esa-sceva/satcom-chunk-collection huggingface dataset:
# Classification Method of Plug Seedlings Based on Transfer Learning
## 2 Materials and Methods
### Classification Method of Plug Seedlings
#### 2.4.2 Transfer Learning  
Transfer learning refers to the automatic extraction of features from new data sets using pre-trained models. This method is a convenient way to apply deep learning without the need for large data sets and time-consuming calculations and training. In some cases where the sample size is small, transfer learning is an effective machine learning method [25; 26; 27]. Pre-trained convolutional neural network models are used. These models have been trained in 1000 classes and 1.2 million samples of ImageNet datasets, and have strong feature extraction capabilities.  
This study is based on four pre-trained convolutional neural network models (Alexnet, Inception-v3, Resnet-18, VGG16), and the last three layers of the networks' structure are modified to make them suitable for the intelligent plug seedlings classification applications. Taking the Alexnet network model as an example, the last three layers of the Alexnet convolutional neural network are discarded, and then the bottleneck layer is taken as the feature extraction result of the new model. Then add a fully connected layer, a softmax layer and a classified output layer to form a new classification model, as shown in Figure 6b.  
* A fully connected layer has been added to map multidimensional features to flat features. The fully connected layer in the convolutional neural network is the same as the role in the shallow neural network and is responsible for logical inference. All parameters in this layer need to be learned. This fully connected layer is used to link the output of the convolutional layer and remove the spatial information (the number of channels) which is a process of turning a three-dimensional matrix into a vector.
* A softmax layer has been added to calculate the probability that each sample belongs to each class. The softmax function was defined as follows. \[\text{y}(z)_{j}=\frac{e^{z_{j}}}{\sum\limits_{k=1}^{K}e^{z_{k}}}\] (3) where \(0\leq\text{y}(z)_{j}\leq 1\), \(\sum\limits_{j=1}^{K}\text{y}(z)_{j}=1\), \(K=3\).
* A classification layer is added to calculate the final classification probability of the sample and calculate the cross entropy [28]. Cross entropy loss function was defined as follows. \[\text{loss}=-\sum\limits_{i=1}^{N}\sum\limits_{j=1}^{K}t_{ij}Iny_{ij}\] (4) where N is the number of the samples, \(K\) is the number of classes, \(t_{ij}\) is the indicator that the \(i\)th sample belongs to the \(j\)th class, and \(\text{y}_{ij}\) is the output for sample \(i\) for class \(j\), which in this case, is the value from the softmax function. This is to say, it is the probability that the network associates the \(i\)th input with class \(j\).
# Measurement and Modeling of 3D Solar Irradiance for Vehicle-Integrated Photovoltaic
## 2 Model
### Measurement Example of the Solar Irradiance on the Car Roof and Car Side  
The city of Miyazaki, Japan, is one of the capitals of local government in Japan (Miyazaki prefecture) with a population of approximately 200,000. Thus, this area contains an urban zone, a residential zone, and a rural zone. In a way, it may be a representative zone in the solar environment (shading frequency and shading height). We did not constrain the driving course of the one-year monitoring of the solar irradiance on the car, but the most frequent driving course of this car is shown in Figure 13 since it was used for commuting between the University of Miyazaki and the driver's residence.  
The typical monitored result in the route in Figure 13 is shown in Figure 14 (clear sky day) with comparison to the global horizontal irradiance (GHI) of fixed pyranometers (mounted on a roof of one of the buildings of the University of Miyazaki).  
Figure 12: Measurement system on the car using five pyranometers on the orthogonal axis.  
Figure 13: Most frequent driving course of the dynamic solar irradiance measurement in the car.  
In the region of the open-air section, the solar irradiance on the car roof is almost identical to the GHI by the fixed pyranometers. However, that of other areas had frequent dips in the irradiance on the car. The timestamp and position data taken by GPS confirmed that these are because of the shading effects by buildings and mountains. The car roof irradiance often exceeded GHI. It is also confirmed because of the reflectance of the buildings.
