esa-sceva/satcom-chunk-collection huggingface dataset:
# Internet of Things-Assisted Vehicle Route Optimization for Municipal Solid Waste Collection
## 3 System Model  
In this section, the system model for our proposed waste collection algorithm is introduced. Our system comprises \(n\) bins strategically distributed across a region, interconnected by \(e\) bin-to-bin paths, as illustrated in Figure 1. Each bin is equipped with IoT sensors to capture volume (\(V\)), toxicity (\(v\)), and the duration the trash has been in the bin (\(t\)). The volume of waste can be measured using ultrasonic sensors [31]. The toxicity of waste measures how hazardous the waste can be for the human health [32; 33]. Typically, gas sensors can be used to measure the toxicity level of a waste [34]. The duration of trash in the bin can be measured using the time the bin was last collected. These sensor readings are communicated to a centralized location responsible for executing the route selection mechanism. Subsequently, vehicles are dispatched along the chosen routes based on the information gathered from the sensors.  
In normal route selection for the waste collection, certain parameters, such as toxicity, are used, and the route is selected that maximizes the waste collection based on toxicity, and hence, an accumulated toxicity value will be maximized.
## 3 Results  
### Feature Importance Scoring by Random Forest  
Feature importance analysis by RF ranks the significance of input variables in predicting the target output through a tree-based learning mechanism. In this study, RF was used to quantify the importance of the different combinations of retrieval indexes and atmospheric correction methods (i.e., the schemes) for the different satellites and data categories (i.e., the different scenarios). We used the RandomForestRegressor and SimpleImputer from the sklearn library [83] in Python to model the scheme importance and handle missing data, respectively. The SimpleImputer was configured to impute missing values using the mean strategy. We then trained a RandomForestRegressor model equipped with 100 estimators (individual decision trees) and set to a random state of 42 (as the seed value for the algorithm's random number generator) to ensure reproducibility. The feature importance scores were directly extracted from the fitted model, providing a quantitative measure of each scheme's contribution to the prediction accuracy.  
The results, shown in Figure 4, exhibit notable differences in scheme importance across scenarios. While in some scenarios a single scheme clearly dominated, indicating a distinct preference for that specific scheme, in other scenarios multiple schemes yielded comparable importance. Among the atmospheric correction processors, QUAC and FLAASH tended to  
Figure 3: Flowchart of this study’s methodology. AC = Atmospheric Correction, Chl-\(a\) = Chlorophyll-\(a\), ECCC = Environment and Climate Change Canada, MECP = Ministry of the Environment, Conservation and Parks (Province of Ontario), RF = Random Forest, RPAS = Remotely Piloted Aircraft System, RS = Remote Sensing, TSS = Total Suspended Solids.  
dominate for Landsat 5 and 8, Level-2 (LEDAPS) for Landsat 7, DOS1, C2 RCC, and ACOLITE for Sentinel-2. Generally, FLAASH, DOS1, Level-2, and QUAC products performed best. Out of the 28 scenarios, Level-1 (uncorrected data) only emerged as the top product in 3 scenarios, hence underscoring the critical importance of atmospheric correction prior to processing satellite data. Interestingly, iCOR and Polymer were never among the top processors, although they showed relatively high importance in several scenarios. In terms of the Chl-\(a\) indexes, I\({}_{3}\), I\({}_{4}\), and I\({}_{18}\) were favoured by Landsat 5 and 7, I\({}_{1}\), I\({}_{3}\), I\({}_{6}\), and I\({}_{24}\) by Landsat 8, and I\({}_{3}\) and I\({}_{18}\) by Sentinel-2. Overall, I\({}_{3}\) (blue-to-green ratio) emerged as the preferred index for Chl-\(a\) concentration prediction across all satellites and data categories. These results should help inform the preliminary selection of retrieval indexes and suitable atmospheric correction processors when developing multiple regression or machine learning models in future RS studies aimed at analyzing spatial-temporal Chl-\(a\) trends in lakes and other surface water bodies.
