esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Study Sites  
Three boreal forests sites were selected for this analysis. The first was the 84-year-old aspen site (SK-OA, established 1919, 53.63 \(\mathrm{\SIUnitSymbolN}\), \(-\)106.20 \(\mathrm{\SIUnitSymbolE}\), 2001-2009), located within the southern boundary of Prince Albert National Park, \(\sim\)50 km from Prince Albert, Canada [26, 27]. This site consists largely of trembling aspen (_Populus tremuloides Michx._), with intermittent occurrences of balsam poplar (_Populus balsamifera_ L.). The continuous understory, which reaches a mean height of 1.5-2.0 m, is composed of hazelnut (_Corylus cornuta Marsh_), and to a lesser extent wild rose (_Rosa woodsii_), willlow (_Salix_ spp.), alter (_Alnus crispa_), and a variety of grasses and herbs. The soil in the area is an Orthic Gray Luvisol that is well- to moderately well-drained, with a loam to clay loam texture. The stand density is \(\sim\)980 trees ha\({}^{-1}\), with a mean tree height of 20.1 m and an average diameter at breast height (DBH) of 20.5 cm.  
The second site was an old black spruce site (SK-OBS, established 1894, 53.98 \(\mathrm{\SIUnitSymbolN}\), \(-\)105.12 \(\mathrm{\SIUnitSymbolW}\), 2001-2009) located in the mid-boreal lowland ecoregion in the boreal plain ecozone near Prince Albert, Canada. The dominant species is black spruce (_Picea mariana Mill._), with scattered jack pine (_Pinus balsiana [PERSON]_) and tamarack (_Larix laricina DuRoi._). The understory is primarily wild rose and Labrador tea (_Ledum groenlandieum_), with a groundcover of _Sphagnum_ spp. in wetter areas, and feather mosses (_Pleurozium schreberi_) and lichens (_Claudina_ sp.) in drier areas. The soil in the area is a Gleyed Eluviated Eutric Brunisol (Peaty Phase) with imperfect to poor drainage, a 20-30 cm forest floor, and a sandy loam to loam texture developed from thick glacial deposits [26]. The stand density is \(\sim\)4330 trees ha\({}^{-1}\), with a mean tree height of 7.2 m and an average DBH of 7.1 cm.  
The third site was an old jack pine stand (SK-OJP, established 1929, 53.92 \(\mathrm{\SIUnitSymbolN}\), \(-\)104.69 \(\mathrm{\SIUnitSymbolW}\), 2001-2009). The dominant species at this site is jack pine, with a sparse understory of isolated alder clumps and a ground cover of bearberry (_Arctostaphylos uva-ursi_), lichens, and grasses. The soil in the area is an Orthic Eutric Brunisol with very good drainage and a fine sand texture. The stand density is 1320 trees ha\({}^{-1}\), with a mean tree height of 12.7 m and an average DBH of 12.9 cm [27].
# Energy Efficient Multipath Routing Algorithm for Wireless Multimedia Sensor Network
## 4 Energy Efficient GA-Based Multipath Routing
### Multipath Formation Using Genetic Algorithm  
The multipath routing algorithm uses a genetic algorithm (GA) to find multiple paths in the wireless multimedia sensor network to transfer the data. GA is employed for multipath searching in the network. The best path will be dynamically selected based on the cost function, with minimum distance, and least energy dissipation. The pseudo-code in Algorithm 3 shows the overall steps of the proposed GA-based algorithm.
## Effective Route Maintenance and Restoration Schemes in  
**Mobile _Ad Hoc_ Networks  
**[PERSON] * and [PERSON]**  
Department of Computer Science, Korea Advanced Institute of Science and Technology (KAIST), 335 Gwahangno, Yuseong-gu, Daejeon, 305-701, Korea; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +82-42-350-6395; Fax: + 82-42-350-6849.  
