esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### Gateway  
An IoT Gateway enables communication between constrained and public networks. A smart device sends sensor information to a cloud IoT service through the Gateway. Similarly, cloud IoT services can communicate with a smart device through the Gateway to receive real-time updates. The Gateway acts as a protocol translator in an IoT network. As such, devices with various networking protocols can communicate with them. For instance, the Gateway allows communications between a Zigbee device and a 6 LoWPAN device. A Zigbee device does not use IPv6 for addressing, while a 6 LoWPAN device does. The Gateway receives a Zigbee packet, translates the packet to an IPv6 packet, and forwards it to the 6 LoWPAN network. Hence, the Gateway enables cross-protocol communications in IoT networks. Moreover, the Gateway translates IPv6 and CoAP protocols to IPv4 and HTTP, respectively, to allow interactions between edge and cloud IoT services.
_Article_  
Variability of the Wind Turbine Power Curve  
[PERSON] \({}^{\ddagger}\)\({}^{\star}\) and [PERSON] \({}^{\ddagger}\)  
\({}^{1}\) Collective Interactions Unit, Okinawa Institute of Science and Technology, Onna, Okinawa 9040495, Japan  
\({}^{2}\) Department of Engineering & Public Policy and Tepper School of Business, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA; [EMAIL_ADDRESS]  
\({}^{\star}\) Correspondence: [EMAIL_ADDRESS]; Tel.: +81-98-966-1546  
Academic Editor: [PERSON]  
Received: 9 July 2016; Accepted: 8 September 2016; Published: 14 September 2016  
Wind turbine power curves are calibrated by turbine manufacturers under requirements stipulated by the International Electrotechnical Commission to provide a functional mapping between the mean wind speed \(\overline{v}\) and the mean turbine power output \(\overline{P}\). Wind plant operators employ these power curves to estimate or forecast wind power generation under given wind conditions. However, it is general knowledge that wide variability exists in these mean calibration values. We first analyse how the standard deviation in wind speed \(\sigma_{v}\) affects the mean \(\overline{P}\) and the standard deviation \(\sigma_{P}\) of wind power. We find that the magnitude of wind power fluctuations scales as the square of the mean wind speed. Using data from three planetary locations, we find that the wind speed standard deviation \(\sigma_{v}\) systematically varies with mean wind speed \(\overline{v}\), and in some instances, follows a scaling of the form \(\sigma_{v}=C\times\overline{v}^{\alpha}\); \(C\) being a constant and \(\alpha\) a fractional power. We show that, when applicable, this scaling form provides a minimal parameter description of the power curve in terms of \(\overline{v}\) alone. Wind data from different locations establishes that (in instances when this scaling exists) the exponent \(\alpha\) varies with location, owing to the influence of local environmental conditions on wind speed variability. Since manufacturer-calibrated power curves cannot account for variability influenced by local conditions, this variability translates to forecast uncertainty in power generation. We close with a proposal for operators to perform post-installation recalibration of their turbine power curves to account for the influence of local environmental factors on wind speed variability in order to reduce the uncertainty of wind power forecasts. Understanding the relationship between wind's speed and its variability is likely to lead to lower costs for the integration of wind power into the electric grid.  
wind power; power curve; variability
# A Comprehensive Review of Indoor Localization Techniques and Applications in Various Sectors
## 3 Methodology and Application of Indoor Localization in Various Fields
### Transforming Industrial Operations with Indoor Localization Technologies
#### 3.2.2 RFID-Based Methodology for Construction Sites
By automating the tracking process, the system enhances real-time decision-making, improves project management, and ensures greater efficiency on construction sites [59].
# A Reduced-Order Model for the Vibration Analysis of Mistuned Blade-Disc-Shaft Assembly
###### Abstract  
An effective reduced-order model is presented in this paper for the vibration analysis of a mistuned blade-disc-shaft assembly considering the flexibility of the shaft and the rotordynamic effects. For the sake of accurate modeling and quantitative analysis, three-dimensional (3D) finite element models were employed in obtaining the governing equations of motion with the Coriolis force, centrifugal stiffening, and spin softening effects taken into account. Then, an efficient model order reduction technique based on the coordinate projection by normal modes of tuned assembly and cyclic symmetry analysis was developed for mistuned blade-disc-shaft assembly. The criterion of whether one matrix could be incorporated in cyclic symmetry analysis is presented. During the modeling, the mistuning in blade and disc was taken into account and dealt with independently. In mistuning projection, the blade and disc parts were both projected onto their tuned counterparts of the sector model, where the boundary conditions were set to be fixed and free, respectively. Finally, an example of a blade-disc-shaft assembly was employed to validate the effectiveness of the presented method in free and forced vibration analysis.  
