esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of New Orthogonal Transforms for Digital Watermarking
## 3 Watermark Embedding
### Method 2: Adaptive Substitution of Grouped Spectral Coefficients (M2)  
This method consists of grouping of the spectrum and then selecting these channels that carry the lowest amount of signal information. Spectrum grouping means that each channel contains all the spectral coefficients that were taken from the same position of each block processed in the forward transform step. This results in 64 transform channels. The further processing is dependent on the nature of the given transform. For instance, the Discrete Cosine Transform has a property of locating the coefficients that represent the low frequencies, to which human eye is much more sensitive, in the top-left channel of the image spectrum with applied grouping [18]. Then, moving across the spectrum in the zigzag order leads to channels representing higher frequencies which are not so noticeable for people. That is why a watermark is inserted in the middle-band frequencies as a trade-off between high robustness and good visual quality. During JPEG compression, the coefficients from the bottom-right channels, representing high frequencies, are removed. However, this paper will not focus on robustness to different attacks such as compression, and for the testing phase, the PNG image format was selected since it supports lossless data compression. Nevertheless, in case of the transforms described in Section 2, their nature may be diverse, and in order to assure that the conditions related to the visual quality and recovery accuracy are met, it is not possible to always follow the same scheme. Therefore, the blocks in which the information bits are inserted are determined in an adaptive manner. This is realized by the calculation of mean values for all the spectrum channels and sorting them in the ascending order. Then, based on this order, the spectral coefficients from the following channels are modified in order to store the information bits. This method consists of replacing the spectral coefficient values with information bit values multiplied by the given constant. Up to some extent, the bigger this constant is, the higher the accuracy in the recovery phase. However, it is accompanied by the higher degradation of the quality of the image with an embedded watermark.
Future Internet_**2011**, \(3\), 298-318; doi:10.3390/fi3040298  
_future internet_  
**ISSN 1999-5903**  
www.mdpi.com/journal/futureinternet  
_Article_  
**A Service-Oriented Architecture for Proactive Geospatial Information Services**  
**[PERSON] \({}^{1}\) and [PERSON] \({}^{2,*}\)**  
\({}^{1}\) School of Geosciences and Info-Physics, and School of Civil Engineering, Central South University, 22 Shaoshan South Road, Changsha 410004, China; E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) Department of Land Surveying & Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China  
\({}^{*}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +852-2766-4335; Fax: +852-2330-2994.  
_Received: 18 November 2011; in revised form: 12 December 2011 / Accepted: 15 December 2011 / Published: 19 December 2011_  
**Abstract:** The advances in sensor network, linked data, and service-oriented computing has indicated a trend of information technology, _i.e._, toward an open, flexible, and distributed architecture. However, the existing information technologies show a lack of effective sharing, aggregation, and cooperation services to handle the sensors, data, and processing resources to fulfill user's complicated tasks in near real-time. This paper presents a service-orientated architecture for proactive geospatial information services (PGIS), which integrates the sensors, data, processing, and human services. PGIS is designed to organize, aggregate, and co-operate services by composing small scale services into service chains to meet the complicated user requirements. It is a platform to provide real-time or near real-time data collection, storage, and processing capabilities. It is a flexible, reusable, and scalable system to share and interoperate geospatial data, information, and services. The developed PGIS framework has been implemented and preliminary experiments have been performed to verify its performance. The results show that the basic functions such as task analysis, managing sensors for data acquisition, service composition, service chain construction and execution are validated, and the important properties of PGIS, including interoperability, flexibility, and reusability, are achieved.  
**Keywords:** geospatial information service; linked service; service-oriented architecture; service chain
## 4 Discussion
### Parasitic Load Monitoring  
The infested beehives had an extremely high infection rate, with a mean drop of 145 mites/day (Figure 1), which greatly exceeds the maximal threshold of 15 fallen mites per day (end of June and early August) recommended to establish an immediate mitotiche treatment (_Ministere de l'Agriculture, des Pecheries et de l'Alimentation du Quebec_--MAPAQ). The fall rate of mites from the control hives was generally below 10 over the observational period, indicating a good mitotiche control as per the MAPAQ monitoring chart. As this cohort study included a group of hives that were exposed to extreme parasitic conditions, the comparison between the two groups and the evaluation of the effects of varroa on honeybees are also enhanced.  