_Received: 13 January 2010; in revised form: 18 January 2010 / Accepted: 20 January 2010 / Published: 21 January 2010_  
**Abstract:** This study proposes a location-based hybrid routing protocol to improve data packet delivery and to reduce control message overhead in mobile _ad hoc_ networks. In mobile environments, where nodes move continuously at a high speed, it is generally difficult to maintain and restore route paths. Therefore, this study suggests a new flooding mechanism to control route paths. The essence of the proposed scheme is its effective tracking of the destination's location based on the beacon messages of the main route nodes. Through experiments based on an NS-2 simulator, the proposed scheme shows improvements in the data packet delivery ratio and reduces the amount of routing control message overhead compared with existing routing protocols such as AODV, LAR, ZRP and AODV-DFR.  
**Keywords:** route maintenance; route restoration; mobile _ad hoc_ network
## 3 Experiment and Results
### Results of Data Augmentation  
The distribution of each type of defect before and after data augmentation is depicted in Figure 10. Since data augmentation is performed at the image level, defects that were not originally targeted for augmentation may still be enhanced when they co-occur with to-be-augmented defects in the same image. This explains why the quantity of non-augmented defect classes may also fluctuate. Additionally, the random cropping process may result in the loss of targets, causing the number of target defects after augmentation to be less than the theoretical value. After data augmentation, the dataset totaled 3436 images, containing 4324 defects. Subsequently, for each type of defect, 20% of the images were extracted as the validation sets, and the remaining 80% were used as the training sets. The training sets included 2749 images with 3497 defects, and the validation sets included 687 images with 827 defects.
## 6 Discussion  
### Open Questions  
1. Principle of classification. Our classification primarily depends on the discriminative features between normal and tunnel traffic. Using fingerprint detection, plaintext tunnel traffic may be found to contain specific strings. From the point of view of traffic statistics, even encrypted tunnel traffic has different temporal and spatial characteristics than normal traffic. As a result, tunnel traffic detection and classification are based on determining how to best fit the tunnel traffic characteristics.
2. Classification of unknown tunnel protocol and passenger protocol. There are not enough self-adapting studies of detecting unknown protocols, and some classification results continue to be inaccurate. A significant number of passenger protocols in the tunnel are very similar, making classification difficult.
3. Granularity of classification. There is a need for fine-grained tunnel traffic classification, and this is currently lacking. Identifying the type of protocol to which tunnel traffic belongs is the first step but identifying applications and user behavior is much more important.
4. Efficiency of classification. The classification efficiency of most methods cannot be met by real-time tunnel traffic classification, and so they can only be trained and tested offline.
# An Evaluation of Optimization Algorithms for the Optimal Selection of GNSS Satellite Subsets
## 2 GNSS Satellite Constellation Determination
### Optimization Algorithms
#### 2.3.1 Artificial Bee Colony (ABC)  
ABC is an algorithm developed by [PERSON] (2005) to simulate the foraging behavior of bees [36]. The algorithm is population-based as it generates multiple numbers of artificial bees. ABC splits these bees into three groups: employed bees, onlooker bees, and scout bees. Each type of bee is assigned to different research tasks. Through sharing information on discovered possible solutions (quality and location in the search space) among the bees (Figure 1), ABC can converge toward the optimal solution. ABC is flexible as it can be used for various types of problems, with the requirement of very few control parameters [37; 38], which leads to reliable and robust results. However, ABC is relatively weak in local searching [39].  
Figure 1: Representation of the ABC searching process and the roles of employed scout and onlooker bees.