# Study of Urban Unmanned Aerial Vehicle Separation in Free Flight Based on Track Prediction
## 4 Case Application
### Calculation of Track Prediction Error
#### 4.1.2 Clustering and Classification  
Using the K-means classification model, UAV track data are clustered and classified based on certain parameters, such as mission attributes, model types, altitude, latitude, and longitude. The overall clustering results are shown in Figure 5.  
To determine the optimal number of clusters, the silhouette coefficient is introduced to select the best cluster quantity. Through clustering of the data and programming implementation, the optimal number of clusters is determined to be six, as shown in Figure 6, where the silhouette coefficient reaches its maximum value of 0.8365.  
Figure 5: UAV track data clustering.  
Figure 6: UAV track data clustering profile coefficient.  
For the track data, the aforementioned model is applied. The data are classified according to the six clustering categories. The figure below shows the track classification from the central station to the third station, as shown in Figure 7.
# LoRaWAN Physical Layer-Based Attacks and Countermeasures, A Review
## 5 Discussion
### Research Potential
#### 5.2.1 Resources  
As discussed in Section 2.3, LoRaWAN communication is mostly based on a limited selection of transceiver IC designs from Semtech, namely, the SX127x and the SX130x families. Although this is beneficial in terms of, e.g., compatibility, many details of the Semtech chips remain undisclosed, which is unfavorable when it comes to the realization of wireless-physical-layer security techniques. For instance, the implementations of digital base-band algorithms and, in particular, the parts of it necessary to calculate metadata, e.g., RSSI/SNR, are only partly expressed in the data sheets [82].  
A more friendly alternative, to this end, would include a free and open-source hardware implementation of LoRa digital baseband on, e.g., an FPGA. Such a concept already exists for 802.11 based wireless research [83; 84], which has also expedited many contributions to physical-layer security. The possibility to define customized signal-processing functions directly on the LoRa transceiver should facilitate, e.g., deployments of more advanced RF-signal sensing and interference cancellation. Furthermore, the user-definable algorithms to perform, e.g., RSSI/SNR estimation, should be useful to enhance the quality as well as the technical readiness of radio-frequency fingerprinting, secret-key agreement and jamming-detection schemes. On the other hand, more advanced reactive jammer-attacker models could be defined with flexible DSP routines. As opposed to the jammer hardware presented with commodity LoRa modems capable of single channel operation only, the advanced reconfigurable hardware supporting wide-band reception will enable preamble detection and reactive jamming on several parallel channels.  
Further research focus beneficial for experimental-validation purposes concerns large collections of metadata, i.e., RSSI values or center frequency offsets, from large-scale LoRaWAN networks over longer periods of time. From a resilience point of view, such datasets, including reactive- and triggered-jamming attacks against event-based communication, shall become helpful to determine the accuracy of the presented jamming detectors in real-world conditions. Furthermore, datasets containing metadata from multigateway bidirectional communication with packet collisions are crucial to evaluate the robustness of secret key generation and radio-frequency fingerprinting algorithms. Finally, datasets collected from LoRaWAN use cases, in which attacks on physical layer would cause dis asters, e.g., flood /landslide sensors, should accelerate applied research activities around wireless-physical-layer security.
## 2 Materials and Methods
### Determination DPPH Radical Savenging Activity  
With some modifications, the USeNPs activity for free radical scavenging was estimated using the DPPH assay [44]. A 0.2 Mm 2,2-Diphenyl-1-picryl hydrazyl solution was prepared in methanol with a concentration of 0.0078 g/100 mL as a radical solution, then 1 mL of this was added to 1 mL of various concentrations of USeNPs solution with a ratio of 1:1 _V/V_. After 30 min of incubation at room temperature in darkness, the absorbance at 517 nm was measured using a spectrophotometer. To create a standard curve, ascorbic acid solutions ranging from 5 to 200 ug/mL were used as standards. DPPH radical scavenging activity (DPPHrsa) was expressed as mg ascorbic acid equivalents (AAE)/g-dried sample and was estimated using this equation:  
\[\text{DPPHrsa (\% inhibition)}=((\ \ \text{\text{\textlbrack}Abs}\ \text{ \text{\textlbrack}Control}-\ \ \text{\textlbrack}Abs\ \text{ \textrbrack}\text{ \textrbrack}\text{\text{\textlbrack}Sample}))/\ \text{\textlbrack}\text{\text{\textlbrack}Abs}\ \text{ \textrbrack}\text{\text{\textlbrack}Control}\times 100\]  
The control, containing all reagents except USeNPs, and all determinations were conducted in triplicate and the average values were tabulated.
