esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 QoS Aware Multipath Routing Protocols for WMSNs
### Lcmr  
Using the ad-hoc on-demand distance vector routing (AODV) [123], [PERSON] and [PERSON] developed least common multiple based routing (LCMR) [51]. Instead of selecting the shortest path by the number of hops, LCMR uses the routing time (end-to-end delay) to choose multiple paths. In the route discovery process, the source node will accept a route reply message RREP only if it arrives before a given deadline. For each RREP message, the source node identifies the routing time spent by the respective route request message RREQ to reach the destination. Among the accepted \(n\) paths with routing time \(\{T_{1},T_{2},\cdots,T_{n}\}\), the least common multiple \(L\) of \(\{T_{1},T_{2},\cdots,T_{n}\}\) is calculated. The number of packets sent along a path \(i\) is then decided such that out of \(k=\sum_{i=1}^{n}L/T_{i}\) packets, \(L/T_{i}\) packets will be sent along path \(i\). Therefore, the total time used to deliver \(k\) packets is the maximum routing time \(T_{max}\) of \(\{T_{1},T_{2},\cdots,T_{n}\}\).  
Pros and Cons. With end-to-end routing time as the metric, LCMR avoids heavily congested routes during its route discovery process. The total transmission time is also reduced by adjusting the number of packets assigned to a path according to the times that the least common multiple \(L\) has over the routing time \(T_{i}\) of the path. However, evenly distributing traffic loads among different paths in the time domain can lead the path with the least end-to-end routing time to take the most traffic burden and can cause early node death. Also LCMR does not adapt well to network dynamics such as congestion during data transmission or route breakage. And the inability to guarantee path disjointness decreases the possible gain of multipath routing.  
Figure 10: Division of Topology in GEAM.
## 2 Data and Methods
### SSD Network Framework
#### 2.2.2 Default Bounding Boxes and Negative Sample Generation  
In the initial phase of training, it is necessary to find out which default bounding box matches well with the bounding boxes of the ground truth samples. The default generated bounding boxes vary with the location and aspect ratio, and a scale process is applied by matching each ground truth box to a default box with the best jaccard overlapping value, which should be higher than 0.5 threshold. This condition facilitates the learning process and allows the network to predict high scores for multiple overlapping default boxes, rather than selecting only those that have the maximum overlap.  
Figure 3: Augmented data: (**a**) original image chip; (**b**) Rotated and randomly scaled in HSV; (**c**) Rotated, randomly cropped, and scaled in HSV.  
To handle different object scales, SSD utilizes feature maps that were extracted from several different layers in a single network. For this aim, a fixed number of default bounding boxes should be produced at different scales and aspect ratios in each region of the extracted feature maps. Six levels of aspect ratios were set supposing \(a_{r}\in\{1,\,2,\,3,\,1/2,\,1/3\}\) and \(s_{k}\) is the scale of the \(k\)-th square feature map for generating default boxes. The sixth one is generated for the aspect ratio of \(1\) with the scale of \(s^{\prime}{}_{k}=\sqrt{s_{k}s_{k}+1}\). Therefore, the width (\(w_{k}^{a}=s_{k}\sqrt{a_{r}}\)) and height (\(h_{k}^{a}=s_{k}\sqrt{a_{r}}\)) can be computed for each default box. Figure 3 illustrates how the generated default bounding boxes on a \(5\times 5\)-feature map are represented on the input image and overlap with the possible objects (Figure 5). For this research,  
Figure 4: SSD architecture that uses Inception V2 as a base network with 32 as the batch size at training.  
150 bounding boxes were generated. At the same time, each of them represents the predictions in the evaluation step.  
After the matching phase, which is performed at the beginning of the training, most of the default boxes are set as negatives. Instead of using all the negative examples to protect the balance with the positive examples, the confidence loss for each default box was calculated and three of them with the highest scores were selected, so the ratio between the negatives and positives is not more than 3:1. This ratio is found to provide faster optimization and training with higher accuracy [36].