# A Review of Multifunctional Antenna Designs for Internet of Things
###### Abstract  
The Internet of Things (IoT) envisions the interconnection of all electronic devices, ushering in a new technological era. IoT and 5G technology are linked, complementing each other in a manner that significantly enhances their impact. As sensors become increasingly embedded in our daily lives, they transform everyday objects into \"smart\" devices. This synergy between IoT sensor networks and 5G creates a dynamic ecosystem where the infrastructure provided by 5G's high-speed, low-latency communication enables IoT devices to function more efficiently and effectively, paving the way for innovative applications and services that enhance our awareness and interactions with the world. Moreover, application-oriented and multifunctional antennas need to be developed to meet these high demands. In this review, a comprehensive analysis of IoT antennas is conducted based on their application characteristics. It is important to note that, to the best of our knowledge, this is the first time that this categorization has been performed in the literature. Indeed, comparing IoT antennas across different applications without considering their specific operational contexts is not practical. This review focuses on four primary operational fields: smart homes, smart cities, and biomedical and implantable devices.
## 5 Experimentation, Results, and Analysis
### Significance of Dynamic Context Modeling
#### 5.5.2 Adaptive Learning  
Dynamic context modeling enables DCAT to learn adaptively from the data it is trained on. This means that the model becomes more proficient at identifying the elements in an image that are crucial for understanding its context. This is particularly beneficial when the model encounters images that have uncommon or complex settings, enabling it to adapt and generate fitting captions rather than relying on generic descriptions.
# Marine-Originated Materials and Their Potential Use in Biomedicine
## 2 Seaweed-Originated Components
#### Biomedical and Food Applications of seaweed-Originated Components
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Seareed-Originated Component** & **Source** & **Application** & **Ref.** \\ \hline vitamins (vitamins A, B1, B2, B9, B12, C, D, E, and K), riboflavin, folic acid, and pantothenic acid & brown seaweeds (_Cystoseira crinita_, _Cystoseira scidoides_ and _Cytosmear_ _compressa_) & food (healthy diet, nutritional supplements) & [8] \\ \hline proteins, vitamins, minerals, dietary fiber, polyphenols, polysaccharides, sterols, essential fatty acids such as eicsogenatenenoic (EPA), and docosaseheraconic (DHA) fatty acids & seaweeds (_Porphyna_/_Pporgia_ spp. _(_Nori_), _Laminaria/Saccharina spp. _(_Kombu_), and _Underia_ spp. (_Wakime_)) & food (healthy diet, low-caloric, nutritive food) & [23] \\ \hline dietary fibers and polyphenols, carbohydrates, proteins, lipids, proline and chlorophyll contents, potassium, sodium, and fatty acids & red and brown seaweeds (_G. acressa_ and _S. wigthii_) & food (healthy diet, food supplements) & [24] \\ \hline proteins, proline, lipids, polyphenols, chlorophyll contents, dietary fibers, and carbohydrates & green seaweeds (_Illuca lactuca_, _U. prolifera_ and _U. linza_, _Enteromorpha intestinal_s, _Caulerpa_ spp., _Cladophora prolifera_, _C. vermilara_, and _C. tometosum_) and & food (healthy diet) & [27] \\ \hline essential amino acids, such as methionine, lysine, valine, and phenyl alanine & macroalgae (_Porphyna dioica_, _Porphyna umbilicalis_, _Gracilia rerranicophyllulla_, and _Ullva rigida_) & food (healthy diet) & [28] \\ \hline linolenic acid and \(\alpha\)-linolenic acid (fatty acids) and vitamins (vitamin C) & green seaweeds (_Caulerpa lentilifera_) & food (healthy diet, nutritional supplements) & [29] \\ \hline essential minerals (e.g., iron, magnesium, calcium, potassium, zinc, selenium, copper, iodine, phosphorus, and fluoride) & macroalgae (_Ullva rigida_, _Saccharina litissima_, _Laminaria digitata_, and _Laminaria luperborean_) & food (healthy diet, nutritional supplements) & [30] \\ \hline  & & biomedicine (drug delivery systems, tissue engineering): defensive barrier against any infective organism) with anti-inflammatory, antitumor, antiviral, and antimicrobial properties & [31] \\ \hline fiber, mineral content, fats and lipids, and vitamin contents & seaweeds (_Ascophullum nodosum_, _Laminaria digitata_, _Himanthalia elongate_, _Underia pinnatifida_ _Porphyna umbilicalis_, _Ulva sp., _Palmaria palmata_, and _Enteromorpha_ sp.) & biomedicine (drug delivery systems): antioxidant and enzyme inhibitor effects & [32] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Biomedical and food applications of seaweed-originated components.