# An Efficient Dynamic Load Balancing Scheme Based on Nash Bargaining in SDN
## 5 Simulation Experiment
### Simulation Environment Settings  
In order to verify the effectiveness of the Nash bargaining based load balancing scheme (LBSNB), this scheme is compared with the under-utilization migration strategy (UMS) [12] and the multi-objective optimization scheme based on multi-objective optimization (MOS) [18]. Among these models, UMS migrates the switches managed by the overload controller to the light-load controller with the lowest resource utilization. MOS uses the genetic algorithm to optimize the load balancing degree andmigration cost and obtains a better mapping relationship. Moreover, the comparative performance indicators include the load balancing degree, controller response time, and migration cost.
## 4 Case Study
### Bilinear Numerical Analysis  
The application is performed using the 42 CrMo4 steel material. Its mechanical properties are Modulus of elasticity of 210 GPa, Poisson ratio of 0.30, and shear modulus of 80 GPa. The numerical application aims to demonstrate how the methodology enables the determination of the P-S-N field of 42 CrMo4 steel. The methodological steps to perform the bilinear analysis are as follows.  
Step 0: Collected the experimental data with bilinear behavior. The collected data are represented graphically in Figure 2, where the knee-point separates the elements into two data sets, and the failure times of both sets are provided in Table 1.  
Step 1. Based on the knee-point, the addressed two bilinear behavior groups are \(s_{1}\) and \(s_{2}\). Data were published in [17].  
Step 2. Because the \(R^{2}\) index for \(s_{1}\) data  
Step 5. By using the (\(\beta\), \(K\), and \(n\)) parameters of the set with the highest \(R^{2}\) (in this case, the parameters of \(s_{2}\)) in Equation (5), the \(\eta_{i}\) values corresponding to each stress value of \(s_{1}\) and \(s_{2}\) are given in Table 4.  
Step 6. Based on the \(R(t)\) index of Table 3 and the Weibull scale values of Table 4, from Equation (8) the equivalent failure times \(N_{eq}\) of data set \(s_{1}\), that should be incorporated to the \(s_{2}\) set data, are given in Table 5.  
Step 7. The complete data set, which includes the equivalent failure times from Table 5 into data of set \(s_{2}\) is given in Table 6.  
Step 8. Based on the Weibull family of the complete set of stress values from Table 6 (represented as \(W\) (\(\beta=5.9148\), \(\eta=779.3\))), the median stress value is \(S=732.4\) MPa.  
Step 9. From the IPL function defined in Equation (5), the \(\eta\) value in cycles corresponding to the median stress value from step 8 is \(\eta=1445.7\) cycles.  
Step 10. Using the reliability indices of \(s_{1}\) and \(s_{2}\) from Table 3 and using the \(\eta\) value from step 9 as a constant in Equation (3), the predicted failure times are given in Table 7.  
By using the maximum likelihood method and the predicted data in Equation (2), the estimated Weibull parameters are \(\beta=4.8031\), \(\eta=1445.7\) cycles, with Fisher matrix given as:  
\[\begin{pmatrix}0.7690&19.8171\\ 19.8171&5278.8121\end{pmatrix}\]  
From the Fisher matrix, the standard deviation of \(\eta\) is \(\sigma_{\eta}=\sqrt{5278.8  
The percentiles in Table 8 represent the P-S-N field around the \(\eta\) value (1445.7 cycles) corresponding to the analyzed median stress (732.4 MPa). Here, we use the median stress because the S-N curve represents median values, but any other desired stress value can be used. Therefore, the Weibull distribution that represents the median stress of the bilinear behavior in cycles is \(W\)(4.8031, 1445.7 cycles), and its P-S-N field are given in Table 8. Now, the testing plan to determine the reliability index represented by the addressed Weibull distribution is as follows.