This study stresses the importance of controlling mite infestation by mid-August as per the modeling of mite population elaborated by [PERSON] et al. [15,32]. The difference in varroa parasitic load between the studied groups was insignificant in the first week of the experimentation (June), supporting the importance of an early mitotiche control to prevent an excessive growth of mite populations later in the summer and fall.  
The sticky board method for monitoring of varroa parasitic load is frequently used in Quebec since it is easy to install and has a high sensitivity. However, this technique shows a great inter-day variation and does not give a direct estimate of mites per bee within the hive since it represents only the dead parasites and does not consider varroas in their reproductive stages [15]. Therefore, the exact number of varroa mites within the hive is much higher.
## 4 Multi-Antenna Frequency-Selective Channels  
In this section, we study the more general scenario with any number of antennas and with frequency-selective channels, resulting in \(S>1\). As we will discuss, a naive extension of the techniques presented in the previous sections is undesirable, because this would not leverage the structure of the channel model (4). For this reason, in the following, we will introduce novel hybrid model- and data-driven solutions that build on the channel model (4).
# Survivable Deployments of Optical Sensor Networks against Multiple Failures and Disasters: A Survey
## 4 AI-Assisted Sensing and Survivability Techniques
### AI-Assisted Sensing on Learning  
Since sensors have limited range and coverage, mobile robots often have to make decisions on where to point their sensors. A good sensing strategy allows a robot to collect information that is useful for its tasks. In [148], the author showed how to learn active sensing strategies for a mobile robot using least square policy iteration, and experimental results suggested their approach was able to learn highly effective sensing strategies. Spectrum sensing plays a key role for opportunistic spectrum access in cognitive radio networks (CRN). Two main sensing schemes consist of individual spectrum sensing, in which each secondary user (SU) makes sensing decisions independently, and cooperative spectrum sensing, in which multiple SUs exchange their sensing information with the fusion center, which makes the final decision. In [149], the author proposed novel cooperative spectrum sensing (CSS) algorithms for cognitive radio (CR) networks based on machine learning techniques, which were used for pattern classification, and concluded that the weighted KNN classifier was well suited for CSS, which required the updating of training energy vectors on-the-fly. In [150], the author proposed a low-dimensional probability vector as the feature vector for machine-learning-based classification, instead of the N-dimensional energy vector, in a CRN with a single primary user (PU) and N secondary users (SUs); due to its lower dimension, the probability vector based classification was capable of having a smaller training duration and a shorter classification time for testing vectors. With the fast advances in mobile devices and communication technologies, various machines and devices are capable of interacting with each other within a network, that is, the IoT. In [151], the author presented a general deep learning framework for RF sensing in the IoT, along with several experimental case studies, and a discussion of challenges and open problems. Sensor-equipped smartphones and wearables are transforming a variety of mobile apps ranging from health monitoring to digital assistants. In [152], the author investigated the potential for techniques from deep learning to address a number of critical barriers to mobile sensing surrounding inference accuracy, robustness, and resource efficiency.
# Volatile Profile of Nuts, Key Odorants and Analytical Methods for Quantification
## 5 Volatile Profile of Nuts after Thermal Treatments
### Deep-Frying Processing  
During the deep-frying process, nuts are immersed in different frying oils at 160-200 \({}^{\circ}\)C in the presence of air [69]. Frying involves the immersion of food in hot oil and during this process, some of the moisture is replaced by the frying oil, leading to the formation of pores that allow oil penetration into the created voids [70]. As a consequence, it is expected that differences in the volatile profile and sensory characteristics of nuts could appear. Aldehydes, pyrazines and alcohols were the predominant VOCs present in fried almonds [61; 62]. Concerning Maillard products, benzyl alcohol, methional, 2-methylbutanal, 3-methylbutanal and 2,5-dimethylpyrazine were reported, whereas 1-butanol, 1-octen-3-ol, hexanal and 1-pentanol were produced due to lipid oxidation. In this sense, the concentration of 1-pentanol increased as a linoleic acid oxidation product [62]. When the results obtained by the frying process were compared with those obtained by hot air roasting, common obtained VOCs were observed, but fried almonds showed 18 additional volatile compounds (2-ethyl-5-methylpyrazine, 3-ethyl-2,5-dimethylpyrazine, diacetyl, (Z)-1,5-octalien-3-one, acetic acid, (E,E,Z)-2,4,6-nonatrienal, cyclotene and 11 unknown compounds). Meanwhile, nine other compounds were identified in the hot air-roasted almonds and not in the fried ones ((E,Z)-2,6-nonadienal, (E)-2-decenal, (E)-4,5-epoxy-(E)-2-decenal, methyl propyl disulfide and five unknown compounds). In general, fried almonds presented a higher number and a higher concentration of tranones and nitrogen-containing compounds. On the other hand, the hot air-roasted almonds were richer in aldehydes and sulfur compounds. The authors justified this behavior as the differences in energy transference and time and temperature. In this sense, in the fried almonds, the energy transfer is mainly produced by conduction, while in hot air-roasted samples, air convection predominates. As oil heat transfer is more efficient, more Maillard reaction and sugar degradation products are formed [42].  