## 3 Results  
### Spray Droplet Characterization  
The customization of the micro-emitter resulted in reduced cone angle and wetted parameter and enhanced vertical throw for modified irrigation micro-emitter (concavity = 50\({}^{\circ}\), cone angle = 95\({}^{\circ}\), vertical throw = 640 mm and wetted diameter = 1520 mm) (Figure 1c,e) compared to the original micro-emitter (cone angle = 150\({}^{\circ}\), vertical throw = 320 mm and wetted diameter = 2100 mm). Furthermore, modified micro-emitters produced a medium droplet size spectrum (D\({}_{\
u}\)0.5 = 256.2 \(\upmu\)m), while a hollow cone nozzle resulted in a fine droplet size spectrum (D\({}_{\
u}\)0.5 = 130.9 \(\upmu\)m) (Table 3). The pressure data indicate a marginal drop of 2 kPa between the main inlet and return outlet for both treatments. Such a small pressure drop eliminates the chances of variation in droplet characteristics due to reduction in pressure along the spray line.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Treatment**} & \multirow{2}{*}{**Date**} & \multirow{2}{*}{**Trial**} & \multicolumn{4}{c}{**Weather Parameters (Mean \(\pm\) Std. Dev.)**} \\ \cline{3-6}  & & & **Wind Speed** & **Wind** & **Air Temperature** & **Relative** \\  & & & **(m s\({}^{-1}\))** & **Direction** (\({}^{\circ}\)) * & (\({}^{\circ}\)C) & **Humidity (\%)** \\ \hline \multirow{3}{*}{Irrigation micro-emitter} & \multirow{3}{*}{22 July 2019} & 1 & 1.0 \(\pm\) 0.2 & 297.3 \(\pm\) 13.6 & 18.7 \(\pm\) 0.4 & 44.8 \(\pm\) 1.5 \\  & & 2 & 0.7 \(\pm\) 0.2 & 219.0 \(\pm\) 27.9 & 19.4 \(\pm\) 0.1 & 48.4 \(\pm\) 0.4 \\ \cline{1-1}  & & 3 & 0.6 \(\pm\) 0.3 & 216.7 \(\pm\) 37.0 & 21.0 \(\pm\) 0.2 & 49.2 \(\pm\) 1.0 \\ \hline \multirow{3}{*}{Hollow cone nozzle} & \multirow{3}{*}{24 July 2019} & 1 & 1.2 \(\pm\) 0.6 & 271.1 \(\pm\) 20.8 & 14.1 \(\pm\) 0.1 & 57.1 \(\pm\) 0.6 \\ \cline{1-1}  & & 2 & 0.9 \(\pm\) 0.4 & 254.4 \(\pm\) 32.8 & 15.8 \(\pm\) 0.1 & 57.3 \(\pm\) 1.1 \\ \cline{1-1}  & & 3 & 1.4 \(\pm\) 0.4 & 218.9 \(\pm\) 18.9 & 17.2 \(\pm\) 0.2 & 50.8 \(\pm\) 0.4 \\ \hline \hline \end{tabular}  
* Reported with reference to true north and the tree rows were oriented north-south.  
\end{table}
Table 2: Weather parameters recorded during the field data collection.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Emitter**} & \multicolumn{4}{c}{**Volume Percentile Diameter (\(\upmu\)m) *} \\ \cline{2-5}  & \(\mathbf{D_{\
u}0.1}\) & \(\mathbf{D_{\
u}0.5}\) & \(\mathbf{D_{\
u}0.9}\) & \\ \hline Modified micro-emitter & 134.6 & 256.2 & 416.8 & Medium \\ Hollow cone nozzle & 72.5 & 130.9 & 360.9 & Fine \\ \hline \hline \end{tabular}  
* Reported volume percentile diameter at a pressure of 310 kPa,
* Droplets have been categorized based on D\({}_{\
u}\)0.5 as per ASABE S-572.3 standard [29].  