## 3 Results and Discussion
### Performance of the Integrated Geothermal System
For this reason, the integrated CHP system only produces electricity when the electric load is larger than 91.0%, corresponding to the purple area in Figure 6.  
Figure 6 highlights the geothermal flow used in the ORC, the electric and thermal power of the integrated CHP system as a function of the electric load. Furthermore, the energy utilisation factor, the marginal efficiency, and the electric efficiency are plotted in Figure 6b. In particular, the minimum electric load is equal to 50%, owing to the constraints of the minimum pressure at the ORC turbine inlet (1.03 \(p_{crit}\)). The corresponding electric and thermal power are equal to 50.7 kW\({}_{\text{el}}\) and 249.5 kW\({}_{\text{th}}\), whereas electric efficiency is 8.7%. A slight variation in the marginal efficiency is observed (from 16.0% to 17.7%) whereas _EUF_ is more sensitive to the load and ranges between 25.2% and 50.9% (Figure 6b). The geothermal temperature at the exit of the evaporator ranges between 95.9 \({}^{\circ}\)C (at full load) and 70.0  
Figure 5: Multi-objective optimisation for the selection of the proper system configuration. \({}^{\circ}\)C (at minimum). For this purpose, it is worth noting that the adopted parallel arrangement permits to satisfy the high-temperature heat request for all the operating conditions.
## 4 Available Capacity Modeling Based on Terminal Voltage Drop
### Constructing the Health Factor  
In order to reflect the trend of the terminal voltage drop during the resting period after the battery was fully charged, the following features are selected to construct the health factor.  
(1) The terminal voltage drop after the battery rests for 5, 10, 15, 20, 25 and 30 min;  
(2) The terminal voltage drop when the battery begins to rest, from the 5 th to 10 th minute, 10 th to 15 th minute, 15 th to 20 th minute, 20 th to 25 th minute and 25 th to 30 th minute. The reason for choosing these characteristics as the health factor is that, the terminal voltage drop over time is nonlinear and the terminal voltage drop with the aging of the battery is also nonlinear. Choosing different features as the health factor will affect the accuracy of the results.
# Comparison of Selected Parameters of a Planetary
## 5 Discussion
The specific sliding ratio values of the calculated convex-concave teeth flanks were lower than the involute ones, as seen in the comparison in Figure 9. The gear pairs with involute teeth flanks showed a high value for the specific slide ratio due to high contact ratio. The lower contact ratio of \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Gear Pair**} & \multirow{2}{*}{
\begin{tabular}{c} **Point of** \\ **the Path** \\ **of Contact** \\ \end{tabular} } & \multicolumn{4}{c}{**Slide Ratio \(v_{ss}\) (-)**} & \multicolumn{2}{c}{**Flank Type Ratio**} \\ \cline{3-6}  & & \multicolumn{2}{c}{**Involute Teeth Flanks**} & \multicolumn{2}{c}{**Convex–Concave Teeth Flanks**} & \multicolumn{2}{c}{\(v_{\text{sSC-C}}\)/\(v_{\text{ssl}NV}\) (-)**} \\ \cline{3-6}  & & **Pinion** & **Gear** & **Pinion** & **Gear** & **Pinion** & **Gear** \\ \hline \multirow{2}{*}{1-2} & A & \(-\)13.4628 & 0.9308 & \(-\)1.1526 & 0.5355 & 0.0856 & 0.5752 \\  & E & 0.8436 & \(-\)5.3937 & 0.5079 & \(-\)1.0323 & 0.6021 & 0.1914 \\ \hline \multirow{2}{*}{2-3} & A & \(-\)5.0521 & \(-\)0.3770 & \(-\)0.2891 & \(-\)0.1869 & 0.0572 & 0.4958 \\  & E & 0.2738 & 0.8348 & 0.1575 & 0.2242 & 0.5752 & 0.2686 \\ \hline \multirow{2}{*}{4-5} & A & \(-\)5.5229 & \(-\)0.3595 & \(-\)0.2574 & \(-\)0.1709 & 0.0466 & 0.4752 \\  & E & 0.2645 & 0.8467 & 0.1459 & 0.2047 & 0.5518 & 0.2418 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Slide ratios of the partial gear pairs of the calculated gearbox. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Gear Pair**} & \multicolumn{2}{c}{**Contact Ratio \(\varepsilon_{\alpha}\) (-)**} \\ \cline{2-3}  & \multicolumn{2}{c}{**Involute Teeth Flanks**} & \multicolumn{2}{c}{**Convex–Concave Teeth Flanks**} \\ \hline