reduced order model; mistuned blade-disc-shaft assembly; rotordynamic effects Article
## 2 Case Study
### Methodology of Back-Analysis  
It was observed that the performance of the embankment stabilised by PVDs was influenced by several factors, such as ground properties, smear effect, and properties of the sand cushion [24; 25]. In order to back-calculate the PVD behaviour in the field, subsoil properties and analysis methods should be verified first. As previously mentioned, the smear effect permeability ratio (\(\eta\)) has been proposed with a wide range of values. In back-analysis, the effect of the drain will be overestimated when a value lower than the actual value of the subsoil permeability is employed. Thus, one of the embankment construction sections at the case study location was applied in order to calibrate the smear effect parameters in the numerical procedure. Subsequently, the performance data of the numerical methods of drainage of the fixed soil parameters and PVD were compared and evaluated. As previously discussed, the smear effect is an uncertain factor in drainage behaviour. Since these factors are independent of one another, a set of different values of \(\eta\) is listed in Tables 3 and 4. This was initially determined based on the literature. Subsequently, the equivalent horizontal and vertical permeability were calculated for the development of numerical models.
## 7 Results and Analysis
### Comprehensive Security Evaluation
#### 7.9.1 Scoring Methodology  
We calculate CSS using the Analytic Hierarchy Process (AHP)-derived weights from O-RAN Alliance security experts, as shown in Equation (21). The Composite Security Score (CSS) integrates six critical security metrics through weighted aggregation.  
\[\text{CSS}=\sum_{i=1}^{6}w_{i}\cdot M_{i} \tag{21}\]  
where  
* \(M_{i}\): Normalized metric values (0-100 scale).
* \(w_{i}\): AHP-derived weights from domain experts.  
Normalization ensures comparability across metrics:  
\[M_{\text{ASR}} =100\cdot(1-\text{ASR})\] \[M_{\text{CLR}} =\text{CLR}\] \[M_{\text{ADP}} =100\cdot\text{F1-score}\] \[M_{\text{PLR}} =100\cdot(1-\text{PLR})\] \[M_{\text{RT}} =100\cdot\left(1-\frac{\text{RT}}{500}\right)\] \[M_{\text{MLA}} =100\cdot\text{Accuracy}\]  
Table 16 presents the AHP-derived weights and normalization formulas for key security evaluation metrics, including attack success rate, classification loss rate, and adversarial detection precision.
# An Architecture to Manage Incoming Traffic of Inter-Domain Routing Using OpenFlow Networks
## 6 Further Discussion
### Future Works and Limitations  
All the network logic in the SDN architecture resides inside applications executed over SDN controllers. The three strategies for load balancers used in this work followed the reactive flow creation. Hence, for future works, it is interesting to investigate the different approaches for the manipulation of flows in an OpenFlow network, for example, proactive and active strategies [29]. Another idea in this line of thought is to manage and apply a more aware congestion control for the flows, e.g., a switchover mechanism [36] that changes congested flows to different paths during the lifetime of the flows.  
For future works, the execution of different protocols to compose the workload has been considered. One limitation of this work is that it only used UDP flows. The exclusivity of this protocol in the experiments was justified for the sake of space and time, and the metric adopted in this  
Figure 13: The potential number of customers affected by the proposed architecture deployed on the Internet.  
work, the bandwidth of the paths. However, the use of the _iperf_ tool to set up and generate TCP flows will be the next step.  
TCP flows will be designed to be as representative as possible to typical Internet TCP flows. The importance of this investigation will discover the sensitivity of the throughput of TCP flows to dropped packets as realistic Internet traffic tends to be highly dynamic, with individual flows varying in their bandwidth utilization. Thus, with more realistic traffic, it will be possible to analyze whether there is any improvement in the throughput, as was seen in the described experiments using UDP flows.  