Regarding fried chestnuts, hexanal, octanal, nonanal, furfural, 3-heptanone, and 4-hydroxy-2-butanone were identified as the main VOCs after 15 min of processing at 240 \({}^{\circ}\)C [68]. In particular, 3-heptanone and 4-hydroxy-2-butanone were underlined as characteristics of the frying thermal processing of chestnuts.
# Design and Analysis of a Novel Floating Docking Mechanism for On-Orbit Refueling
## 2 Description of the Mechanism
### System Design  
The proposed mechanism adopts the traditional probe-drogue configuration with an active drogue and a passive probe. As shown in Figures 3 and 4, the active service part comprises a docking subassembly, passive alignment subassembly, driving subassembly, supporting subassembly, and refueling pipe as the service part. The passive client part consists mainly of a docking subassembly. The basic information of the docking mechanism--i.e., dimension and weight are shown in Table 1--and the direction definitions of each length are shown in Figure 3.  
The docking subassembly which is made up of probe and drogue, a pair of electric connectors and a pair of fluidic connectors are used for establishing connection between the service and client part. The supporting subassembly is composed of several supporting structures, playing the role of supporting and fixation. As for the passive alignment subassembly, driving subassembly, and refueling pipe, they are core subassembibles of the proposed mechanism and will be introduced in detail below.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Dimensions**} & \multirow{2}{*}{**Weight**} \\  & \multicolumn{1}{c}{**X-Direction**} & \multicolumn{1}{c}{**Y-Direction**} & \multicolumn{1}{c}{**Z-Direction**} \\ \hline Active part & 174 mm & 205 mm & 174 mm & 4.74 kg \\ Passive part & 119 mm & 46 mm & 99 mm & 0.72 kg \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic information of the docking mechanism.
## 3 Experiments and Discussion
### Dataset Description
\begin{table}
\begin{tabular}{c c c c} \hline
**Layer (Type)** & **Kernel Size** & **Stride** & **Padding** & **Output Shape** \\ \hline \hline \multicolumn{1}{c}{Residual Connection\_1} & (3,3,3) & (1,1,1) & (0,0,0) & Out\_R\_1: (15,15,14,32) \\ Add (Out\_C1, Out\_R1) & & & Out\_A1: (15,15,14,32) \\ Reshape (Out\_A1) & & & Out\_Re: (15,15,448) \\ Separable\_Conv2D\_4\_1\_1 & (3,3) & (1,1) & (0,0) & Out\_4\_1\_1: (13,13,64) \\ Conv2D\_4\_1\_2 & (1,1) & (1,1) & (0,0) & Out\_4\_1\_2: (13,13,64) \\ Separable\_Conv2D\_4\_2\_1 & (3,3) & (1,1) & (0,0) & Out\_4\_1\_1: (13,13,64) \\ Conv2D\_4\_1\_2 & (1,1) & (1,1) & (0,0) & Out\_4\_1\_2: (13,13,32) \\ Separable\_Conv2D\_4\_2\_3 & (3,3) & (1,1) & (0,0) & Out\_4\_2\_1: (13,13,64) \\ Conv2D\_4\_2\_4 & (1,1) & (1,1) & (0,1) & Out\_4\_2\_2: (13,13,64) \\ Concatenate\_2 (Out\_4\_1\_2, Out\_4\_2) & & & Out\_SE: (13,13,64) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._The proposed method is compared with various existing classification methods: an SVM [32], a 2D CNN [33], a 3D CNN [34], an SSRN [21], and HybridSN [23]. All experiments are performed on Python, and the experimental computing platform is configured with an Intel Core i7-12700k (Santa Clara, CA, USA), an Nvidia GeForce GTX 3070 Ti (Santa Clara, CA, USA), and 16 GB of RAM (Microsoft Corporation, Redmond, WA, USA). The kappa (\(\kappa\)) value, overall accuracy (OA), and average accuracy (AA) are used to evaluate the performance of the classification methods.
