adjusting for matches with red card incidents. The research employed both two-way ANCOVA and binary logistic regression analyses to explore the relationships between running performance (categorized into four intensity zones) and match results, considering factors such as match location and teams' strength. The results of the two-way ANCOVAs indicate that running performance metrics, specifically the distances covered at different intensities, even in cases where statistically significant differences are observed, have small practical significance (partial eta squared \(\leq\) 0.03 in all cases). Conversely, as shown by the binary logistic regression, home advantage triples the probability of winning (_p_ < 0.001, Exp(B) = 3.119), while the increase in probability caused by team quality (_p_ < 0.001, Exp(B) = 1.085) and the decrease caused by opponent quality (_p_ < 0.001, Exp(B) = 0.911) are also significant. The conclusions highlight that running performance metrics are not decisive predictors of match outcomes in professional football. This suggests the importance of integrating tactical, technical, and psychological factors into team preparation and performance analysis. This study underscores the need for future research to adopt dynamic methods that reflect the game's fluid nature and to explore these relationships across various leagues and seasons to enhance the generalizability of the findings.  
p 2025 15 637 +
Footnote †: journal: Applied sciences  
Article
# Testing an \"IoT\" Tide Gauge Network for Coastal Monitoring
## 1 Introduction  
### Beach Morphology Changes  
Understanding morphological change in the intertidal zone is important for coastal flood management [1], as beaches often provide the first defense against extreme sea levels and waves. Beaches offer natural coastal protection and can reduce some of the impact of high tides with storm surges and waves [2, 3].  
Beaches are dynamic and under constant change; the overall sediment load tends to move onshore in the summer and offshore during the winter. In addition, large single event storms can move significant amounts of sediment along and across a coastal frontage [4]. In these vulnerable locations, it is common practice for coastal engineers to regularly monitor sediment transport and sedimentation, using survey methods such as beach profiles and spatial surveys with mobile GPS devices, photography and video, and LiDAR surveys [5], which can be costly and time-consuming.  
A recent technique developed by [PERSON] et al. [6] applies the \"waterline method\" to a set of marine X-band radar images to produce a time-series of spatial and temporal images equivalent to digital elevation models (DEMs). The accuracy of intertidal morphology obtained by this \"waterline method\" can be significantly improved when combined with tidal data obtained from within the radar survey operating (spatial and temporal) window. The advantage of this technique is that it is relatively inexpensive to install and can provide a detailed picture of the daily and weekly morphological changes within the intertidal zone, and thus supports strategic planning and management of coastal defenses. Indeed, these types of data can help identify where sections of the beach have changed and provide information to determine the best time to intervene with beach replenishment schemes [7].  
An \"Internet of Things\" (IoT) tide gauge network will be capable of providing accurate sea-level data, be portable and easily deployed. It should also be low cost so as, for example, to provide redundancy in case of failure and loss and supplement any existing local tidegauges. Furthermore, IoT-enabled devices using an ultra narrowband wide area network such as Sigfox would provide low-power communication and transfer of data.  
Therefore, developing a low-cost tide gauge network that is easy to deploy is necessary to support routine monitoring of the coastal morphology, for example, in combination with X-band surveys. The near-real-time data aspect of the tide gauge network allows rapid production of morphological maps for coastal engineers to quickly identify significant changes (in the sediment distribution, particularly after storms) and timely identification and notification of faults in the network. Furthermore, this type of low-cost tide gauge network could supplement other regional tidal gauge networks, including those for safe port navigation [8] and for tsunami/surge detection [9], and has the potential to provide enough spatial tidal data to fully understand complex coastal locations, e.g., macro-tidal estuaries.
## 6 Numerical Results
### Analysis of Network Performance
#### 6.4.3 Network Slice Prioritization  
Next, two prioritization policies described in Section 3.4 and applied in network slicing, namely SP-FH and DP-FH, were evaluated. Figure 11 shows the results comparing both policies in terms of the number of active PPs (denoted by bars) as a function of the number of RUs (\(R\)). In addition, a relative difference between the results of the policies is presented. The results were averaged over \(\mu=\{1,2,3\}\).  