Another idea to be explored is the creation of a numerical network model to be executed over BGP topology. A good question to be answered will be how much path diversity on the Internet can be improved with a large adoption of the proposed architecture. Exchange Internet Points (IXPs) and Content Distributed Networks (CDNs) can contribute to increasing the capillarity of the Internet traffic, which is reflected in the BGP topology. The proposed architecture can make it interesting for transit ASes to provide a new type of network services to stub ASes, principally for the management inbound network traffic.
## 4 The Proposed Hybrid Approach for Intrusion Detection
### Data Preprocessing
#### 4.2.1 Feature Mapping  
The features extracted from network traffic include numerical and symbol features, but machines can only identify numerical values. Therefore, each symbol feature needs to be first converted into numerical values. For example, the NSL-KDD [21; 22] dataset contains 3 symbol features and 38 numeric features, and the UNSW-NB15 [25; 26] dataset contains 3 symbol features and 39 numeric features. Symbol features in the NSL-KDD dataset include protocol types (e.g., TCP, UDP, and ICMP), destination network services (e.g., HTTP, SSH, FTP, etc.) and normal or error status of the connection (e.g., OTH, REJ, S0, etc.). In the proposed model, the original training dataset is divided into several subsets using the MDPCA clustering algorithm. To avoid the influence of the discrete attributes on clustering, these symbol features are encoded using a one-hot encoding scheme. For example, the symbol feature \"protocol type\" in the NSL-KDD dataset has three discrete values, namely TCP, UDP, and ICMP, which can be encoded in a space of three dimensions (with one-hot feature vectors \([0,0,1]\), \([0,1,0]\), and \([1,0,0]\)). Since the symbol features \"service types\" and \"flag types\" in the NSL-KDD dataset include 70 and 11 discrete attributes, respectively, they can be transformed to 70 and 11 one-hot feature values, respectively. According to this method, the 41-dimensional original features of the NSL-KDD dataset are finally transformed into 122-dimensional features. Similarly, the 42-dimensional features in the UNSW-NB15 dataset are converted to 196-dimensional features.
## 4 Discussion
### Change in Primary and Secondary Metabolism Caused by Glyphosate Treatment
Using a three-band conceptual model where the third spectral band is used to control backscatter, [PERSON] et al. [22] developed the carotenoid reflectance index (CRI-1) and the modified anthocyanin reflectance index (mARI). Carotenoids were estimated using two reflectance bands in the green spectral range. Anthocyanin retrieval was based on green and red reflectance bands. In both cases, a third band in the NIR range was used for backscatter correction. Reflectance of green vegetation in the short-wave infrared region is dominated by water absorption, which obscures absorption features of biochemical components such as lignin [43]. During vegetation dieback, however, the water content decreases and the absorption bands become visible. [PERSON] et al. [25] proposed the normalized difference lignin index (NDLI) as an indicator of bulk canopy lignin content based on the 1754 nm lignin absorption feature. The status of plant canopy water was addressed using the normalized difference water index (NDWI), which was suggested by [PERSON] [27] with focus on hyperspectral systems. In addition to the abovementioned indices, the normalized difference vegetation index (NDVI) published by [PERSON] et al. [26] was evaluated because of its widespread use in vegetation studies. Unlike all other indices, NDVI aims at assessing structural vegetation parameters such as green biomass, LAI or fractional cover. The setup was originally as an index for broadband satellite applications, a narrowband equivalent was used in this study. As shown in the results section it is possible to distinguish between control plants and glyphosate-treated plants by means of narrowband spectral vegetation indices. Their sensitivity differs with respect to the time of detection. The investigated indices respond in the following order: NDLI (3) \(>\) CRI-1 = PRI (4) \(>\) PSSRa (5) \(>\) IRECI = mARI = NDVI (6) \(>\) NDWI (7), with NDLI differentiating first already 3 days after glyphosate application and NDWI differentiating last with 7 days after application. Also, in Figures 5 and 6 it is striking that at 1754 nm, where lignin absorption takes place a clear difference is visible between the treatments and with time. Besides the NDLI the indices that referred to photosynthetic pigments showed a good relation to glyphosate treatment, while the indices referring to polyphenols (mARI), plant structure (NDVI) and water content (NDWI) seem to be less sensitive to early detect glyphosate application. From the indices related to photosynthetic pigments, the CRI-1 and the PRI showed the highest sensitivity and at the same time the best correlation with the chlorophyll and carotenoid content (Table 5). It is necessary to underline again, that the present trial has to be seen as a preliminary trial, which need to be verified by further studies, especially by field studies as well. Also, with the spectral vegetation indices it is not possible to distinguish between glyphosate-treated plants and drought stress plants at the last sampling when the plants were almost dead.