## 2 Graph Models
Thereby, [PERSON] and [PERSON] are often credited with the foundation of the theory of random graphs. As the probability of an edge existing between each pair of nodes is equal in an Erdos-Renyi graph, this model is not well suited to WSNs, which are embedded in two (or three) dimensional space, and in which the probability of a link existing is very much higher between nodes which are geometrically close. Moreover, as discussed by [PERSON] and [PERSON] [22], Erdos-Renyi graphs do not consider correlations between different links. Therefore, we need new methods to model networks with randomness. A natural candidate for random network modelling is the class of _Random Geometric Graphs_[23]. With node set \(V\), a _geometric graph_\(G=(V,r)\) is equivalent to a graph \(G_{1}=(V,E)\), in which the vertex set \(V\) is embedded in a metric space, and \(E=\{(u,v)|\forall u,v\in V,\|u-v\|\leq r\}\). If the nodes in \(V\) are independent and identically distributed (i.i.d.) and uniformly distributed in the metric space, \(G\) is called a random geometric graph. In this case, the locations of nodes form a Poisson process if the number of nodes is Poisson distributed, or infinite in the case of an unbounded metric space. An important special case of geometric graphs is the class of unit disk graphs. A unit disk graph is a graph embedded in Euclidean space that has an edge between any two vertices whose Euclidean distance is less than 1. If the positions of vertices of the unit disk graph are random, this unit disk graph is a random graph, and it is a random geometric graph if the locations of the vertices are i.i.d. and uniformly distributed. Figure 1 shows an example of the construction of a unit disk graph. As can be seen, any pair of nodes that have a distance less than or equal to \(r=1\) are connected by an edge. [PERSON] [24] studied unit disk graphs on an infinite plane, under the name _Random Plane Networks_. He appears to be the first to suggest their applicability to connectivity studies of packet radio networks.
## 2 Materials and Methods
### Impacts of Smart Roof Solutions and Technologies
#### 2.4.1 Energy Efficiency Solutions  
To evaluate the energy savings after the rooftop renovation, the assessment of heat fluxes through the roof were quantified during the heating and cooling seasons. Different thermo-physical properties of the roofs, indicated in Figure 4, were used according to roof type: Common roof, common insulated roof (red tiles), insulated high-reflectance roof, and insulated green roof. The roofs have different values of thermal transmittance (\(U_{Road}\)) that depend on the type of insulations. \(U_{Road}\) is taken to equal 1.80 W/m\({}^{2}\)/K for common roofs and 0.24 W/m\({}^{2}\)/K for insulated roofs (according to Italian standard) and has different solar absorption coefficients (\(\alpha_{Road}\)) that depend on the roof-covering materials. The \(\alpha_{Road}\) is equal to 0.6 for common roofs with red tiles, 0.3 for light-color roofs, and 0.87 for green roofs. The quota of solar radiation changes according to the presence of vegetation, the incident global solar radiation (\(I_{i}\)), was calculated according to global solar radiation recorded by weather stations, while the quota of incident solar radiation entering a green roof (\(I_{n}\)) depends on the Leaf Area Index (\(LAI\)), which is the ratio between the green area and the underneath soil area [38], and on the short-wave extinction coefficient (\(k_{s}\)) [39]. Using green roof technology, the heat flow of solar radiation that enters the system is a net contribution taking into account the solar reflection and green absorption. Equation (2) describes the exponential law developed by [PERSON] [40] used in this work to assess the effect of green roofs on incident global solar radiation:  
\[I_{n}=I_{i}^{-k_{s},LAI} \tag{2}\]  
where:  
\(I_{n}\) is the solar irradiance entering the system (W/m\({}^{2}\));  
\(I_{i}\) is the incident solar irradiance (W/m\({}^{2}\));  
\(k_{s}\) is the short-wave extinction coefficient (-), which was assumed to equal 0.29 (values proposed for similar vegetation characteristics in [40]); and  
\(LAI\) is the ratio between the green area and the underneath soil area (-), which was assumed to equal 5 in summer, 3.5 in spring, 3 in autumn, and 0.5 in winter [38; 39]. To assess the energy savings of a building, due to the roof component, some simplified assumptions were made: (1) The heat flow rate from internal gains was constant; (2) the heat flow rate dispersed by ventilation was constant; (3) the evapotranspiration of green roofs was not considered; and (4) and the thermal capacity of different roof typologies was equal. The energy savings for space heating and cooling were quantified calculating the hourly heat flow rates before and after the rooftop retrofit interventions with the following equations [39]:  
Figure 4: Energy efficiency solutions’ scheme.
