esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### First Order and Total Sensitivity Coefficients
Similar to the first-order effects, the total sensitivity coefficients are shown in Figure 5b. The magnitude of the sensitivity values is generally larger than the first-order sensitivity coefficients. This is reasonable, since the total sensitivity coefficients account for both the first-order effect, as well as the higher-order (interaction) effects. Here, the bar width also has the largest effect on most parameters; these are the flow resistivity, the tortuosity and the viscous characteristic length. Large effects are also observed for the bar spacing. This is similar to the findings for the first-order sensitivity coefficients. The lowest effects (on average) are shown for the plane angle. In summary, an analysis of the first-order and total sensitivity coefficients offers an important insight into the effects of the microscale geometry on the model parameters. Moreover, the relations found seem reasonable. However, these findings only show the effects of single parameters. Interdependencies, if apparent, cannot be found, or are only implicitly contained in the total effects. For the case shown here, rather large differences between first-order and total sensitivity coefficients are apparent, indicating that strong interaction effects influence the behavior. Therefore, an analysis of the second-order effects can help us to investigate the behavior further. This is shown in the next section.
## 3 Application to the Quantum Two-Element Antenna Array  
The purpose of this section is to present an application of the super-operator concept to a quantum two-element array of the dipole emitters shown in Figure 2. This type of antenna is widely used in classical radio engineering and its application in super-resolving quantum radars was proposed in [15].
## 3 Optimization of Controllable Rectification Based on Advanced Control Strategies
### PWMC Rectification Control Strategy  
In the free-piston internal combustion linear power generation system, the free piston's motion curve is sine-like. Consequently, the mover's velocity in the linear motor and the resulting three-phase voltage and current can be approximated as sine waves. This allows the electric power output from the linear motor to be processed using PWMC, which also regulates the electromagnetic force in the linear motor, enhancing the system's stability and reliability.  
PWMC rectification strategies are categorized into three-phase voltage-source rectification (VSR) and current-source rectification (CSR) based on the main circuit type. Compared to CSR, VSR is more straightforward and offers highly controllable output voltage and frequency, making it more adaptable to the linear generator's varying amplitude and frequency characteristics. Hence, this study adopts the three-phase VSR strategy for motor power rectification.  
The dual-loop control block diagram of the system is shown in Figure 10. The rectification system adopts a dual-loop control structure, which is formed by a voltage outer loop and a current inner loop based on PI control. The voltage outer loop is used to regulate the DC bus capacitance voltage to stabilize it at the target value. The output of the voltage outer loop control compensator serves as the reference value for the current inner loop. By adjusting the machine-side current, the current inner loop ensures that the voltage and current phases are the same and that the machine-side current stably follows the reference current. This enables the rectifier to operate stably.  
Figure 9: Rectifier bridge topology.
## 2 Spectral Clustering Algorithm  
The k-means machine learning algorithm is widely implemented to cluster time-varying information in optical communications. However, this is hindered when the obtained constellations present nonlinear phase noise due to elongated clusters, confusing the algorithm based on the Euclidean distance. The spectral clustering algorithm proposed in [20] is an alternative to the classical k-means for elongated cluster shapes.  
The spectral clustering algorithm selects the number of eigenvectors of a matrix according to the number of groups required to partition the original data set. Subsequently, k-means clustering is applied to the normalized eigenvectors, thereby achieving data grouping. The description above provides a brief overview of the algorithm, but a formal description will be provided, and a visual example will illustrate the behavior of the data when applying this algorithm.
## 3 Stationary Localization Algorithms
### Distributed Localization Algorithms
#### 3.2.3 Reactive Localization Algorithm (RLA)  
In [23], Reactive Localization Algorithm (RLA) is proposed. Instead of localizing every single node in the network, RLA localizes a node that detects an event. Once a sensor node detects an event, RLA which consists of two steps starts. The first step is to find anchor nodes. The sensor node first broadcasts a hello message with its ID and energy level to its neighbors. By the K-Node Coverage Algorithm, at least 4 non-coplanar anchor nodes are found. The second step is reactive localization of the sensor node. Once the selected anchor nodes receive localization request message, they reply with their location information. The sensor node hence localizes itself by quadrilateral. Due to additional process for anchor nodes' localization, energy consumption and communication overhead of RLA are high. Furthermore, accumulated localization error exists.