## 4 Results and Discussion
### Discharge Efficiency of Horizontal Drains  
Figure 17 presents the discharge rate (or discharge capacity) of the two drainage wells in the typhoon season. The subscripts _w1_, _w2_ and _d1-d4_ represent the _Drainage Well-1_, _Drainage Well-2_, and 1 st -4 th level of horizontal drains. In the typhoon season, the average initial groundwater level prior to the subsurface drainage approximates 11.0 m below ground surface for _Drainage Well-1_ and 5.0 m below ground surface for _Drainage Well-2_. For _Drainage Well-1_, 1 st and 2 nd level horizontal drains are placed at 5.0 m and 10.0 m below the ground surface which are above the average initial groundwater level (11.0 m below ground surface). Under such circumstances, the discharge rate of 1 st and 2 nd level horizontal drains (_Qw1d1_ and _Qw1d2_) becomes zero and ineffective for groundwater drawdown as shown in Figure 17a. Concurrently, the _Drainage Well-2_ (_Qw2d1_ and _Qw2d2_) possesses a similar situation to the _Drainage Well-1_ as shown in Figure 17b.  
On the contrary, 4 th level horizontal drains for both drainage wells (_Qw1d4_=163.104 m3/day and _Qw2d4_ =236.664 m3/day) exhibit the highest discharge rate. According to the parametric study and field monitoring results, [PERSON] et al. also indicated that the horizontal drain is most effective and beneficial for the subsurface drainage when it is located at the bottom zone of the slope [8; 9]. However, the discharge rate of 3 rd and 4 th level horizontal drains decrease gradually with elapsed time due to the groundwater drawdown. The total discharge rate of _Drainage Well-2_ (_Qw2_) is higher than that of _Drainage Well-1_ (_Qw1_) because the horizontal drains of _Drainage Well-2_ are all located below the groundwater level (refer to \(t\) = 0 day in Figure 13a). Table 7 summarizes discharge rates of horizontal drains at different elevations of the well shaft and the total discharge rate of drainage wells. In You-Ye-Lin landslide, the discharge rate of a single shallow horizontal drain varies in a range of 54.60-0.18 m3/day in dry season.  
Figure 16: Potential sliding area and potential sliding surface along _A-A’_ profile in typhoon season (**a-1**,**a-2**) without (**b-1**,**b-2**) with subsurface drainage.
## Appendix A  
Proof that the Mean-Square Error across a dataset \(D\) has a lower bound value of \(\text{Var}(Y)\):  
Let \(Y\) be the true output and \(\hat{Y}\) the predicted output of the network, then the expected value \(\mathbb{E}_{D}\) of the mean-squared error over the entire dataset \(D\) is \(\mathbb{E}_{D}\Big{[}(Y-\hat{Y})^{2}\Big{]}\).  