## 6 Conclusions  
In this paper we study the features of leak monitoring information and the structure of pipeline monitoring sensor networks, and present a type of pipeline leak detection and localization method based on a hierarchical model. At sensor nodes, to remove the noise disturbance and dispersion phenomena, wavelet transformation technology is first used to decompose the original leak signals, obtaining single mode signals and characteristic leak parameters. Then we set up a SVM multiple classifiers model for the initial recognition of the pipeline state. To enhance the classification effect and avoid a slow convergence rate, a bilinear search method is utilized to select optimal model parameters, which is of great importance for the recognition result. Moreover, a sigmoid function converts the initial recognition results to probability values, achieving the objectivation of the BPA function. At the sink, an improved evidence combination rule is designed for making final decision according to the recognition results from different nodes. The new rule can tackle the evidence conflict problem and increase the degree of focus of the correct proposition. To get the accurate position of a leak point, a weighted average localization algorithm based on cross-correlation TDOA is introduced. Through weighted combination of localization results of multiple groups of nodes, the position accuracy of the leak point is dramatically improved. Experimental results show that hierarchical method can obviously increase the accuracy of pipeline leak recognition (the focal degree of the right proposition has increased from 0.2344 to 0.4638) and avoid the problem of difficult decision making. Additionally, weighted average localization remarkably reduces the process error which is beneficial to localization accuracy (the maximum relative error has decreased from 4.75% to 1.75%). In this paper, we mainly focus on the linear pipeline without considering other pipeline cases. Hence in the future, we plan to carry out a large amount of research on leak detection and localization approaches of pipelines with special structures such as zig-zag or bent pipelines. Besides, we will do a number of experiments to exploit the feasibility and validity of our approach used in pipelines of other materials such as water and oil as well as underground pipelines, finally extending our method to industrial applications.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Signal pair** & **(1,4)** & **(1,5)** & **(2,4)** & **(2,5)** & **(3,4)** & **(3,5)** \\ \hline Node distance (m) & 15 & 20 & 10 & 15 & 5 & 10 \\ \(\Delta t\) (\(\mu\)s) & 1,672 & 638 & 684 & 378 & 346 & 996 \\ \(X\) (m) & 3.43 & 8.45 & 3.33 & 6.58 & 1.63 & 2.57 \\ Coordinate of leak point \(P\) (m) & 11.57 & 11.55 & 11.67 & 11.58 & 11.66 & 12.57 \\ Absolute error (m) & 0.43 & 0.45 & 0.33 & 0.42 & 0.34 & 0.57 \\ Relative error & 3.58\% & 3.75\% & 2.75\% & 3.50\% & 2.83\% & 4.75\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Localization result and error of each pair of signals.