_Axioms_**2014**, \(3\), 177-201; doi:10.3390/axioms3020177  
_axioms_  
**ISSN 2075-1680**  
www.mdpi.com/journal/axioms  
_Article_  
**Deterministic Greedy Routing with Guaranteed Delivery in 3D Wireless Sensor Networks**  
**[PERSON]**\({}^{1}\)**, **[PERSON]**\({}^{2}\)**, **[PERSON]**\({}^{3}\)**, **[PERSON]**\({}^{3,\ast}\)**and [PERSON]**\({}^{4}\)  
\({}^{1}\) Cisco Systems, Milpitas, CA 95035, USA; E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) Mathematics Department, Harvard University, Cambridge, MA 02138, USA;  
E-Mail: [EMAIL_ADDRESS]  
\({}^{3}\) The Center for Advanced Computer Studies, University of Louisiana, Lafayette, LA 70504, USA;  
E-Mail: [EMAIL_ADDRESS]  
\({}^{4}\) Department of Computer Science, Stony Brook University, Stony Brook, NY 11790, USA;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +01-337-482-1679.  
_Received: 22 February 2014; in revised form: 25 April 2014 / Accepted: 28 April 2014 / Published: 15 May 2014_  
**Abstract:** With both computational complexity and storage space bounded by a small constant, greedy routing is recognized as an appealing approach to support scalable routing in wireless sensor networks. However, significant challenges have been encountered in extending greedy routing from 2D to 3D space. In this research, we develop decentralized solutions to achieve greedy routing in 3D sensor networks. Our proposed approach is based on a unit tetrahedron cell (UTC) mesh structure. We propose a distributed algorithm to realize volumetric harmonic mapping (VHM) of the UTC mesh under spherical boundary condition. It is a one-to-one map that yields virtual coordinates for each node in the network without or with one internal hole. Since a boundary has been mapped to a sphere, node-based greedy routing is always successful thereon. At the same time, we exploit the UTC mesh to develop a face-based greedy routing algorithm and prove its success at internal nodes. To deliver a data packet to its destination, face-based and node-based greedy routing algorithms are employed alternately at internal and boundary UTCs, respectively. For networks with multiple internal holes, a segmentation and _tunnel_-based routing strategy is proposed on top of _VHM_ to support global end-to-end routing. As far as we know, this is the first work that realizes truly deterministic routing with constant-bounded storage and computation in general 3D wireless sensor networks.
# Optimization Algorithms for Scalable Stream Batch Clustering with k Estimation
## 5 Experiments
### Quantitative Analysis of Quality
The quality of the SPGMS results is worse than the SGMS ones, although the opposite was presented in [19] for their centralized counterparts when evaluating other validation indices. SPGMS is not guided by the SS index, unlike other algorithms, using likelihood instead. SPGMS achieved the worst result for 10M10-5. This result is derived from the fact that close to 96% of the full-clustering runs of SPGMS (using EM) over the data set overestimated the number of clusters, stopping at the defined upper bound. A different behavior was presented by SPKMS, which also uses projections and tests the goodness-of-fit. Such results indicate that models that were refined by EM and evaluated by likelihood performed worse than those that were refined by \(k\)-means and evaluated by SS in our experiments. SPKMS produced quality results for both artificial and real data sets. Moreover, it had a better score than SPGMS for all data sets and was able to cluster high-dimensional data sets (PPA and YTG). Moreover, it was the best algorithm for the 1M5-2, 1M10-5, and 10M10-5 data sets (Table 4). The quality of SPKMS results from the combination of four characteristics: (i) a systematic method to decide whether to increase \(k\), guided by goodness-of-fit tests and inherited from PG-means; (ii) the seeding of the new prototypes based on k-means++; (iii) the refinement of models by \(k\)-means and, (iv) the selection of the best partition by the SS index. Based on the ARI scores in Table 4, SPGMS presented the worst results for most data sets that it was able to process, and SPKMS was the best algorithm in most scenarios, followed by ISESC-CD, ISESC, ESSC, and SGMS. Furthermore, SPKMS was the best-evaluated considering normally distributed data sets, and SGMS was shown to be more accurate than SESC. Note that the clusters of the real data sets do not need to fit the Gaussian distributions, i.e., the goodness-of-fit tests are not expected to fit all data sets. However, SPKMS was among the best results for most of these data sets.
