esa-sceva/satcom-chunk-collection huggingface dataset:
# A Network Coding Based Hybrid ARQ Protocol for Underwater Acoustic Sensor Networks
## 2 Network and Channel Model
### Network Model  
The network model of underwater acoustic sensor networks is illustrated in Figure 1. We consider a deep water environment for data transfer here. The source sensor node collects information in the underwater environment and sends data packets to the sink node hop by hop. Several relay nodes are distributed randomly between source node and sink node and anchored to the ocean bottom. There exists a main multi-hop path and data packets are transmitted mainly through this path. The path can be obtained by a suitable routing process and the routing process is not the concerned issue in this paper. Each node broadcasts its encoded packets to the whole network. All the receiving nodes can not only receive packets from its adjacent main path node, but also overheard packets from all the nodes in the network. We mainly focus on the reliable data transfer protocol on these paths. In addition, as most of the underwater sensors can not have a fixed position in the moving underwater scene, we suppose the nodes can appear at any position within its mobility range (the dashed circle with radius \(R\)) in our network model. The total number of nodes is \(N+1\). Data packets are generated and encoded at node 0 (source node) and transmitted to the destination node \(N\) (sink) through \(N-1\) relay nodes. The distance between node \(i\) and node \(j\) is defined as \(d_{i,j}\). Then, the following condition holds for all nodes in the network:  
\[d_{i,j}>d_{i,i+1}\ \,\ \ for\ all\ \ |i-j|>1. \tag{1}\]  
Equation (1) means that for node \(i\), the adjacent relay nodes in the main multi-hop path always have less distance than the nodes in the overhearing path and thus have a better channel condition. This condition is always true for a suitable routing protocol.
## 4 IoT Integration with Digital Twin
### IoT Devices and Data Collection
The data collected by these IoT devices is sent, using the MQTT protocol, to a MQTT broker such as Mosquito into. The efficiency of this lightweight messaging system ensures the real-time transfer of data. The sensor information formatted in JSON format has a unique identifier for each sensor. This standardized format ensures easy integration and accurate handling of various types of data, such as temperature, humidity, and energy consumption. For a detailed representation of this data flow and integration process, the data collected might be locally stored or transmitted to a cloud server for further analysis. Edge computing performs preliminary data processing effectively at the source. In general, data analysis on the edge optimizes resources, improves transmission speed, and accelerates decision-making. This approach further supports sophisticated data management with accurate analytics. The flow of the IoT system, as illustrated in Figure 4, begins with the ICON, which serves as the central controller for connections and interactions throughout the system. From this node, it connects to specific modules for a reliable exchange of information. These \begin{table}
\begin{tabular}{l l l} \hline
**Device Type** & **Function** & **Data Collected** \\ \hline ICON & Manages connections & Aggregated sensor data \\ \hline Communication Module & Facilitates data exchange & Signal communication \\ \hline Device Management Interface & Configures and controls & Device status and configuration \\ \hline Sensors & Measure various conditions & Temperature, humidity, CO\({}_{2}\), motion, light levels, energy consumption \\ \hline Appliance Controller & Manages appliance operation & Appliance status \\ \hline MQTT Broker & Manages data flow & Real-time data handling \\ \hline \end{tabular}
\end{table} 
Table 2: Devices and sensors in an IoT system. modules, sensors, and appliance controllers communicate with each other using low-power protocols designed for energy conservation and scalability. The proposed network has a mesh topology, together with a star network topology. The mesh network allows for improved redundancy because there are more paths that data can travel to ensure that communication is good in case something like a device or a link goes down. The star network simplifies routing by connecting directly with the ICON hub, thereby reducing latency, especially for those devices that are further away. This will ensure robust and efficient communication across the network.
## 4 Results and Discussion  
### Blood Vessel Model  
After performing the ratio calibrations, [PERSON]'s error-grid analysis (EGA) [20; 21] and Bland-Altman analysis plots were used for the performance analysis of the estimated \(HbA1c\) values. As seen in Figure 4, from the EGA, Zone A contained 15 samples (75%; clinically accurate), Zone B contained 5 samples (25%; data outside of 20% of the reference but would not lead to inappropriate treatment), and Zone C contained 0 samples (0%; data that would lead to inappropriate treatment). The Bland-Altman analysis indicated that the blood vessel model provided a bias of \(-0.15998\pm 0.961\), and the limits of agreement ranged from \(-1.12\) to \(0.80\). For the estimated %\(HbA1c\) values, statistical analysis using mean square error (MSE), mean error (ME), mean absolute deviation (MAD), root mean square error (RMSE), and Pearson's r yielded \(0.266\), \(-0.1599\), \(0.423\), \(0.5156\), and \(0.8959\), respectively.  