# Estimating the Spectral Response of Eight-Band MSFA One-Shot Cameras Using Deep Learning
## 3 Materials and Methods
### Deep-Learning Algorithms
#### 3.2.1 Very Deep Super-Resolution (VDSR)  
VDSR is a sophisticated deep-learning technique designed to improve image resolution. It uses a convolutional neural network (CNN) architecture of 20 layers, significantly more profound than traditional networks. This depth allows VDSR to effectively capture complex features and high-frequency details in images [20].  
The architecture includes the following steps:  
* **Input processing:** An 8-band multispectral image is fed to the first convolutional layer, which uses 64 filters of size 3 \(\times\) 3 and applies a'same' padding to maintain the dimensions of the input image. A function of ReLU activation is applied to introduce nonlinearity.
* **Hidden layers:** The image output from the first layer will be passed through 19 convolutional layers, followed by a final convolutional layer, which uses several filters equal to the number of output channels and applies'same' padding. (20 th layer). Each convolutional layer uses 64 filters of size 3 \(\times\) 3 to extract features from the image.
* **Residual learning:** Another convolution is applied to the input image to bring it into the desired output shape. Residual addition combines the final layer's outputs and the input image's convolution. This will help the model learn the residual details needed to improve the image resolution. The equation resulting from this model is the following:  
* **I** is the input image.
* **K1, K2,..., Kn** is the convolution filters.
* Let **R** be the result of the last convolution layer.
* Let **O** be the output image. \[X_{i}=ReLU(K_{i}*X_{i-1})\ with\ X_{0}=I\] (1) \[R=K_{final}*X_{18}\] (2) \[F=K_{residual}*I\] (3) \[O=R+F\] (4) Algorithm 1 presents the pseudocode of the VDSR algorithm.
## 1 Introduction
Many studies have demonstrated its advantages, such as reducing the construction period owing to the conversion of fieldwork into pre-factory work and productivity improvement [19; 20]. Furthermore, it improves safety remarkably, as it can be used to manage quality improvement and flammable work in the factory [21]. However, despite its various advantages, the application of PMR increases cost. The prefabrication technology has been reported to have a limited application and needs technical improvement and government support for economic application [22; 23]. Detailed discussions on various design and construction techniques for the economic application of PMRs are required. One of the most widely studied economic factors driving the use of PMRs is the economic design of the structural frames [10]. This study sheds light on the economic structural framing of PMRs, which has recently become the most actively discussed multi-trade prefabrication technique. For PMR application, integrated structural framing for combining various members is required. Because of costs incurred in addition to the expenses for the existing MEP elements, the contribution of MEP corridor racks to economic growth may become significantly lesser than that of the conventional method. Studies on detailed design methods of PMRs are insufficient to date. This study proposed five methods for designing PMRs and suggested ways to measure their economic feasibility by comparing three aspects: design cost, material cost, and manufacturing cost. In addition, a structural framing technique for the economic application of PMRs was proposed. This article is organized as follows. First, we investigate the existing literature on multi-trade prefabrication and analyze the ways of improving the application of PMRs. Second, the research method is presented, and the case study details are provided. Third, five types of PMR structural frames are proposed and described. We also present the results of the efficiency analysis experiments for these five types of frames. Finally, we discuss the type of structural framing that can be economical.