As seen in Figure 11, assigning different priorities to the FH packets belonging to the network slices of diverse FH latency requirements (in DP-FH) leads to lower PP requirements than when applying the same priorities (in SP-FH). This is achieved thanks to reducing the buffering latencies of the latency-sensitive URRLC FH packets as they are served with a higher priority than the less sensitive eMBB FH packets. Consequently, more distant PP nodes can be selected for the URRLC slice, and the overall number of active PPs may be reduced. In the case considered, the relative difference in performance is a few percent for a lower number of RUs and between 10-20% for larger \(R\).
## 2 Materials and Methods
### Color Space Transform  
The CIELAB color space is mainly composed of three dimensions: L, a, and b, where the L channel represents information on brightness, and a and b represent the dimension of color components [13; 14; 15]. Lab colors are designed to approximate human vision. Its L component closely matches human brightness perception. Therefore, modifying the L component can be used to adjust the brightness contrast. These transformations are difficult in RGB. Many \"colors\" in Lab space go beyond the visual field of human vision; transforming brightness, chroma, and even hue in processing. [PERSON] claims that it is useful to use imaginary colors between multiple steps in image manipulation [16]. This article uses the L channel in CIELAB to make grayscale images for the next steps.  
Since the RGB color space cannot be directly transformed to the CIELAB color space, we must first transform it into the CIEXYZ color space before converting it to the CIELAB color space. The conversion method of CIEXYZ is to reduce the range of the \(R\), \(G\), and \(B\) channels to [0,1] through Equation (1), then we can obtain the \(X\), \(Y\), and \(Z\) channels in the CIEXYZ color space through Equation (2).  
\[R_{S}=\frac{R}{255},\;G_{S}=\frac{G}{255},\;B_{S}=\frac{B}{255}, \tag{1}\]  
\[\left[\begin{array}{c}X\\ Y\\ Z\end{array}\right]=\left[\begin{array}{ccc}0.412453&0.357580&0.180423\\ 0.212671&0.715160&0.072169\\ 0.019334&0.119193&0.950227\end{array}\right]\left[\begin{array}{c}R_{S}\\ G_{S}\\ B_{S}\end{array}\right]. \tag{2}\]  
After obtaining the \(X\), \(Y\), and \(Z\) channels, they are then converted into three values of X_0, Y_0, and Z_0 using Equation (3). Finally, we can then obtain the L, a, and b channels in the CIELAB color space through the conversion of Equations (4) to (6). We will only choose the L-channel, which provides information on the brightness. The range of the L-channel value is between 0 and 100, and can represent pure black to pure white. You can see the example of the L-channel image in Figure 2, and although we only chose L-channel information, the image still did not change much.  
\[X_{0}=\frac{X}{0.950456},\;Y_{0}=Y,\;Z_{0}=\frac{Z}{1.089058}. \tag{3}\]  
Figure 1: Flowchart of the proposed method. OCTA: optical coherence tomography angiography; CNV: choroidal neovascularization.  
\[L^{*}=166\;f(Y_{0})-16. \tag{4}\]  
\[a^{*}=500(f(X_{0})-f(Y_{0})). \tag{5}\]  
\[b^{*}=200(f(Y_{0})-f(Z_{0})). \tag{6}\]
## 2 Related Work
### Traffic Classification Methods Based on ML  
ML-based methods mainly achieve traffic classification by manually selecting features and then training a ML classifier to associate the feature set with known traffic classes [12].  
