esa-sceva/satcom-chunk-collection huggingface dataset:
# REERP: A Region-Based Energy-Efficient Routing Protocol for IoT Wireless Sensor Networks
###### Abstract  
An essential component of the Internet of Things (IoT) is wireless sensor networks (WSNs). Since individual sensor nodes are strongly power-constrained, several techniques are adopted to save power. By grouping nodes into clusters--thus reducing the transmission distance between sensor nodes and the base station (BS)--a clustering protocol can ensure energy preservation and increase the lifetime of the network. However, current clustering techniques have problems with the clustering structure that negatively impact their performance. Whenever routing protocols were implemented for a longer period of time, it was observed that they had a higher rate of energy consumption, a shorter period of stability, and fewer data transfers to the BS. In this paper, an improved region-based routing protocol (REERP) is developed for wireless sensor networks in the IoT is developed. It is based on (i) the addition of new nodes to the already formed clusters, (ii) the selection of the new head node based on the amount of residual energy, (iii) the setup of the multi-hop communication in all the regions of network, and (iv) the utilization of the energy hole reduction method. All of these tactics increase the useful life of the network. Performance has been evaluated against (1) a stable election protocol, (2) a gateway energy-aware routing protocol, and (3) a heterogeneous gateway energy-aware routing protocol, and using the metrics lifetime, energy consumption, number of dead nodes, and number of packets sent to the base station vs. number of packets acquired by the base station. The results of the proposed routing protocol have been found to outperform the state-of-the-art approaches considered.  
Internet of Things (IoT); wireless sensor networks (WSN); routing protocols; energy utilization +
Footnote †: journal: Accepted: 25 August 2023
## 4 Experiment and Result Analysis
### Evaluating Indicator  
Because this article is aimed at identifying multiple intentions, the macro average accuracy (Macro_P), macro average recall rate (Macro_R), and macro average F1 value (Macro_F1) were used as evaluation indicators. The confusion matrix is shown in Table 5.  
The calculation method for the evaluation indicators is as follows:  
In the following formula, \(i\) represents the \(i\)-th intention category and \(N\) represents the total number of intention categories.  
\[Macro\_P=\frac{1}{N}\biggl{(}\sum\frac{TP_{i}}{TP_{i}+FP_{i}}\biggr{)} \tag{10}\]  
This value is the macro average accuracy, which represents the overall accuracy of the model's prediction results for each type of label. The closer this value is to 1, the better the model's accuracy.  
\[Macro\_R=\frac{1}{N}\biggl{(}\sum\frac{TP_{i}}{TP_{i}+FN_{i}}\biggr{)} \tag{11}\]  
This value is the macro average recall rate, which represents the coverage of the model's prediction results for each type of label and the sum of the average values to obtain the overall coverage. The closer this value is to 1, the better the recall rate of the model.  
\[Macro=\frac{1}{N}\biggl{(}\sum\frac{2\times P_{i}\times R_{i}}{P+R}\biggr{)} \tag{12}\]  
This value is the macro average F1 value, which represents the accuracy and coverage of the model's prediction results for each type of label. This value is the harmonic average of the accuracy and recall. The closer this value is to 1, the better the model's performance.  
The Influence of the Number of Attention Heads and the Size of Convolutional Kernels on the Model's Intention Recognition Performance  
To verify the effect of the number of heads and the size of the convolutional kernel on the intention recognition, this article used the method of controlling variables for comparative verification. Firstly, the convolutional kernel size was set to [3,4,5] unchanged, and the number of attention heads was set to 4, 8, 12, and 16, respectively. The results are shown in the first row of Figure 5: when the number of attention heads was set to 12, the model performed best; therefore, in the subsequent experiments, this model set the number of attention layers of multiple heads to 12 unchanged, and set the convolutional kernel sizes to [2,3,4], [3,4,5], and [4,5,6] for comparative experiments. The results are shown in the second row of Figure 5: when the convolutional kernel size was [3,4,5], [3,4,5,6], the model had the best intention recognition effect. Due to the very similar values, in order to reduce computational costs, this model set the convolutional kernel size to [3,4,5].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Real** & \multicolumn{2}{c}{**Prediction Category**} \\ \cline{2-3}
**Category** & **Positive** & **Negative** \\ \hline Positive & TP & FN \\ \hline Negative & FP & TN \\ \hline \hline \end{tabular}
\end{table}
Table 5: Confusion matrix.