1-2 & 1.5055 & 1.1567 \\
2-3 & 1.6893 & 1.1251 \\
4-5 & 1.7017 & 1.1025 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Contact ratios of the partial gear pairs of the calculated gearbox. the convex-concave teeth led to better behavior of the teeth flanks in terms of the wear and scoring resistance. The biggest drop of the specific slide ratio was also observed for the gear pair 4-5 at the starting point of the path of contact A, where the specific slide ratio value in the convex-concave teeth flanks of the pinion dropped to 4.66% of the value calculated for the involute teeth flanks (Table 4).
# An Empirical Study on Ensemble of Segmentation Approaches
## 3 Results
### Experiments
#### 3.3.3 Comparison with the Literature  
For comparison with other approaches in the literature, the results of our best ensembles are reported in full in the different datasets of polyp segmentation (Table 5) and skin detection (Table 6). The following tests clearly show that _FH_(2) + 2 \(\times\)_PVT_(2) obtains a state-of-the-art performance. In LEUKO, the authors of the dataset report an IoU of 0.842, and _FH_(2) + 2 \(\times\)_PVT_(2) obtains a higher IoU of 0.916. In EMicro, the authors of the dataset report a dice score of 0.884, and _FH_(2) + 2 \(\times\)_PVT_(2) obtains a higher dice of 0.924. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & **DA** & **PRAT** & **MCG** & **UC** & **CMQ** & **SFA** & **HGR** & **SCH** & **VMD** & **ECU** & **VT** & _AVG_ \\ \hline _ERN101_(1) & DA1 & 0.922 & 0.887 & 0.923 & 0.823 & 0.948 & 0.969 & 0.750 & 0.748 & 0.948 & 0.796 & 0.871 \\ _ERN101_(10) & DA1 & 0.924 & 0.887 & 0.920 & 0.845 & 0.952 & **0.971** & 0.778 & 0.754 & 0.950 & 0.794 & 0.878 \\ _ELoss101_(10) & DA1 & 0.926 & 0.892 & 0.923 & 0.844 & **0.956** & **0.971** & 0.777 & 0.751 & 0.953 & 0.807 & 0.880 \\ _ELossMix_(10) & DA1/DA2 & 0.924 & 0.893 & 0.929 & 0.850 & **0.956** & 0.970 & 0.789 & 0.739 & 0.952 & 0.829 & 0.883 \\ _H\_S_ & DA1 & 0.903 & 0.880 & 0.903 & 0.838 & 0.947 & 0.964 & 0.793 & 0.744 & 0.941 & 0.810 & 0.872 \\ _H\_A_ & DA1 & 0.913 & 0.880 & 0.900 & 0.809 & 0.951 & 0.967 & 0.792 & 0.717 & 0.945 & 0.799 & 0.867 \\ _PVT_ & DA1 & 0.920 & 0.888 & 0.925 & 0.851 & 0.951 & 0.966 & 0.792 & 0.709 & 0.951 & 0.828 & 0.878 \\ _FH_(2) + 2 \(\times\)_PVT_(2) & DA1/DA2 & **0.927** & **0.894** & **0.932** & **0.868** & 0.954 & **0.971** & 0.797 & **0.767** & **0.955** & **0.853** & **0.893** \\ Ensemble in [24] & DA1 & 0.926 & 0.888 & 0.916 & 0.842 & 0.955 & **0.971** & **0.799** & 0.764 & 0.952 & 0.820 & 0.883 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance (dice = F1-score) in the skin detection problem. Best performance is marked in bold font. \begin{table}
## 4 Results and Discussion
### Use of Tools Supporting the Improvement of Efficiency in the Analyzed Enterprise
#### 4.5.4 Method SMART  
When formulating the optimization goals, the SMART method was used, which enabled them to precisely define and implement the process. An example of a target that was formulated as part of this method is \"shortening the average time of rearranging for the product, only up to 30 min after the workshop\". The goal is clearly defined and leaves no room for doubt as to what exactly is to be achieved. In this case, the specificity of the target is that it is about shortening the average time of rearranging for product A. The goal indicates a specific process (reckoning) and determines which product (A) is to be optimized.  