# MUXER--A New Equipment for Energy Saving in Ethernet
###### Abstract  
Nowadays, the rapidly increasing energy consumption of communication equipment is an economic and environmental problem that needs to be addressed. Small Local Area Networks (LAN) switches in the US alone consume about 8 TWh per year that corresponds to hundreds of millions of US dollars in electricity. A main reason is communication equipment, due to the fact that, Ethernet switches have to active all days even in low-traffic time. There are many approaches to make the switches sensitive with the different traffic load environment, but they have to put some changes to current Ethernet that cause the incompatibility issues. This paper proposes a new device called Modified Multiplexer (MUXER), which can control the power functions of switches without modifying the current Ethernet protocol. The MUXER devices are put before the switches in the network, which provide some functions to transfer incoming Media Access Control (MAC) data frames to the corresponding switches managed by MUXERs. By using the proposed device, the wasted energies in the network can be reduced, especially in the specified low-traffic time. We describe the result of numerical analysis and compare detail power consumption data between a normal and a network where MUXERs are installed. In this work, the explained result shows that if a network administrator can estimate the number of MUXER needed for a particular network and install accordingly, our device can provides a significant energy saving.  
M 2017 5, 74; doi:10.3390/technologies5040074
## 5 Data Processing  
### Topographic Data Processing  
The measurements of ground points were carried out with a Leica total station. Seven stations in total were employed in \"free\" station mode and the measurements were performed over two days. A Leica Viva GNSS receiver was installed on the south end of the dike to give absolute georeferencing information. Its position was precisely determined by processing a \(\sim\)26 h static observation session over two days with the service provided by the IGN. The compensation of the topographic measurements was carried out with Comp3D, a geodesic micro-compensation software developed at IGN.
## 4 Results and Discussion
### Feature Scoring
#### 4.5.3 Confusion Matrix and Error Analysis  
Table 7 elaborates on the performance of the ML models used in this study. These confusion matrices provide a comprehensive breakdown of how each utilised model performed in classifying instances correctly and incorrectly.  
The error analysis focused on analysing misclassifications and identifying common patterns in malware classification. LR and SVM showed 3 false positives and 13 false negatives. They appeared to struggle more with the negative class, resulting in lower precision for the positive class. Nevertheless, both maintained a relatively high recall for the positive class, indicating their ability to capture true malware instances. NB showed four false positives and eight false negatives, indicating higher precision but lower recall for the positive class, as it tended to classify fewer instances as positive when they were actually positive. However, RF and XGB demonstrated strong performance. RF had only one false positive and seven false negatives, whereas XGB, with only two false positives and seven false negatives, displayed good precision and recall for both classes, indicating a balanced performance. Both models excelled in minimising false positives while effectively capturing true positives. KNN, on the other hand, had 4 false positives and 23 false negatives, which demonstrates reasonable precision and lower recall for both classes.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**ML Models** & **TP** & **FP** & **FN** & **TN** & **ATPR** & **AFPR** \\ \hline LR & 89 & 3 & 13 & 100 & 0.8850 & 0.0326 \\ SVM & 98 & 3 & 13 & 100 & 0.8850 & 0.0326 \\ NB & 88 & 4 & 11 & 102 & 0.9027 & 0.0435 \\ RF & 96 & 1 & 7 & 101 & 0.93 & 0.0098 \\ XGB & 92 & 2 & 7 & 101 & 0.9352 & 0.0213 \\ KNN & 93 & 4 & 23 & 85 & 0.7870 & 0.0417 \\ \hline \hline \end{tabular}  
* TP = True positive, FP = false positive, FN = false negative, TN = true negative, ATPR = average true-positive rate, AFPR = average false-positive rate.  
\end{table}
Table 7: ML models and performance evaluation metrics.  
Figure 3: Receiver operating characteristic (ROC) curves for ML models.  
KNN had a slightly higher false-negative rate, implying that it sometimes misses true malware instances. Among these models, XGB and RF demonstrated the highest overall performance. They achieved remarkable accuracy, precision, and recall for both malware and goodware samples, outperforming other models.
## Real-Time QoS Routing Protocols in Wireless Multimedia Sensor Networks: Study and Analysis  
**[PERSON] * and [PERSON]**  
Computer Science and Engineering Department, University of Bridgeport, Bridgeport, CT 06604, USA; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +1-816-703-9142; Fax: +1-203-576-4766.  