# Broadening of Light Coupling to Waveguide Modes in Solar Cells by Disordered Grating Textures
## 3 Results and Discussion
### Local Layer Thickness
Each unit cell shows a typical characteristic in the perfect periodic case, which is quite similar to the shape of the unit cells for the texture with position disorder applied to the grating. The waveguide resonance can be understood as laterally coupled optical resonators with each unit cell forming a resonator. When each resonator exhibits the same resonance energy, the lateral interconnection leads to an extended optical mode, the waveguide mode. In the case of the texture with radius disorder, the shape of the different resonators differs significantly from each other. Therefore, the formation of an extended optical mode is hindered. In other words, any guided mode is damped, which leads to a spectrally broader resonance. From this part of the investigation, it is concluded that radius disorder has a much stronger influence on the spectral width of the waveguide mode than position disorder. It should be kept in mind that, in contrast, the position disorder has a stronger impact on the wave vector transfer than radius disorder. With these two ways of implementing disorder in a grating structure, the investigation of the impact of these two origins of broadening on the performance of a spectrally broad optoelectronic device such as a thin-film solar cell is now possible.
# Transmitter Diversity Gain Technique Aided Irregular Channel Coding for Mobile Video Transmission
## 4 Proposed System Setup
### Channel Coding Arrangements  
IRCC outer code is used in combination with URC as an inner channel encoder. Channel codes operates for the removal of the channel errors. The output codeword \(x_{i}^{\prime}\) from a set of a codeword of IRCC encoder is interleaved (II) for protecting the bits from a burst of errors by randomizing the bitstream through permutation. Interleaved sequence (\(\tilde{x_{i}}\)) is further presented to URC. The overall code rate of the system is \(R=IRCC_{code-rate}\cdot URC_{code-rate}\). The processed output \(y_{i}\) of the URC is presented to the transmission system for its delivery to the receiver.
# Manufacturing Processes of Integral Blade Rotors for Turbomachinery, Processes and New Approaches
## 2 Monolithic Components: Manufacturing Processes
### Casting  
Sand Casting is used for medium-small impellers that do not require elevated finishing accuracy and mechanical properties. This is the case of automotive turbocharger application [16]. This type of impeller does not require tough tolerances, and it is generally manufactured in big batches. These components are widely used for automotive reciprocating internal combustion engines, particularly for diesel cycles. These engines consist of a turbine and a compressor, where the impeller is connected coaxially.  
Casting impellers are used, in usual manner, for centrifugal pump impellers [17]. Impellers fabricated by sand casting are commonly made of steel, bronze, brass, aluminum or plastic. Sand casting can be used to mold high melting point components and is suitable for both small and large series sizes. However, dimensional precision and surface finish are generally limited.
## 5 Experiment Setup and Results
### Experimental Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**Effect of the Proposed Framework on Processing Time (sn)**} \\ \cline{2-4}  & \multicolumn{1}{c}{**DUC**} & \multicolumn{1}{c}{**BBC**} \\ \cline{2-4}  & **200 Words** & **400 Words** & **3 Words** \\ \hline \multirow{3}{*}{XLNet} & 196.54\(\rightarrow\)77.23\(\blacktriangle\) & 185.35\(\rightarrow\)82.17\(\blacktriangle\) & 43.26\(\rightarrow\)24.27\(\blacktriangle\) \\  & (\(-\)60.71\(\%\)) & (\(-\)55.67\(\%\)) & (\(-\)43.90\(\%\)) \\ RoBERTa & 122.33\(\rightarrow\)56.24\(\blacktriangle\) & 125.03\(\rightarrow\)64.49\(\blacktriangle\) & 32.61\(\rightarrow\)21.73\(\blacktriangle\) \\  & (\(-\)54.03\(\%\)) & (\(-\)48.42\(\%\)) & (\(-\)33.36\(\%\)) \\ DistilBERT & 64.14\(\rightarrow\)27.14\(\blacktriangle\) & 67.58\(\rightarrow\)33.43\(\blacktriangle\) & 21.57\(\rightarrow\)13.33\(\blacktriangle\) \\  & (\(-\)57.69\(\%\)) & (\(-\)50.53\(\%\)) & (\(-\)38.20\(\%\)) \\ ELECTRA & 37.84\(\rightarrow\)15.68\(\blacktriangle\) & 39.62\(\rightarrow\)19.82\(\blacktriangle\) & 11.91\(\rightarrow\)8.92\(\blacktriangle\) \\  & (\(-\)58.56\(\%\)) & (\(-\)49.97\(\%\)) & (\(-\)25.09\(\%\)) \\ ALBERT & 113.62\(\rightarrow\)45.2\(\blacktriangle\) & 114.09\(\rightarrow\)55.52\(\blacktriangle\) & 24.32\(\rightarrow\)11.71\(\blacktriangle\) \\  & (\(-\)60.22\(\%\)) & (\(-\)51.34\(\%\)) & (\(-\)51.85\(\%\)) \\ Pegasus & 4478.08\(\rightarrow\)1384.51\(\blacktriangle\) & 4843.66\(\rightarrow\)1794.01\(\blacktriangle\) & 903.73\(\rightarrow\)333.52\(\blacktriangle\) \\  & (\(-\)69.08\(\%\)) & (\(-\)62.96\(\%\)) & (\(-\)63.10\(\%\)) \\ \hline \hline \end{tabular} \(\blacktriangle\) Improvement from baseline.  