Measurable--the target is measurable because it determines the specific value to which to strive in the optimization process. In this case, the goal is to achieve an average rearranging time of 30 min. This is a measurable indicator that allows for easily assessing progress and determining whether the goal has been achieved.  
Acceptable (Achievable)--the goal is acceptable because it is based on the real capabilities of the company. It was determined taking into account available resources, technology and team experience. As part of the workshop, it is planned to introduce appropriate  
Figure 13: Chart Ishikawy.  
tools, methods and training that will allow for reducing the time of rearranging. Based on previous process analyses, it was considered that such a goal was realistic to achieve at a specified time.  
Realistic--the goal is realistic because it was determined in the context of the company's previous experience and available funds. Shortening the average time of rearranging for the product by up to 30 min is possible to achieve by optimizing existing processes, for example by improving work organization, modification of conversation procedures or introducing new technologies. The purpose takes into account real restrictions and resources that are available at the plant.  
Time-bound--the target is set out in time because a specific date has been set for implementation after the workshop. By giving time to implement the planned optimization activities and monitoring them, the goal becomes more concentrated and they can precisely determine when it should be achieved. Determining the deadline also allows for assessing its progress.
# Miniaturized Near-Infrared Analyzer for Quantitative Detection of Trace Water in Ethylene Glycol
## 3 Results and Discussion
### Selection of Feature Bands  
Selecting appropriate spectral bands for quantitative analysis is a critical preprocessing step in NIR spectroscopy. Full-spectrum data often contain excessive redundant information, which can compromise model accuracy and stability while increasing processing time and computational demands, thereby hindering rapid online detection. Consequently, wavelength screening is essential to prioritize bands that contain characteristic absorption peaks of the target analyte, ultimately enhancing predictive precision [38,39].  
In this study, the spectral range of 1100-2500 nm was divided into equidistant sub-bands. The number of sub-bands was incrementally increased until further segmentation ceased to enhance feature band selection. Ultimately, the range was partitioned into 12 equidistant sub-bands. Partial Least Squares Regression (PLSR) was applied to each sub-band to develop quantitative calibration models for ethylene glycol water content [40]. Feature bands were identified by comparing the root mean square error of cross-validation (RMSECV) across sub-bands. Those with RMSECV values less than or comparable to the full-spectrum model were deemed optimal.  
As shown in Figure 4, the 1800-1900 nm and 1900-2050 nm bands exhibited the lowest RMSECV values (both below 0.5), indicating a strong responsiveness to water content. To further refine the model, these adjacent bands were merged into a continuous spectral region (1800-2050 nm), which was ultimately designated as the primary feature band for modeling.