Academic Editors: [PERSON] and [PERSON]  
_Received: 17 June 2015 / Accepted: 31 August 2015 / Published: 2 September 2015_  
**Abstract:** Many routing protocols have been proposed for wireless sensor networks. These routing protocols are almost always based on energy efficiency. However, recent advances in complementary metal-oxide semiconductor (CMOS) cameras and small microphones have led to the development of Wireless Multimedia Sensor Networks (WMSN) as a class of wireless sensor networks which pose additional challenges. The transmission of imaging and video data needs routing protocols with both energy efficiency and Quality of Service (QoS) characteristics in order to guarantee the efficient use of the sensor nodes and effective access to the collected data. Also, with integration of real time applications in Wireless Senor Networks (WSNs), the use of QoS routing protocols is not only becoming a significant topic, but is also gaining the attention of researchers. In designing an efficient QoS routing protocol, the reliability and guarantee of end-to-end delay are critical events while conserving energy. Thus, considerable research has been focused on designing energy efficient and robust QoS routing protocols. In this paper, we present a state of the art research work based on real-time QoS routing protocols for WMSNs that have already been proposed. This paper categorizes the real-time QoS routing protocols into probabilistic and deterministic protocols. In addition, both categories are classified into soft and hard real time protocols by highlighting the QoS issues including the limitations and features of each protocol. Furthermore, we have compared the performance of mobility-aware query based real-time QoS routing protocols from each category using Network Simulator-2 (NS2). This paper also focuses on the design challenges and future research directions as well as highlights the characteristics of each QoS routing protocol.  
keywords:wireless multimedia sensor network (WMSN); quality of service (QoS); routing protocols; energy efficiency; reliability; mobility; real-time +
Footnote †: journal: Energy Communications
## 2 Experimental Section
### Method Validation
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Detected THMs**} & **GC** & \multirow{2}{*}{**DI- HS- TWA-Vial**} & **HS-TWA Vial** & **HS-TWA on-Site** \\  & **Retention** & & **Sampling\({}^{\text{a}}\)** & & **Sampling\({}^{\text{b}}\)** & & **Sampling\({}^{\text{c}}\)** \\ \cline{3-6}  & **Times (min)** & **Trout** & **3 Mile** & **Trout** & **3 Mile** & **Trout** & **3 Mile** \\  & & **Pond** & **Pond** & **Pond** & **Pond** & **Pond** \\ \hline Chloroform & 1.88 & 52 & 48 & 60 & 55 & 58 & 52 \\ Bromoform & 6.01 & 26 & 24 & 37 & 33 & 37 & 29 \\ Bromodichloromethane & 2.92 & 19 & 16 & 25 & 19 & 26 & 20 \\ Dbromochloromethane & 4.59 & n.d. & n.d. & n.d. & n.d. & n.d. \\
1,2-dibromoethane & 6.26 & 14 & 10 & 25 & 17 & 25 & 21 \\
1,2-dichloroethane & 2.21 & n.d. & n.d. & n.d. & n.d. & n.d. \\ \hline \multicolumn{6}{c}{**Water Parameters**} \\ \hline Total THM std. Detected & 111 & 98 & 147 & 120 & 146 & 122 \\ Temp (\({}^{\text{c}}\)C) \(\pm\) 0.5 \({}^{\text{c}}\)C & 25 & 25 & 25 & 9, 10\({}^{\text{d}}\) & 12, 12\({}^{\text{d}}\) \\ pH \(\pm\) 0.1 & 6.4 & 6.7 & 6.4 & 6.7 & 6.4 & 6.7 \\ Conductivity (\(\mu\)S) \(\pm\) 0.5 & \(-\) e & \(-\) e & 62 & 66 & 62 & 66 \\ Dissolved solids (mg. L\({}^{-1}\)) \(\pm\) 1 & \(-\) e & \(-\) e & 29 & 30 & 29 & 30 \\ \hline \hline \end{tabular} \(N\) = 3. Results based upon calibration curves constructed by the appropriate SPME method for the extraction of THMS standards from sealed vials and followed by GC/MS analysis. For HS-TWA methods, the fiber was detracted 20 mm into the fiber casing and sampling was undertaken for two days (optimum conditions).  