# Optimal Power Dispatch of an Offshore Wind Farm under Generator Fault
## 5 Case Study  
In order to verify the effectiveness of the proposed Optimal Dispatch Strategy (ODS), the cases were simulated to explain the effects of ODS from three aspects: power generation capacity, power fluctuation, and fault accommodation. In Case 1, all the WTs were in the normal condition, which means no fault occurred. The power generation capacity of the WF in ODS was compared with that in PDS. In Case 2, WF operated in the normal condition, as well as in ODS, but \(k2\) in the objective function took different values. In Case 3, a WT had a generator cooling system fault. The proposed ODS was compared with two different fault-handling methods in PDS.  
The WF and strategies used here are described in Sections 2 and 3. After many trials and adjustments, the weight factors in objective functions: \(k1\), \(k2\), \(k3\) are set to 10, 4, 3, respectively. The parameters of the WT are shown in Table 1.
# Underwater Electromagnetic Sensor Networks--Part I: Link Characterization +
## 3 Results
### Electric Dipole Performance  
Several measurements were made with insulated dipoles described in Section 2.3. However, the performance of these antennas was very disappointing. Dipole received power was tenths of decibels below loop antennas in all distances and scenarios tested. The signal generator had to be forced to transmit its maximum amplitude (23 dBm of power) when the antennas were separated just 1 m. Therefore, they were dismissed for further experiments.  
In addition, some experiments were carried out using insulated dipoles with uninsulated terminations, that is, with 1 cm of both ends of the dipole elements in direct contact with sea water. In this case, distances of 6 m were reached before signal disappeared below our instrument noise floor (\(\approx\)\(-\)120 dBm), so it did not fulfill the network distance requirements. Moreover, loop antenna attenuation was up to 20 dB lower at 1 m, for instance. Having said that, these dipole antennas were also dismissed.
# Carcass Yield and Meat Quality of Broiler Chicks Supplemented with Yeasts Bioproducts
## 3 Results
### Meat pH and the Chemical Proximate Analysis
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline _Group_ & SC & RM & **LW\({}^{2}\)** & **CW\({}^{2}\)** & **CY\({}^{3}\)** & **Breast\({}^{3}\)** & **Thigh\({}^{3}\)** & **Drumistics\({}^{3}\)** & **Liver\({}^{3}\)** & **Gizzard\({}^{3}\)** & **Hear\({}^{3}\)** & **Abd. Fat\({}^{3}\)** \\ \hline