## 3 Materials and Methods
### Retrieval of Vegetation Properties Time Series Using HLS and Airborne Data and the SCOPE Model  
The SCOPE model is an integration of four modules ([PERSON] et al., 2016) that interact with each other to simulate the optical properties and physiological state of plants. The modules include: (a) the radiative transfer module in the optical domain (RTMo) for simulating TOC reflectance by tracking the propagation of incident solar and sky radiation; (b) the radiative transfer module for thermal radiation (RTMt), to simulate TOC outgoing thermal radiation ; (c) energy balance and biochemical routines to simulate heat fluxes and photosynthesis; and (d) a radiative transfer module that simulates the TOC spectrum for chlorophyll fluorescence (RTMf). In this study, we inverted the RTMo module as a separate model to retrieve vegetation properties and finally simulated the photosynthesis (i.e., GPPSIM) using forward SCOPE modeling. The leaf level model PROSPECT5 ([PERSON], 2016) and the canopy-level reflectance model 4 SAIL (scattering by arbitrary inclined leaves) ([PERSON], 2016) are combined in the RTMo model of SCOPE. The PROSPECT5 model simulates the leaf-level reflectance and transmittance from the input biochemical leaf properties. Leaf-level optical properties are further upscaled on canopy scale by accounting for the canopy architecture using the 4 SAIL model. We also incorporated the effect of soil background (soil brightness and SWC) on the simulated reflectance using the brightness-shape-moisture (BSM) submodel as previously suggested ([PERSON] et al., 2016; [PERSON] et al., 2016). Table 2 provides the input parameters of the RTMo model and the BSM submodel. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Meteorological Variable** & **Symbol** & **Unit** & **Min** & **Max** & **Mean** & **SD** \\ \hline Air temperature & T\({}_{\text{a}}\) & \({}^{\circ}\)C & \(-\)0.66 & 24.69 & 13.77 & 5.09 \\ Incoming shortwave radiation & R\({}_{\text{in}}\) & W m\({}^{-2}\) & 32.57 & 523.19 & 297.87 & 123.21 \\ Incoming longwave radiation & R\({}_{\text{li}}\) & W m\({}^{-2}\) & 258.48 & 391.54 & 328.12 & 27.37 \\ Vapor pressure & e\({}_{\text{a}}\) & hPa & 3.44 & 19.49 & 11.50 & 3.41 \\ Air pressure & p & hPa & 895.04 & 925.07 & 914.39 & 5.68 \\ Wind speed & u & m s\({}^{-1}\) & 0.77 & 6.90 & 1.85 & 0.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary statistics of daily (means of half-hourly measurements) meteorological variables during the 2016 vegetation growing season. Min: minimum of daily means, Max: maximum of daily means, Mean: mean of daily means, SD: standard deviation of daily means.
# A Survey on Optimal Channel Estimation Methods for RIS-Aided Communication Systems
## 3 Channel Estimation in the RIS-Assisted Communication Systems
### MIMO Systems
#### 3.3.3 Algorithm for an RIS-Assisted AB-HBF System  
The authors in [17] create an RIS-assisted AB-HBF mMIMO cell system, as mentioned in Section 2. Their purpose is to maximize the achievable system rate by reducing the CSI overhead size and hardware complexity. First, the RF beamformers are designed based on slow-time varying angular parameters. Then, the BB precoder/combiner is designed with a SVD and water filling algorithm using an efficient channel with reduced size. Finally, to enhance the system capacity, the phase shifts of the RIS use a PSO method. For more details, see Algorithm 1 in [17].  