\({}^{\text{a}}\) DI-, HS- and TWA-sampling methods with salt added under optimized conditions (\(<\)2% differences detected between these methods); \({}^{\text{b}}\) HS-TWA from sealed vial with no salt added but otherwise under optimized conditions; \({}^{\text{c}}\) HS-TWA sampling was positioned at the same depth (1/3 m below water surface) where water samples were collected for spot sampling. \({}^{\text{d}}\) Temperature data taken from City of Corner Brook, NL in 2013; \({}^{\text{e}}\) Sample was adjusted with 10% wt./vol. of (NH4)2 SO4 salt with additional ascorbic acid for pH adjustment to the vial before extraction, hence not determined. n.d. = not detected. Note: Newfoundland and Labrador, Canada government total THM ppb data (by LLE-UV/VIS) was 158.15 ppb and 117.07 ppb for Trout Pond and 3 Mile Pond, respectively, in Winter 2013. [4].  
\end{table} 
Table 1: Analysis of Trout and 3 Mile Ponds by Direct (DI)-, headspace (HS)-, time weighted averaging (TWA)- Vial and Headspace- time weighted averaging (HS-TWA) On-site solid phase microextraction-gas chromatography/ mass spectrometry (SPME-GC/MS) Sampling of THM Standards.
## 4 Model and Calculation Principle
### Penman-Monteith Model  
[PERSON]'s model formula is:  
\[\mathrm{\lambda E_{g}}=\frac{\mathrm{\epsilon}\cdot\mathrm{R_{n}}+\mathrm{ \rho_{a}}(\mathrm{C_{p}}/\mathrm{r})\mathrm{D}\mathrm{G_{a}}}{\mathrm{\epsilon }+1+\mathrm{G_{a}}/\mathrm{G_{c}}} \tag{13}\]  
where \(\mathrm{\epsilon}=\mathrm{s}/\mathrm{r}\); \(\mathrm{s}\) is the slope of the function of water vapor pressure deficit and temperature (kPa\(\cdot\)K\({}^{-1}\)); \(\mathrm{r}\) is the dry and wet table constant (kPa\(\cdot\)K\({}^{-1}\)); Rn is the net radiation absorbed by the canopy (MJm\({}^{-2}\)h\({}^{-1}\)); \(\mathrm{\rho_{a}}\) is air density (kg\(\cdot\)m\({}^{-3}\)); \(\mathrm{C_{p}}\) is the specific heat of dry air at constant pressure (Jkg\({}^{-1}\)k\({}^{-1}\)); D is water vapor pressure deficit (kPa); G\({}_{\rm a}\) is aerodynamic conductance (ms\({}^{-1}\)); G\({}_{\rm c}\) is canopy conductance (ms\({}^{-1}\)); \(\lambda\) is the latent heat of vaporization (2.45 MJ\(\cdot\)kg\({}^{-1}\)), and E\({}_{\rm g}\) is canopy transpiration (kg\(\cdot\)m\({}^{-2}\).s\({}^{-1}\)).  
The final model of canopy stomatal conductance was deduced as [46]:  
\[\rm g_{s}=\frac{E_{g}}{18 LAI_{e}}\rho_{w}g_{v}\frac{T+273}{\rho_{a}VPD} \tag{14}\]  
where E\({}_{g}\) is the canopy transpiration rate (kg\(\cdot\)m\({}^{-2}\).s\({}^{-1}\)); g\({}_{v}\) is the gas constant of water (0.462 kPa\(\cdot\)m\({}^{3}\).kg\({}^{-1}\).k\({}^{-1}\)) [46]; \(\rho_{w}\) is the water density (998 kg/m\({}^{3}\)); \(\rho_{a}\) is the air density (1.29 kg/m\({}^{3}\)), and VPD is the saturated vapor pressure difference (kPa). The unit of stomatal conductance is mmol\(\cdot\)m\({}^{-2}\).s\({}^{-1}\).