**1** & **0** & **No** & 2761.80 & 2070.40 & 74.98 & 41.59 & 12.51 & 10.35 & 2.83 & 1.90 & 0.620 & 2.61 \\
**2** & **0.6** & **No** & 2789.60 & 2064.00 & 74.06 & 41.76 & 12.08 & 10.39 & 2.73 & 2.04 & 0.589 & 2.11 \\
**3** & **1** & **No** & 2723.80 & 2049.40 & 75.24 & 41.94 & 12.37 & 10.57 & 2.81 & 2.06 & 0.628 & 1.64 \\
**4** & **1.3** & **No** & 2525.80 & 1902.40 & 75.38 & 41.88 & 12.51 & 10.95 & 3.08 & 2.20 & 0.685 & 1.44 \\
**5** & **0** & **Yes** & 2744.60 & 2046.20 & 74.57 & 41.32 & 12.10 & 10.84 & 2.61 & 1.58 & 0.529 & 2.12 \\
**6** & **0.6** & **Yes** & 2659.40 & 1975.00 & 73.61 & 41.65 & 12.13 & 10.97 & 2.78 & 1.71 & 0.643 & 2.16 \\
**7** & **1** & **Yes** & 2555.80 & 1925.20 & 75.32 & 41.71 & 12.53 & 10.79 & 2.74 & 1.85 & 0.638 & 1.96 \\
**8** & **1.3** & **Yes** & 2463.00 & 1872.60 & 75.93 & 41.97 & 12.81 & 10.35 & 2.96 & 2.89 & 0.644 & 1.47 \\ \hline
**SEM** & & & & & & & & & & & & \\
**\(p\) value** & & & & & & & & & & & & & \\ \hline \multicolumn{10}{c}{**Main effect—SC levels**} \\ \hline
**0** & 2753.20 \({}^{*}\) & 2058.30 \({}^{*}\) & 74.77 & 41.46 & 12.31 & 10.60 & 2.74 & 1.88 \({}^{b}\) & 0.56 & 2.37 \({}^{a}\) \\
**0.6** & 2724.50 \({}^{*}\) & 2010.30 \({}^{b}\) & 73.84 & 41.71 & 12.11 & 10.68 & 2.76 & 1.88 \({}^{b}\) & 0.62 & 2.14 \({}^{b}\) \\
**1** & 2639.80 \({}^{b}\) & 1987.30 \({}^{b}\) & 75.28 & 41.88 & 12.45 & 10.68 & 2.78 & 1.96 \({}^{b}\) & 0.63 & 1.80 \({}^{b}\) \\
**1.3** & 2494.40 \({}^{b}\) & 1888.30 \({}^{b}\) & 75.66 & 41.93 & 12.66 & 10.65 & 3.03 & 2.54 \({}^{a}\) & 0.66 & 1.45 \({}^{c}\) \\ \hline
**SEM** & 53.105 & 44.029 & 0.720 & 0.815 & 0.359 & 0.257 & 0.118 & 0.138 & 0.029 & 0.145 \\ \hline \multicolumn{10}{c}{**Main effect—RM addition**} \\ \hline
**No** & 2700.25 & 2022.05 & 74.92 & 41.79 & 12.37 & 10.56 & 2.86 & 2.05 & 0.633 & 1.95 \\
**Yes** & 2605.70 & 1950.25 & 74.86 & 41.69 & 12.39 & 10.74 & 2.77 & 2.08 & 0.614 & 1.92 \\ \hline
**SEM** & 37.551 & 31.133 & 0.509 & 0.576 & 0.254 & 0.182 & 0.084 & 0.098 & 0.021 & 0.102 \\ \hline \multicolumn{10}{c}{**\(p\) value**} \\ \hline \multicolumn{10}{c}{SC—_Saccharomyces cerevisiae_, g/kg feed; RM—_Rhodlorula uncalieginos_ 0/0.3 kg/t of feed; \({}^{1}\) LW—final five weight; \({}^{2}\) CW—eviscerated carcass weight; \({}^{3}\) CY—carcass yield; Abd. Fat—abdominal fat; SEM—standard error mean; SC \(\times\) RM—interaction between the dietary supplements; \({}^{1}\)\(n=5/\)replicate per; \({}^{2}\) expressed in gram; \({}^{3}\) expressed as percentage of CW; \({}^{\text{a}\text{b}\text{c}}\) Columns means with different superscripts differ significantly at \(p<0.05\).  
\end{table} 
Table 3: Carcass yield and detailed body parts of broilers\({}^{1}\) diet supplemented with yeast.