3.4 Channel Estimation Algorithms for the Cases with Long-Term Imperfection (LTI) and Short-Term Imperfection (STI)  
In [1], the authors propose different efficient tensor algorithms for channel estimation in RIS-assisted MIMO systems, with the RIS elements affected by real-world imperfections. Non-ideal channel estimation problems are solved with trilinear and quadrilinear PARAFAC. The proposed trilinear ALS (TALS)-based LTI algorithm solves with static imperfections. The TALS-STI and higher-order singular value decomposition (HOSVD)-STI algorithms are used for demanding scenarios with non-static behavior of RIS imperfections and with channel temporal coherence. Furthermore, the TALS-LTI and TALS-STI algorithms have iterative solutions, to relax the system requirements and work with more options for training parameters. The HOSVD-STI algorithm has a closed-form solution, has a lower computational complexity than ALS algorithms, and performs parallel processing. For more details, see Algorithms 1-3 in [1].
Future Internet_**2013**, \(5\), 113-127; doi:10.3390/fi5020113  
_future internet_  
**ISSN 1999-5903**  
www.mdpi.com/journal/futureinternet  
_Article_  
**A Concept for Support of Firefighter Frontline Communication**  
**[PERSON] \({}^{1,\ast}\), [PERSON] \({}^{1}\), [PERSON] \({}^{2}\), [PERSON] \({}^{3}\), [PERSON] \({}^{4}\) and [PERSON] \({}^{1}\)**  
\({}^{1}\) TecO, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany;  
E-Mails: [EMAIL_ADDRESS] (D.G.); [EMAIL_ADDRESS] (M.B.)  
\({}^{2}\) Fraunhofer Institute for Applied Information Technology FIT, 53754 Sankt Augustin, Germany;  
E-Mail: [EMAIL_ADDRESS]  
\({}^{3}\) National Institute of Informatics (NII), Tokyo 101-8430, Japan; E-Mail: [EMAIL_ADDRESS]  
\({}^{4}\) Department of Information Systems and New Media, Universitat Siegen, 57076 Siegen, Germany;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +49-721-608-4-1708.  
_Received: 24 December 2012; in revised form: 15 March 2013 / Accepted: 8 April 2013 / Published: 16 April 2013_  
**Abstract:** In an indoor firefighter mission, coordination and communication support are of the utmost importance. We present our experience from over five years of research with current firefighter support technology. In contrast to some large scale emergency response research, our work is focused on the frontline interaction between teams of firefighters and the incident commander on a single site. In this paper we investigate the flaws in firefighter communication systems. Frequent technical failures and the high cognitive costs incurred by communicating impede coordination. We then extract a list of requirements for an assistant emergency management technology from expert interviews. Thirdly, we provide a system concept and explore challenges for building a novel firefighter support system based on our previous work. The system has three key features: robust ad-hoc network, telemetry and text messaging, as well as implicit interaction. The result would provide a complementary mode of communication in addition to the current trunked radio.  
**Keywords:** firefighter; communication flaws; communication assistance
## 3 Discussion of Results from Sensitivity Analyses
### Studies Concerning Tank Materials and Technologies
#### 3.1.1 Sensitivity to Structural Materials
* A \(L/D_{str}\) value outside the (1, 1.5) interval should be selected, moving towards higher or lower values depending on the required amount of LH\({}_{2}\). In the following cases, a value of 1.5 was adopted;
* A \(d_{supp}/L\) value lower than 0.8 should be considered. In the following cases, a value of 0.8 has been adopted. Additional observations can be made for the welding efficiency (\(\eta_{w}\)) and the operating temperature, which are both expected to influence the design according to Equations (17), (19) and (20). Results of the analyses, not reported here for brevity, indicate that the effect of \(\eta_{w}\) in the range [0.7, 1] is negligible for all the structural materials considered, whereas the variation in mechanical characteristics with temperatures between 20 and 280 K is barely observable only for the case of AA 2219 above 100 K, well away the cryogenic conditions. Such a low sensitivity is mainly due the adoption of a minimum value for the thickness of tank walls, as prescribed by [39].