\end{table}
Table 3: The volumetric droplet size distribution of the modified irrigation micro-emitter and hollow cone nozzle.
## 3 Results
### Train Running Time Delay Increase  
We employed multiple logistic regression and negative binomial regression models to examine the correlation between increases/decreases in train running time delays and trackwork (Table 3), adjusting for a set of categorical independent variables (Table 2). Our sensitivity analysis focused on understanding the impact of train delay thresholds of 5 and 10 min on this association. We assessed the statistical significance of each coefficient using the Wald chi-square test. Comprehensive summaries of these models can be found in Appendix A, Tables A1 and A2.  
The multiple logistic regression analysis presented in Table 4 reports the probability of train running time delays, categorised into delays of \(\geq\)1 min, \(\geq\)5 min, and \(\geq\)10 min, in relation to scheduled trackwork and other operational factors. The regression coefficients are significant at the 0.1% level, except for the airport train type and the impact of trackwork on delays of at least 10 min. For delays of at least 1 min, the model reveals an increase in the likelihood of delay (OR = 1.43) when trackwork is scheduled. This effect diminishes slightly for delays of 5 min or more (OR = 1.37), and becomes non-significant for substantial delays of at least 10 min (OR = 1.04). Track and train type play a considerable role in predicting delays. Quadruple tracks demonstrate a decreased probability of short and moderate delays but an increased likelihood of longer delays (OR = 1.28). Conversely, single tracks and commuter trains consistently correlate with higher odds across all delay thresholds. The analysis also indicates that unspecified passenger and high-speed trains are less likely to experience significant delays. Notably, late departures and night-time operations do not emerge as significant predictors of delay.  
The negative binomial regression analysis, summarised in Table 5, investigates the frequency of train running time delays at thresholds of \(\geq\)1 min, \(\geq\)5 min, and \(\geq\)10 min, considering other explanatory variables (track type, train type, train departure status, and day time). For delays \(\geq\)1 min, the presence of trackwork slightly increases the frequency of delays (IRR = 1.16). This effect is marginally more pronounced for delays \(\geq\)5 min (IRR = 1.20) but becomes non-significant for substantial delays of \(\geq\)10 min (IRR = 0.98). The track type shows a differential impact, with quadruple tracks slightly reducing the frequency of shorter delays (IRR = 0.95) but increasing for longer delays (IRR = 0.72). Single tracks and unspecified passenger train types tend to increase the frequency of delays across all thresholds.  
The analysis reveals significant variability across different train types in influencing train running time delay increase occurrences. For instance, intercity and regional trains consistently show a decreased frequency of delays across all delay size thresholds for intercity trains for delays \(\geq\)1 min). In contrast, although airport trains have a non-significant
## 4 Case Study and Discussion
### Simulation Setup
#### 4.1.3 SMP and REC Price  
The historical data for the yearly SMP and REC price was obtained for a one-hour time step [28; 29]. Although the historical price differed both hourly and monthly, the average monthly price was calculated to reduce the variable price effect on the simulation, as shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Season** & **Month** & **Date** & \
\begin{tabular}{c} **Number of Violating** \\ **Variation Criteria** \\ \end{tabular} &
\begin{tabular}{c} **Peak Period for** \\ **REC Multiplier** \\ \end{tabular} \\ \hline Winter & 1 & 01.20 & 34 & \\ Winter & 2 & 02.10 & 26 & 18:00–21:00 \\ Winter & 3 & 03.08 & 18 & \\ \hline Spring & 4 & 04.17 & 15 & \\ Spring & 5 & 05.09 & 10 & 19:00–22:00 \\ Spring & 6 & 06.06 & 10 & \\ \hline Summer & 7 & 07.02 & 6 & 13:00–15:00, \\ Summer & 8 & 08.20 & 10 & 19:00–21:00 \\ Summer & 9 & 09.07 & 9 & 19:00–21:00 \\ \hline Autumn & 10 & 10.01 & 18 & 18:00–21:00 \\ \hline Winter & 11 & 11.24 & 17 & \\ Winter & 12 & 12.05 & 32 & 18:00–21:00 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The number of violations of the variation criteria and peak period for the renewable energy certificate (REC) multiplier.  