# The Role of Fucoidan in Controlling _Listeria monocytogenes_ Biofilms on Seafood-Contact Surfaces
## 2 Materials and Methods
### Fucoidan Preparation and MIC Determination  
Fucoidan obtained from Sigma-Aldrich (St. Louis, MO, USA) and stock solution was prepared at a concentration of 1 mg/mL dissolved in sterile DW. A conventional microdilution technique, with previous reported methods, was used to determine the MIC of fucoidan against _L. monocytogenes_[25]. A variety of fucoidan concentrations were obtained in TSB by performing a two-fold serial dilution. A sterile 96-well microplate (Corning Incorporated, Corning, NY, USA) was filled with 100 \(\upmu\)L of bacterial suspension that had been adjusted to 10\({}^{5}\) log CFU/mL and 100 \(\upmu\)L of each diluted fucoidan solution (final concentrations: 0-300 \(\upmu\)g/mL). For the MIC experiment, this led to a final amount of 200 \(\upmu\)L/well.  
Microbial growth was measured using a microplate reader (Spectra Max 190, Sunnyvale, CA, USA) for measuring absorbance at 600 nm after the plate was incubated for 24 h at 30 \({}^{\circ}\)C. In order to verify the MIC, 100 \(\upmu\)L aliquots that showed no discernible microbial growth were cultured on Trypic Soy Agar (TSA) for colony enumeration (Thermo Scientific, Oxoid, Basingstoke, UK).  
Three duplicates of each experiment were carried out. A MIC of 150 \(\upmu\)g/mL was found for fucoidan. To assess the impact of fucoidan on the production of _L. monocytogenes_ biofilms at 0, 1/8, 1/4, and 1/2 MIC were used in the following tests.
# Fast Point Cloud Registration Method with Incorporation of RGB Image Information
## 1 Introduction
This method is computationally complex during feature extraction and is prone to erroneous matches. [PERSON] et al. proposed an improved ICP algorithm [19] by introducing RGB-D point cloud color information to assist in establishing accurate point correspondence and reducing the influence of noise and outliers on registration results, thus improving the accuracy of point matching in the improved ICP algorithm. This method requires point clouds to have color information. [PERSON] et al. also proposed an improved sample consensus point cloud registration algorithm [20]. By introducing a quadratic function of distance in FPFH, the weight of points in far-distance neighborhoods is reduced, while the weight of points in near-distance neighborhoods is increased to match corresponding points. This method makes the matching of corresponding point pairs more precise, but the efficiency is compromised. ICP and variants mentioned above are prone to dependence on the initial position of point cloud in point cloud registration, which can lead to poor registration results. In addition, the initial registration stage of these algorithms is computationally complex [21], and will lead to erroneous matches. In order to solve these problems, a point cloud registration method that incorporates RGB images is proposed. Firstly, the SIFT algorithm is used to extract feature points from the RGB image corresponding to the point cloud, and feature point matching is performed with bidirectional KNN. The random sample consensus (RANSAC) algorithm [22] is then applied to eliminate incorrectly matched point pairs. The initial transformation matrix is calculated using four matched pairs of points based on the transformation of the coordinate system between the laser scanning device and the coaxial visible camera. The initial alignment is performed on the point cloud that has been denoised with a pass-through filter [23] and transited down the voxel grid [24]. Finally, on the basis of the initial alignment, the FR-ICP algorithm [13] can be used for precise registration. Extracting feature points from point clouds can bring about complex computation. In this work, the complexity of initial alignment is reduced by extracting feature points from the RGB image corresponding to point cloud, and using the RANSAC algorithm to eliminate incorrectly matched points, thereby reducing the computational complexity and the rate of incorrect matching. Experimental research was conducted using a point cloud dataset of automotive parts collected in real scenes, and error measurements were performed on the registered point cloud data under different initial angles. Experimental comparison and analysis were conducted with ICP and FR-ICP, and the results showed that the proposed method has a registration error of only 0.487 mm. In the same group of point cloud experiments with comparable registration error, the proposed method showed an improvement in registration speed of up to 69%/48% compared to ICP/FR-ICP.