[PERSON] et al. [13] proposed an improved network traffic classification model based on support vector machine to classify network traffic, which achieved a classification accuracy of 97.2% and the training time was 0.31 s. [PERSON] and [PERSON] [14] proposed an ICS traffic classification method, which achieved 100% accuracy by extracting data features, data imbalance processing, and ensemble learning method. [PERSON] et al. [15] designed two feature selection methods to select traffic features, then used the feature data to train a variety of tree-based classification models to classify network traffic. [PERSON] et al. [16] proposed a method for ICS traffic detection based on measurement data. The method used a variety of ML algorithms to monitor the data flow from alternator and selected relevant features from a large amount of data manually. The random forest had the best performance; the accuracy was about 99% and the training time was 2.21 s, while the predicting time was 0.0505 s. [PERSON] et al. [17] applied an ML algorithm to traffic classification,manually selected flow features and industrial control protocol keyword features, and achieved 99% classification accuracy with a classification time of 0.187 s.  
ML-based methods need expert experience when extracting features, which limits the generality of the methods.
# A Case Study on Distresses of Concrete Pavements Supported on a Retaining Wall
## 3 Evaluation of the Performance of the Subject Section
### Evaluation of Concrete Pavement Structure
#### 3.1.2. Tie-Bar Failure Modes  
In the design and construction of CRCP, short pieces of reinforcing steel (i.e., tie-bars) are used across the longitudinal construction joints to keep the two adjoining slabs from separating and keep the pavement surface across the joints flat. As previously mentioned, faulting at longitudinal joints and lane separation have been identified as major distresses in the subject section. Lane separations in CRCP have been reported in many projects in Texas, and efforts were made to resolve this issue [45]. One recommendation was the placement of additional tie-bars, particularly when constructing multiple lanes. In 2009, the Texas Department of Transportation (TxDOT) revised its CRCP Design Standards to incorporate this recommendation, decreasing tie-bar spacing from 4 ft (1.2 m) to 2 ft (0.6 m). However, the subject section in this study was constructed in 1997, and, therefore, tie-bars were placed with 4 ft (1.2 m) spacing.  
Figure 7 shows two types of tie-bar failure modes observed in the subject section. Figure 7 clearly shows a necking failure and suggests that the tie-bar failed in tension. On the other hand, Figure 7b shows that the tie-bar was sheared off with no sign of necking. These two failure modes indicate that the concrete slab experienced both lateral and vertical movements. These tie-bar failures were observed in most sections where lane separation had occurred.  
Figure 6: Results of FWD testing: (**a**) deflections along the subject sections and (**b**) colored profile map of slab deflections.
# Design and Development of an SVM-Powered Underwater Acoustic Modem
## 3 Results  
The following results demonstrate the effectiveness of the SVM and CWT in classifying signals for underwater acoustic communication. Detailed analysis results of the signal classification accuracy, feature extraction robustness, and system performance under varying conditions are presented, illustrating the reliability and the efficacy of the methodologies employed.
## 2 Methodologies and Dataset
### Random Forest Regression  
Random forest is a supervised learning algorithm consisting of an ensemble of decision trees [23]. It is used for both classification and regression problems. The RF algorithm is trained through both bootstrap aggregating and bagging. An RF algorithm is built using decision trees. A decision tree has three parts-root nodes, decision nodes, and leaf nodes. The root node comprises the whole predictor space. The trees used in RF are based on binary recursion partitioning trees. Leaf nodes are nodes that have not been split and form the ultimate decision. The dataset was first split into training and testing datasets. A sliding window approach was used for annual rainfall prediction, with the window size remaining fixed. The main hyperparameter was the number of estimators or the number of trees in the forest and the number of random states. [PERSON]'s Randomized Search CV method was used to define a set of optimum hyperparameter values Algorithm 2.  
```
1.  For \(n=1\) to \(N\): (i)  Draw a bootstrap sample of size \(s^{\prime}\) from training data. (ii)  Grow an RF tree \(R_{n}\) to the bootstrapped data, by repeating the below steps for each leaf node of the tree, until the minimum node size is reached. Steps (a)  Select '\(K\) variables randomly. (b)  Choose the best split-point among '\(K\)' variables out of '\(p^{\prime}\)' variables. (c)  Split the node into two nodes. 2. Output the ensemble of trees \(\{R_{n}\}\).The predictions from each tree at a point '\(x^{\prime}\)' are averaged for regression.
```  
**Algorithm 2**Random Forest for Regression