# Software Defined Networking for Improved Wireless Sensor Network Management: A Survey
## 5 Management of WSNs Based on SDN
### Topology Management  
WSNs are different from the conventional networks that SDN was initially designed for WSNs have limited resources and use the same channel for node to node communication and data processing (in-network processing). SDN allows for decoupling of control plane and data plane however, for WSNs the control and data packets will still use the already resource constrained and dynamic WSN topology. In management of the network topology, the technique employed should be able to support node mobility which results in topology changes including dealing with unreliable wireless links. These functions should be in tandem with management of QoS which ensures that the network is robust. WSN topology management based on SDN can be divided into management of the following:
# Crowd Monitoring and Localization Using Deep Convolutional Neural Network: A Review
## 4 Crowd Related Research Approaches
### Crowd Monitoring
#### 4.2.1 Crowd Counting  
Counting means specifically count the number of people in the crowd. The crowd counting has been discussed by many authors in the literature. [PERSON] et al. [43] proposes a novel head counting and localization technique, Density Independent and Scale Aware Model (DISAM), that performs well for high density crowed where human head is the only visible part in the images. CNN is first used as head detector and later for computing response matrix from the scale aware head proposals to obtain the probabilities of head in the images. In [56], you only look once (YOLO) is a detection technique which is broadly used for the detection of objects in an image with high level of perspective values i.e., maximum threshold value. [PERSON] et al. [57] recommended CNN and learn to scale that generate multi polar normalized density maps for crowd counting. It extracts a patch-level density map by a process of density estimation and clusters then into multiple levels of density. Each patch density map is normalized via an online learning strategy for the center with multi polar loss. In [58] the crowd density of surveillance videos is measured using CNN and short term memory. Two classical deep convolutional networks namely Googlenet [59] and VGGnet [5] were used for estimating crowd density [60]. Similarly, [4] first approximate crowd size estimation and secondly count the exact number of people in the crowd. The efficiency remains unchanged in the terms of its accuracy of (90%).
## 3 Results
### Validation of AOD Estimation Methods
#### 3.2.2 Wintertime  
We further examined the seasonal sensitivity of each method through an additional case analysis for winter. Figure 7 displays the horizontal distribution of observed and estimated AOD at 01:00 UTC on 2 January 2017 over East Asia and the Korean Peninsula. While the GOCI satellite missed the AOD for most of the East Asia region (Figure 7a), the estimated AODs of all methods showed large values in the missing region (southern China and the central Korean Peninsula; Figure 7b-d). Unlike springtime, the M2 method showed an AOD similar to that of the M1 method. However, the M3 method still showed the lowest AOD. For the 9 km resolution analysis (Figure 7g), reduced AOD estimation by the M3  
Figure 6: Daily aerosol optical depth (AOD) from the Yonsei-YSU station and estimated AOD from the forecast model during March-May 2016. The left and right panels represent the results from the 27 and 9 km resolution model results, respectively. Estimation methods are (**a,b**) M1 with max RH = 93%, (**c,d**) M1 with RH = 96%, (**e,f**) M2, and (**g,h**) M3.  
method was significant on the eastern and the southern Korean Peninsula and for the Seoul Metropolitan area compared with the other methods Figure 7e,f). Although the observable area from the satellite image in Figure 7a is very small, the M3 estimation might be the closest to the satellite comparing the eastern side of the Korean Peninsula.  
The daily variation in AODs was compared at the Yonsei University AERONET station (Figures 8 and 9). All estimated AODs were closer to the AERONET observation in wintertime than in springtime, and the overestimation tendency was not very significant in winter. Interestingly, high-concentration PM2.5 (PM\({}_{2.5}\) > 35 \(\upmu\)g m\({}^{-3}\)) events were more frequent in winter (32 days) than in springtime (17 days) during our analysis period, but wintertime estimations showed good agreement with the overall observations. Although differences between estimation methods still exist, especially in the high-concentration AOD period (Figure 8a), these are reduced with high resolution (Figure 8b). Furthermore, there is no underestimation by the M3 algorithm, in contrast to the springtime for both the D01 and D02. Most statistical scores were similar or better than those in springtime (e.g., enhanced R and RMSE); however, for the M2 method, RMSE and MB increased and were similar to those of the M1 method with maximum RH = 93% (Figure 9).  