## 6 Connection-Aware Scheduling Heuristics
### Connection-Aware Reinforcement Learning Agent
#### 6.4.3 Environment Definition: States, Actions, and Rewards
As a result, any optimal policy \(\pi^{*}(a|s)\) will optimally distribute jobs according to our problem definition from Section 6.4.2. As a summary of this section, Figure 2 illustrates the training loop and how a learning agent interacts with our proposed environment. The agent assigns the current job to a particular device using its actions. In response, the dew environment returns the next state \(s_{t}\) and a reward \(r_{t}\). The state includes information about the current job to be assigned and the state of the devices. The reward \(r_{t}\) will be zero unless the agent has just distributed the last job. If that is the case, \(r_{t}\) will be equivalent to the successfully executed jobs. Since the agent tries to maximize the reward received from the environment, it will learn to distribute jobs to improve the performance of the dew environment.
## 5 WBAN and the Communications Protocol Stack
### MAC Layer  
Extensive research has been carried out on the adaptation of the MAC layer for WSNs to improve channel management and extend battery life. Although many of the schemes designed for WSNs focus on issues relating to distributed low power ad hoc networks, some have the potential to deliver improvements in WBAN applications. MAC layer research specific to WBANs is a relatively recent research area and is still an active topic for protocol designers. The transmission of RF signals is a major overhead for the energy efficiency of a WBAN. The MAC layer controls the channel access, packet encoding, addressing and as such must achieve maximum energy efficiency and data throughput by the efficient management of these functions. Studies reveal [33] that energy wastage in existing MAC protocols occurs from four major sources: _excessive collisions_, _control packet overheads_, _idle listening_, and _overhearing_. All contribute to network delay and energy inefficiency. WBAN applications require MAC protocols that are energy efficient and able to deliver battery lifetimes of several months or in the case of implanted nodes-years. MAC duty cycling needs to deliver energy efficient channel access to reduce packet collisions and reduce the impacts of idle listening and overhearing.
# A New DGNSS Positioning Infrastructure for Android Smartphones
## 3 DGNSS Positioning Infrastructure
### Implementation of the Infrastructure  
There are two approaches to implementing the DGNSS based on NMEA messages. One approach is to transmit directly the reference station data to smartphones, which is in turn integrated with the NMEA messages in the smartphones. The smartphone performs most of the calculations. Figure 2a illustrates this approach. It consists of some reference stations that provides the GNSS data to a smartphone in a specific protocol, e.g., Ntrip. In the smartphone, the reference station data and  
Figure 1: Flowchart of Differential Global Navigation Satellite System (DGNSS) based on NMEA messages.  
the NMEA messages are processed in the manner as specified in Figure 1. In this simple method, the reference station data are transmitted from the reference station directly, and only one-way communication is required. However, because of the limited computational capacities of smartphones, it is challenging to implement the DGNSS algorithm in the smartphone, including the range correction engine, the monitor engine, and the position engine. For example, the position correction calculation in (20) requires the multiplication and inversion operation of the matrix, and the matrix can have tens of rows in the case of multiple satellite constellations. Such a calculation can be challenging to perform using a smartphone.  
The other approach is to use a server collect GNSS data from reference stations, as shown in Figure 2b. When the smartphone requests the DGNSS service, it transmits the NMEA messages to the server. For each request, the server computes the DGNSS position using the NMEA messages from the smartphone. The DGNSS position is then transmitted to the smartphone. In this method, the server performs most of the calculations in the architecture. The smartphone in this method must transmit the NMEA messages to the server and must receive the DGNSS position from the server. Therefore, two-way communication is required for the smartphone. The first advantage of this method is that most of the calculations are performed by the DGNSS server efficiently, i.e., the smartphone does not have to consider these calculations. The second advantage is that the range correction engine and the monitor engine are common parts for all smartphones that request the DGNSS service. These two engines can be performed simultaneously at the server, and, in turn, the range corrections are utilized by all smartphones that are connected to the server.  
To reduce the user's computational requirement, we developed the DGNSS infrastructure based on the second method. The infrastructure consists of a server program responsible for providing DGNSS service, and a Software Development Kit (SDK) capable of sending the NMEA messages to the server and receiving the DGNSS positions from the server. Both programs can be configured with different settings, such as the IP address and the port number.  