## 3 Results and Discussion
### First Experiment  
In the first experiment, we intended to test a correlation between the RADAR's signal and the soil water content derived from the capacitive soil moisture sensor. To achieve the goals of this experiment, we recorded the data from the sensors for more than an hour, without changing the soil and the sensor's conditions, to acquire the values for the soil's state. After that, we added to the soil 300 mL of water directly below the RADAR.  
We arranged the readings to make a graphic of the RADAR's signal, soil humidity sensor signal, temperature, and humidity of the air by the time to see the evolution of the RADAR's signal over time and compare it to the rest of the data. Figure 3 presents the results from two runs of this experiment.  
Both derived soil moisture sensing techniques strongly respond to the water-added event, showing sharp readings fall. The sensors' signal reached a minimum, which was followed by a consistent signal increase in a short period, likely related to water drainage. After this period, the soil moisture sensors behave differently. While the RADAR sensor shows a time profile toward signal stability or even descent, the capacitive sensor, in both experiments, shows a clear tendency toward a signal increase. In this period, the temporal pattern of the RADAR sensor signal is similar to the humidity sensor signal and quite  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **RADAR** & **Ground-Truth** & **Distance from** & **Description** \\  & **Sampling Period** & **Sampling Period** & **RADAR to Soil** & **Description** \\ \hline
1 & 60 s & 30 s & 9 cm & Use the RADAR in a vase with soil \\ \hline
2 & 300 s & 100 s & 9 cm & Use the RADAR in a big container \\  & & & & made of plastic filled with soil \\ \hline
3 & 30 s & 5 s & 9 cm & Use the RADAR in a plastic container \\ \hline
4 & - & - & 16 cm & Use the RADAR to map the soil \\  & & & & moisture in a big container \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the four experiments implemented.  
different from the capacitive sensor time profile. After adding water, the temperature and soil moisture show a distinct evolution in both experiments. In one of the situations, the temperature has a tendency to rise and the humidity has a tendency to decrease. Whilst in another experiment, these variables show a contrary trend after adding water. Soil moisture sensor capacitance-based seems to be affected by the soil temperature, as reported previously by [PERSON] et al. [13].  
The calculated Spearman's correlation between the signals of both sensors yields 0.78. Meanwhile, the cross-correlation function (CCF) between the signals of both sensors yields 0.94 with a 5 min delay.
# Research on a Measurement Method for the Ocean Wave Field Based on Stereo Vision
## 2 Materials and Methods
### Stereo Matching  
#### 2.2.1 Scale-Invariant Feature Transform (SIFT) Feature Extraction and Matching  
The first part of the SIFT process is the production of a Gaussian scale space [24], in which each octave is computed by means of the convolution between a down-sampled image and different Gaussian kernels, such as \(\mathrm{L}(\mathrm{x},\mathrm{y},\sigma)\) in Equation (1), where \(\mathrm{G}(\mathrm{x},\mathrm{y},\sigma)\) is a variable-scale Gaussian kernel and \(\mathrm{I}(\mathrm{x},\mathrm{y})\) is an input image.  
Next, the second part includes a difference of Gaussian (DoG) scale space [25], this DoG is a close approximation of the Laplacian of Gaussian (LoG), as expressed in Equation (2). Compared with LoG, DoG has lower computational costs and uses approximately the same function to extract stable features.  
\[\mathrm{L}(\mathrm{x},\mathrm{y},\sigma)=\mathrm{G}(\mathrm{x},\mathrm{y}, \sigma)\times\mathrm{I}(\mathrm{x},\mathrm{y}) \tag{1}\]  
\[\mathrm{D}(\mathrm{x},\mathrm{y},\sigma)=\mathrm{L}(\mathrm{x},\mathrm{y}, \mathrm{k}\sigma)-\mathrm{L}(\mathrm{x},\mathrm{y},\sigma) \tag{2}\]  
The third part of SIFT contains the pixel and sub-pixel localizations of a keypoint [26]. The extreme pixel is compared with 26 neighboring pixels in a DoG scale space, which is chosen as the candidate pixel keypoint. Then, the localization of the sub-pixel keypoint is implemented using a quadratic approximation, which is computed using the second-order Taylor expansion.  