# Accelerating Image Classification using Feature Map Similarity in Convolutional Neural Networks
## 4 Experimental Results and Analysis
### Result Based on Cache Location
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Models**} & \multicolumn{10}{c}{Search Time (\(ms\))} \\ \hline \multirow{3}{*}{AlexNet} & 22.8 & 49.9 & 63.9 & 63.9 & 43.2 & \multirow{3}{*}{\begin{tabular}{c} \\ **35.5** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **81.8** \\ \end{tabular} } & **15.0** & **115.0** & **79.8** & \multirow{3}{*}{\begin{tabular}{c} \\ **8** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} \\ **85.4** \\ \end{tabular} } & 87.5 & 137.0 & 135.0 & 167.0 & \multirow{3}{*}{\
\begin{tabular}{c} \\ **245.0** \\ \end{tabular} } \\  & 27.8 & **65.3** & **82.4** & **153.0** & **152.0** & **152.0** & **152.0** & **157.0** & **245.0** & **245.0** & **304.0** \\ \cline{2-13}  & VGG-16 & 62.6 & 62.3 & 48.3 & 48.3 & 60.1 & 59.5 & 59.9 & 97.9 & 97.4 & 97.9 & 83.4 & 83.4 & 83.1 & \multirow{3}{*}{
\begin{tabular}{c} \\ **153.0** \\ \end{tabular} } \\  & **52.1** & **51.5** & **53.0** & **52.9** & **87.3** & **86.9** & **87.0** & **164.0** & **165.0** & **164.0** & **153.0** & **153.0** & **153.0** & **153.0** & \\ \cline{2-13}  & VGG-19 & 62.4 & 62.5 & 48.7 & 48.8 & 60.9 & 60.8 & 60.8 & 60.8 & 99.9 & 99.7 & 99.9 & 99.5 & 85.9 & 85.4 & 85.7 & 85.5 \\  & **52.0** & **53.6** & **52.8** & **53.0** & **86.4** & **88.3** & **86.3** & **86.4** & **163.0** & **166.0** & **162.0** & **166.0** & **153.0** & **153.0** & **153.0** & **153.0** & **153.0** & \\ \cline{2-13}  & ResNet-50 & 24.9 & 59.8 & 59.6 & 59.9 & 97.4 & 97.9 & 97.0 & 166.0 & 164.0 & 164.0 & 164.0 & 164.0 & 164.0 & 164.0 & 164.0 & 23.0 & 33.0 & 322.0 \\ \cline{2-13}  & **27.9** & **86.2** & **86.1** & **86.7** & **164.0** & **171.0** & **161.0** & **162.0** & **302.0** & **304.0** & **302.0** & **300.0** & **302.0** & **300.0** & **596.0** & **598.0** & **598.0** \\ \cline{2-13}  & 25.0 & 60.2 & 60.1 & 60.0 & 98.3 & 98.8 & 98.4 & 97.9 & 166.0 & 166.0 & 166.0 & 166.0 & 166.0 & 166.0 & 166.0 & 166.0 & 166.0 & 165.0 \\ \cline{2-13}  & **27.7** & **85.9** & **85.8** & **86.0** & **160.0** & **160.0** & **160.0** & **300.0** & **299.0** & **299.0** & **299.0** & **299.0** & **299.0** & **299.0** & **299.0** & **301.0** & **299.0** \\ \cline{2-13}  & 166.0 & 166.0 & 166.0 & 166.0 & 166.0 & 166.0 & 166.0 & 166.0 & 166.0 & 166.0 & 166.0 & 166.0 & 166.0 & 325.0 & 326.0 & 326.0 \\ \cline{2-13}  & **29.0** & **298.0** & **297.0** & **298.0** & **297.0** & **297.0** & **297.0** & **297.0** & **297.0** & **299.0** & **298.0** & **299.0** & **297.0** & **297.0** & **590.0** & **597.0** & **597.0** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Cache search time according to layer location measured by ODROID and LattePanda. Bold indicates LattePanda.