Figure 7: (a) Spatial distribution of aerosol optical depth (AOD) obtained from the geostationary ocean color imager (GOCI) satellite at 01:00 UTC on 2 January 2017. Estimated AOD from 27 km resolution model results using (**b**) M1 with maximum relative humidity (RH) = 93%, (**c**) M2, and (**d**) M3. (**e**-**g**) Estimated AODs from the 9 km resolution model using (**e**) M1, (**f**) M2, and (**g**) M3.
## 3 Results
### MTT Assay Results  
DLD1 human colorectal cancer cell line was used to determine the antiproliferative effects of different berry extracts. MTT analysis shows that the viability of DLD1 cells is significantly reduced by applying extracts at different time intervals. The results show that all anthocyanins have time and dose-dependent inhibitory effect on the viability of cancer cells (\(p<0.05\)). Responses of DLD1 cells to increased anthocyanin concentrations were given in Figures 1 and 2. Indications: RSNE: _Sambucus nigra_ ethanol, RSNA: _Sambucus nigra_ acetone; RVME: _Vaccinium myrtillus_ ethanol; RVMA: _Vaccinium myrtillus_ acetone; RACE: _Aronia melanocarppa_ ethanol, RACA: _Aronia melanocarppa_ acetone and RVCE: _Vaccinium corbyosum_ ethanol \begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Compound** & **Rt (min)** & **Molecular Ions** & **Fragment Ions** & **Bilberry Ions** & **Highbust Ions** & **Eldeberry Vaccinium cryptoshum** & **Eldeberry Vaccinium** & **Black Chokeberry Chronic _Aronia melanocarppa_ \\ \hline petunidine-3-arbinoside & 11,685 & 449,109 & 317,066 & \(\bullet\) & \(\bullet\) & - & - \\ \hline pelargonidine-3-sambubio-side & 11,725 & 565,155 & 271,060 & - & - & \(\bullet\) & - \\ \hline malvidine-3-galactoside & 11,973 & 493,131 & 331,079 & \(\bullet\) & \(\bullet\) & - & - \\ \hline cyanidine-3- xyloside & 12,035 & 419,097 & 287,053 & - & - & - & \(\bullet\) \\ \hline peonidine-3-arbinoside & 12,215 & 433,111 & 301,070 & \(\bullet\) & \(\bullet\) & - & - \\ \hline malvidine-3-glucoside & 12,240 & 493,131 & 331,079 & \(\bullet\) & \(\bullet\) & - & - \\ \hline malvidine-3-arbinoside & 12,524 & 463,122 & 331,079 & \(\bullet\) & \(\bullet\) & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._ \begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Compound Assigned** & **Bilberry Vaccinium myrtillus** & **Highbush Blueberry Vaccinium cryptoshum** & **Eldeberry Sambucus nigra** & **Black Chokeberry Chronic _Aronia melanocarppa_** \\ \hline cyanidine-3-glucose & 153.04 \(\pm\) 29.64 & 1.39 \(\pm\) 0.29 & 240.51 \(\pm\) 50.87 & 52.52 \(\pm\) 2.60 \\ \hline cyanidine-3-galactoside & 148.91 \(\pm\) 30.50 & 35.42 \(\pm\) 1.03 & - & 356.83 \(\pm\) 84.76 \\ \hline malvidine-3-galactoside & 43.19 \(\pm\) 8.87 & 65.46 \(\pm\) 11.74 & - & - \\ \hline malvidine-3-glucose & 144.75 \(\pm\) 31.03 & 3.64 \(\pm\) 0.29 & - & - \\ \hline cyanidine-3-glucose & - & - & 46.44 \(\pm\) 2.78 & - \\ \hline cyanidine-3- runtimeide & - & - & 8.96 \(\pm\) 0.58 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Quantitative anthocyanin content of ethanol extracts by LC-MS-IT-TOF analyses [ppm]. (-: absence). Figure 1: The antiproliferative effects of anthocyanins for 24 h application (* \(p\) \(<\) 0.05; ** \(p\) \(<\) 0.001).