The fourth part of the SIFT algorithm uses 128 dimension descriptors [27] to conduct the feature matching process. The SIFT feature descriptors represent the image gradient magnitudes and orientations in a \(4\times 4\) sub-region around the candidate feature.  
Unfortunately, this strategy needs high computational power and memory consumption, which limit the use of SIFT in real-time engineering applications [28]. Notably, the above steps of the primal SIFT can run on a GPU in parallel fashion. It is shown that the performance of SiftGPU is nearly real-time for the images and the quality of the features that are extracted by SiftGPU is approximately the same as those that are extracted by the primal SIFT. However, since the GPU's memory is limited, large images need to be down-sampled for size reduction before applying SIFT [29].  
Due to the limitations of GPU and SIFT, a gridding siftGPU method is proposed in this work in order to extract and match the features from large images more efficiently.
# Smart Preliminary Channel Access to Support Real-Time Traffic in Wi-Fi Networks
## 4 Investigated Methods to Support RTA
### Tuned EDCA  
The first method is Tuned EDCA [15]. It involves reducing the TXOP Limit for the AC corresponding to non-RTA STAs. Note that reducing the time for which non-RTA STAs can occupy the channel decreases the payload portion in one transmission, and, as a result, decreases the throughput. Therefore, the TXOP Limit will not be reduced below the value that is enough to provide the required delays for RTA.
# Optimization of Submodularity and BBO-Based Routing Protocol for Wireless Sensor Deployment
## 3 Routing Protocols for Wireless Sensor Networks
### Fitness Function  
The fitness values are based on parameters used to achieve the best solution. It considers intra-cluster compactness, inter-cluster separation and total energy consumption.  
Tightness refers to the internal distance, that is, the distance between the nodes in the cluster and the cluster head (\(CH\)).  
\[C=\sum_{i=1}^{CHs}\sum_{\forall n\in C_{i}}d(n,CH_{i}). \tag{20}\]  
Separability refers to the distance between clusters, that is, the minimum distance between cluster heads.  
\[S=\min_{\forall C_{i},C_{j},C_{i}\
eq C_{j}}(d(CH_{i},CH_{j})). \tag{21}\]  
The total energy consumption refers to both the cluster head communication energy consumption \(E_{CH}\) and ordinary node communication energy consumption \(E_{NN}\), of which the energy consumption of the cluster head includes the energy consumption \(E_{RN}\) required to receive data sent by the nodes in the cluster, the energy consumption \(E_{HD}\) required to collect data for fusion, and the energy consumption \(E_{TB}\) required to send data to the base station. The energy consumption of ordinary nodes includes theenergy consumption \(E_{TH}\) required to send data to the cluster head. Assume that the total number of nodes is \(N_{A}\), the number of cluster heads is \(m\), and the ordinary nodes in each cluster are \(n_{1}\), \(n_{2}\),..., \(n_{m}\).  
\[E_{CH}=E_{RN}+E_{HD}+E_{TB}=kE_{elec}n_{i}+kE_{DA}(n_{i}+1)+(kE_{elec}+kE_{mp}l_{ HB}^{4}). \tag{22}\]  
In Equation (16), \(E_{DA}\) is the energy consumed by unit bit data fusion, and \(l_{HB}\) is the distance between the cluster head and the base station.  
\[E_{NN}=E_{TH}=kE_{elec}+kE_{fs}l_{NH}^{2}. \tag{23}\]  
In Equation (17), \(l_{NH}\) is the distance between the nodes in the cluster and the cluster head.  
\[E_{total}=\sum_{i=1}^{m}E_{CH_{i}}+\sum_{j=1}^{N}E_{NN_{j}}. \tag{24}\]  
In Equation (18), the closer the distance between the cluster nodes and the cluster head in a cluster, the better. The greater the separation between cluster heads, the better the total energy consumption. The fitness function is as follows.  
\[F=w_{1}C+\frac{w_{2}}{S}+w_{3}E_{total},w_{1},w_{2},w_{3}\in(0,1). \tag{25}\]  
In Equation (19), \(w_{1}\)+\(w_{2}\)+\(w_{3}\)=1.
## 4 Performance Evaluation
### Evaluation Scenarios and Metrics
For streaming 200 segments from the server with the 2-push strategy, baseline values of 100 HTTP responses and 100 PUSH_PROMISE frames must be precisely achieved. A summary of the above evaluation metrics is shown in Table 5. In the following subsection, the experimental results in each scenario are presented in details.