Figure 2: (**a**) use of a smartphone to calculate the DGNSS positions; (**b**) use of a server to calculate the DGNSS positions.
## 5 Numerical Examples
### Model Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **LSE’s** & **MSE** & **AIC** & **PRR** & **PP** & **SAE** & \(\mathbf{R^{2}}\) \\ \hline GOM & \(\hat{\mathrm{a}}=133.835\), \(\hat{\mathrm{b}}=0.000146\) & 8.620 & 86.136 & 0.556 & 0.242 & 42.166 & 0.991 \\ DSM & \(\hat{\mathrm{a}}=101.918\), \(\hat{\mathrm{b}}=0.000507\) & 45.783 & 117.316 & 22.692 & 1.318 & 101.659 & 0.951 \\ ISM & \(\hat{\mathrm{a}}=133.835\), \(\hat{\mathrm{b}}=0.000146\) & 9.127 & 88.136 & 0.556 & 0.242 & 42.166 & 0.991 \\  & \(\hat{\mathrm{g}}=0.000001\) & & & & & & \\ YIDM & \(\hat{\mathrm{a}}=130.091\), \(\hat{\mathrm{b}}=0.00015\) & 9.084 & 88.267 & 0.561 & 0.243 & 42.052 & 0.991 \\  & \(\hat{\mathrm{a}}=0.000003\) & 9.532 & 90.326 & 0.530 & 0.234 & 41.538 & 0.991 \\ PNZM & \(\hat{\mathrm{a}}=122.259\), \(\hat{\mathrm{a}}=0.0002\) & & & & & & \\ PZM & \(\hat{\mathrm{a}}=9955.597\), \(\hat{\mathrm{g}}=0.305\) & 11.491 & 92.020 & 0.643 & 0.268 & 44.848 & 0.990 \\  & \(\hat{\mathrm{c}}=0.569\) & & & & & & \\ DPM & \(\hat{\mathrm{a}}=123.193\), \(\hat{\mathrm{y}}=0.0001\) & 156.480 & 212.867 & 0.917 & 2.879 & 196.360 & 0.851 \\  & \(\hat{\mathrm{t}}_{0}=0.0001\), \(\hat{\mathrm{m}}=38.459\) & & & & & & \\  & \(\hat{\mathrm{a}}=0.00013\), \(\hat{\mathrm{b}}=0.78\), & & & & & & \\ TCM & \(\hat{\mathrm{a}}=141.399\), \(\hat{\mathrm{g}}=54.71\), & 7.090 & 90.758 & 0.091 & 0.068 & 37.880 & 0.9937 \\  & \(\hat{\mathrm{N}}=254.707\) & & & & & & \\  & \(\hat{\mathrm{a}}=0.016\), \(\hat{\mathrm{b}}=0.07\) & & & & & & \\
3 PFDM & \(\hat{\mathrm{c}}=0.00001\), \(\hat{\mathrm{g}}=157.458\) & 9.410 & 92.360 & 0.420 & 0.200 & 39.909 & 0.992 \\  & \(\hat{\mathrm{N}}=205.025\) & & & & & & \\ New & \(\hat{\mathrm{a}}=0.064\), \(\hat{\mathrm{a}}=0.731\), & & & & & & \\ Model & \(\hat{\mathrm{g}}=2509.898\), \(\hat{\mathrm{N}}=337.765\) & 6.336 & 88.885 & 0.086 & 0.066 & 36.250 & 0.9940 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Model parameter estimation and comparison criteria from MRS data set (DS3). Figure 2: Mean value function of the ten models for DS1. Figure 4: Mean value function of the ten models for DS3. Figure 3: Mean value function of the ten models for DS2. Figure 5: 95% confidence limits of the newly proposed model for DS1. Figure 6: 95% confidence limits of the newly proposed model for DS2. Figure 8: Relative error of the ten models for DS1. Figure 10: Relative error of the ten models for DS3. Figure 7: 95% confidence limits of the newly proposed model for DS3. Figure 9: Relative error of the ten models for DS2.