\end{table} 
Table 6: Time savings of the proposed framework for varying abstract lengths in LLMs. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Rouge Metrics** & **t-Statistic** & \(p\)**-Value** \\ \hline \multirow{6}{*}{400-word} & Rouge-1 & 5.420 & 0.0056 \\  & Rouge-2 & 6.268 & 0.0033 \\  & Rouge-L & 7.407 & 0.0018 \\  & Rouge-W & 6.909 & 0.0023 \\  & Rouge-S & 8.129 & 0.0012 \\  & Rouge-SU & 9.186 & 0.0008 \\ \hline \multirow{6}{*}{200-word} & Rouge-1 & 7.301 & 0.0019 \\  & Rouge-2 & 6.389 & 0.0031 \\ \cline{1-1}  & Rouge-L & 9.231 & 0.0008 \\ \cline{1-1}  & Rouge-W & 7.714 & 0.0015 \\ \cline{1-1}  & Rouge-S & 10.157 & 0.0005 \\ \cline{1-1}  & Rouge-SU & 8.337 & 0.0011 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Statistical validation of ROUGE improvements via paired \(t\)-tests across summary length variants.
## 2 Optical Access-Metro Networks
### Traffic Classes  
RPR supports three different traffic classes: \((i)\)**Class A** assures high priority, low latency and jitter service with guaranteed bandwidth (e.g., voice); \((ii)\)**Class B** traffic offers medium priority with predictable latency and jitter service (e.g., video); and \((iii)\)**Class C** offers low priority and best-effort service (e.g., web browsing). Traffic classes A and B are divided into two subclasses, _i.e._, A0, A1, B-CIR (committed information rate), and B-EIR (excess information rate), respectively. Bandwidth is pre-allocated for classes A0, A1, and B-CIR. A0 bandwidth is called _reserved_ and each RPR node informs the amount of required bandwidth by means of the topology discovery protocol. The reserved A0 bandwidth cannot be reclaimed by another node, even if the corresponding source node does not use it. The remaining amount of bandwidth is called _unreserved_. A portion of the unreserved bandwidth, called _reclaimable bandwidth_, is pre-allocated for classes A1 and B-CIR. Unused reclaimable bandwidth and the remaining bandwidth may be used by classes B-EIR and C.  
While the above-mentioned three traffic classes are typically found in metro-access technologies, eight traffic classes are defined in EPON. However, IEEE 802.1D layer-2 bridges might be used to map these traffic classes. IEEE 802.1D is the bridge standard which applies IEEE 802.1p to provide QoS support for bridged traffic [11]. The IEEE 802.1p standard defines eight traffic classes for Ethernet local area networks (LANs). More specifically, IEEE 802.1p uses 3 bits of each Ethernet frame header to specify its traffic class based on given QoS requirements.