Figure 3: Profiles of wind power generation variation at each 5-min time step for each month based on one-year historical data at the Haengwon wind farm.
# DCS-TransUperNet: Road Segmentation Network Based on CSwin Transformer with Dual Resolution
## 4 Experiment
### Result on DeepGlobe Dataset
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Precision** & **Recall** & **F1-Score** & **IoU** & **Inference** \\ \hline FCN-8s & 65.08 & 63.12 & 63.56 & 47.23 & 1.66 \\ U-Net & 72.33 & 65.84 & 68.93 & 52.86 & 1.83 \\ D-LinkNet & 70.79 & 68.95 & 69.85 & 53.62 & 1.75 \\ ELU-SegNet & 78.02 & 55.63 & 64.94 & 48.01 & **1.02** \\ D-ResUnet & **79.38** & 63.94 & 70.82 & 54.76 & 1.46 \\ DCS-TransUperNet & 77.94 & **69.52** & **73.48** & **56.74** & 2.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Quantitative analysis results using different methods on DeepGlobe dataset.
# Enhanced Effective Filtering Approach (eEFA) for Improving HSR Network Performance in Smart Grids
## Proposed eEFA Approach
### Discovering Passive QuadBox Rings
#### 3.2.2 Bidirectional Communications  
In bidirectional communications, unlike in the case of EFA, eEFA does not use any control message to detect and lock passive QuadBox rings. Instead, eEFA uses the first replied frame sent by the destination DANH to discover passive QuadBox rings.  
When the destination DANH receives the first sent frame, it replies by sending a unicast frame back to the source. Upon receiving the first replied frame, trunk QuadBoxes detect passive QuadBox rings, as shown in Figure 7.  
Figure 8a,b show the processes of forwarding unicast frames between the source DANH and the destination DANH in EFA and eEFA, respectively.
## 3 Implementation of AOG Network Mechanism within OENNs  
### Basics of OENNs  
The OENN network used in this research is based on the network structure proposed in [17]. The difference between the two approaches in calculating network output \(y(t)\) is in the existence of hormone sensitivity parameter, \(S_{j}\). In [17], \(S_{j}\) is introduced to apply sensitivity features of excitatory and inhibitory glands. In this novel work, the algorithm is simplified, different gland structure (excitatory and inhibitory glands) does not exist, and only one gland unit (AOG mechanism) is introduced. This setup results in excluding parameter \(S_{j}\) from the mathematical apparatus, which is performed by making \(S_{j}=1\) for all proposed cases. That enables the network to calculate the output as follows:  
\[y(t)=\sum_{i=1}^{n}\big{(}\omega_{i}(t)C_{g}(t)\big{)}\phi_{i}(X)+R\big{(}X,m,C_{ g}(t)\big{)} \tag{18}\]  
where \(X\) is the input value, \(\omega_{i}\) is the \(i\)-th network weight coefficient, \(C_{g}\) is the concentration of hormones of a single gland, \(\phi_{i}\) is an orthogonal function (processing element), \(t\) is a time instance, and \(R(X,m,C_{g}(t))\) represents an expansion error, which is decreasing when the number of expansion terms \(m\) is increasing. The main difference between [17] and the novel network is building the orthogonal part of the structure. In [17] Chebyshev functions were used, while in this paper, previously described Gegenbauer functions will be utilized.