With \(\mu\) denoting the mean of \(Y\), we can express \(\mathbb{E}_{D}\Big{[}(Y-\hat{Y})^{2}\Big{]}\) as:  
\[\mathbb{E}_{D}\Big{[}(Y-\hat{Y})^{2}\Big{]} =\mathbb{E}_{D}\Big{[}\big{(}Y-\mu+\mu-\hat{Y}\big{)}^{2}\Big{]} \tag{12}\] \[=\mathbb{E}_{D}\Big{[}\big{(}(Y-\mu)+\big{(}\mu-\hat{Y}\big{)}^{2 }\Big{]}\] \[=\mathbb{E}_{D}\Big{[}\Big{(}(Y-\mu)^{2}+\big{(}\mu-\hat{Y}\big{)} ^{2}+2(Y-\mu)\big{(}\mu-\hat{Y}\big{)}\Big{)}\Big{]}\]  
Since expectation of the sum is the sum of expectations, and constants can be pulled out of expectations, the expression can be written as follows:  
\[\mathbb{E}_{D}\Big{[}\Big{(}(Y-\mu)^{2}+ \big{(}\mu-\hat{Y}\big{)}^{2}+2(Y-\mu)\big{(}\mu-\hat{Y}\big{)} \Big{)} \tag{13}\] \[=\mathbb{E}_{D}\Big{[}(Y-\mu)^{2}\Big{]}+\mathbb{E}_{D}\Big{[} \big{(}\mu-\hat{Y}\big{)}^{2}\Big{]}+2\big{(}\mu-\hat{Y}\big{)}\ \mathbb{E}_{D}[(Y-\mu)]\]  
Given the definition of variance and that the expectation of a constant equals the constant, the expression is written as follows:  
\[\mathbb{E}_{D}\Big{[}(Y-\mu)^{2}\Big{]} +\mathbb{E}_{D}\Big{[}\big{(}\mu-\hat{Y}\big{)}^{2}\Big{]}+2\big{(} \mu-\hat{Y}\big{)}\ \mathbb{E}_{D}[(Y-\mu)] \tag{14}\] \[=Var(Y)+\big{(}\mu-\hat{Y}\big{)}^{2}+2\big{(}\mu-\hat{Y}\big{)} \mathbb{E}_{D}[(Y-\mu)]\]  
The term \(\mathbb{E}_{D}[(Y-\mu)]\) can be evaluated as follows:  
\[\mathbb{E}_{D}[(Y-\mu)]=\mathbb{E}_{D}[Y]-\mathbb{E}_{D}[\mu]=\mu-\mu=0 \tag{15}\]  
which gives,  
\[Var(Y)+\big{(}\mu-\hat{Y}\big{)}^{2}+2\big{(}\mu-\hat{Y}\big{)} \mathbb{E}_{D}[(Y-\mu)] =Var(Y)+\big{(}\mu-\hat{Y}\big{)}^{2}+2\big{(}\mu-\hat{Y}\big{)}\times 0 \tag{16}\] \[=Var(Y)+\mu-\hat{Y}\]  
Thus,  
\[\mathbb{E}_{D}\Big{[}(Y-\hat{Y})^{2}\Big{]}\geq Var(Y) \tag{17}\]  
with equality holding when \(\hat{Y}=\mu\).
# Automatic Classification of Fatty Liver Disease Based on Supervised Learning and Genetic Algorithm
## 3 Materials and Methods
### Methodology  
The main objectives of the present CAD system were 1- To analyze the consequence of a dynamic size of ROI extracted from each ultrasound liver image; 2-To categorize the USA liver images as fatty or normal without the segmentation stage, a voting-based classifier was applied. In this research work, multiple ROIs (nine ROIs) were selected within the liver tissue. This was also automatically determined by the Genetic Algorithm (GA). GA is an adaptive investigative search algorithm [41]. In GA, firstly, an initial population was created. The population consisted of 100 chromosomes. Separately, a chromosome was composed of concatenated thresholds or candidate solutions. The chromosome construction is shown in Figure 2. Secondly, each pair of parent chromosomes was carefully chosen, and then the crossover process was performed at the random crossover point. Thirdly, the mutation operation was executed on every chromosome at a random point. Then, the offspring was generated. Finally, an optimization using an objective function was performed for offspring and the parent chromosomes.  
The objective function was used to exploit the accurately classified US images or to minimize the number of wrongly classified images. The fitness function was reciprocal of the Penalty Value (PV), or precisely the number of incorrectly categorized images. Ultimately, the fitness function was the number of correct categorized images.  
The position was initially determined by three experienced physicians. As a consequence of labeling ROIs, the fixed positions were then used for newly acquired images. To make the process faster, only ROIs in the selected images were converted from RGB to a grey level [46], according to the following equation:  
\[I=0.2989*\ R+0.5870\ *\ G+0.1140\ *\ B \tag{1}\]  
Figure 1: An acquired image from an ultrasound scan machine (**a**) normal liver and (**b**) fatty liver.  
Figure 2: The chromosome structure, note: F1\(\rightarrow\)Feature1, etc.  
where \(I\) is a transformed image; then, the 26 features of each ROI were extracted, as in Figure 3. Finally, the voting-based classifier was used to categorize the kind of liver tissue normal/fatty [46]. In a Voting-based classifier, the key concept was to perform multi-independent Extreme Learning Machine (ELM) training in place of one single ELM training, and then a final decision was taken depending on the majority voting method [47].