We considered value (\(HbA1c\)) calibrations in addition to ratio calibrations, and the EGA and Bland-Altman analysis results are shown in Figure 5. From the EGA, Zone A contained 17 samples (85%), Zone B contained 3 samples (15%), and Zone C contained 0 samples (0%). The Bland-Altman analysis indicated that the blood vessel model provided a bias of \(-0.029\pm 0.8598\), and the limits of agreement ranged from \(-0.89\) to \(0.83\). For the estimated %\(HbA1c\) values, statistical analysis using MSE, ME, MAD, RMSE, and Pearson's r yielded \(0.259\), \(-0.0118\), \(0.4366\), \(0.5087\), and \(0.8873\), respectively.  
Figure 4: EGA and Bland–Altman analysis for the \(HbA1c\) estimation with the blood vessel model (considering ratio calibrations only).
## 4 Dataset Acquisition
### Transformers  
Models based on Transformers, derived from neural networks, have shown promising results in tasks related to natural language processing (NLP), especially in generating captions for images, also known as image-to-text [23; 55; 35]. The preparation of these models involves a data preprocessing phase, in which images are converted into numerical representations, typically using CNNs, while captions are encoded into numerical vectors [55]. Figure 2 illustrates the architecture of the Transformer, as well as the process of extracting features from images by CNNs, which serve as inputs for the Transformer to generate reports automatically.  
After training, the model becomes capable of generating captions for new images by encoding the image through the same CNN used during training. Subsequently, the image representation is combined with the textual representation generated by the Transformer model, resulting in the production of the final report from the embeddings, using the Softmax layer to calculate the probabilities [23; 55]. Recently, encoder-decoder models employing Transformers have been applied in medical report generation, introducing a relational memory mechanism that aids in retaining text patterns present in similar examination reports [23].  
The encoder, part of the Transformer architecture, plays a crucial role in processing input sequences in various natural language processing tasks. Comprising layers of self-attention followed by dense neural networks, each layer uses multi-head self-attention mechanisms to capture relationships between different words in the input sequence, employing residual connections and layer normalization to ensure training stability. Its output provides a contextualized representation of the input sequence, vital for subsequent tasks [55]. The architecture of the encoder is shown in Figure 2.  
On the other hand, the Transformer decoder is enhanced with a mechanism designed to aid in remembering text patterns present in medical reports, such as \"bones with normal texture and density\" in Polydactyly examination [55]. This mechanism, called relational memory, utilizes input and forget gates, resembling those of a long short-term memory (LSTM) cell. This pioneering approach in using Transformers for medical reports has shown promising results [23]. The architecture of the decoder is depicted in Figure 2.  
For this study, we configured the encoder with 8 heads (HEADs) and the decoder with 1024 units (UNITs) and 12 heads (HEADs), based on empirical lessons learned from previous investigations by our team. Opting for a larger number of units and heads in the decoder is a conventional practice to handle the complexity and diversity of linguistic relationships in text-generation tasks. Meanwhile, the moderate number of heads in the encoder provides a balance between computational efficiency and modeling capacity, an approach that has proven suitable for solving the problem at hand [56].
# A Bird Vocalization Classification Method Based on Bidirectional FBank with Enhanced Robustness
###### Abstract  
Recent advances in audio signal processing and pattern recognition have made the classification of bird vocalization a focus of bioacoustic research. However, the accurate classification of birdsongs is challenged by environmental noise and the limitations of traditional feature extraction methods. This study proposes the iWAVE-BiFBank method, an innovative approach combining improved wavelet adaptive denoising (iWAVE) and a bidirectional Mel-filter bank (BiFBank) for effective birdsong classification with enhanced robustness. The iWAVE method achieves adaptive optimization using the autocorrelation coefficient and peak-sum-ratio (PSR), overcoming the manual adjustments required with and incompleteness of traditional methods. BiFBank combines FBank and inverse FBank (iFBank) to enhance feature representation. This fusion addresses the shortcomings of FBank and introduces novel transformation methods and filter designs to iFBank, with a focus on high-frequency components. The iWAVE-BiFBank method creates a robust feature set, which can effectively reduce the noise of audio signals and capture both low- and high-frequency information. Experiments were conducted on a dataset of 16 species of birds, and the proposed method was verified with a random forest (RF) classifier. The results show that iWAVE-BiFBank achieves an accuracy of 94.00%, with other indicators, including the F1 score, exceeding 93.00%, outperforming all other tested methods. Overall, the proposed method effectively reduces audio noise, comprehensively captures the characteristics of bird vocalization, and provides improved classification performance.  
bird vocalization classification; wavelet threshold denoising; feature extraction; BiFBank +
Footnote †: journal: Applied sciences
## 3 Adaptive Field-of-View Visible Light Communication: Concept and Prototype Development
### Experimental Results
#### 4.2.2 Experimental Results Showing the Benefits of the Adaptive FoV VLC Receiver
Receiver 2 continued to experience link loss, but what is important is that receiver 1 managed to have a BER of less than \(10^{-7}\) with an FoV of \(\pm 7^{\circ}\). Finally, around 5 p.m., in the final phase before sunset, the shadow from a tree lowered the measured irradiance and receiver 2 regained its link, but the BER was still higher than that seen at receiver 1. Due to the lower irradiance value, the adjustable mechanism gradually widened the FoV up to \(\pm 22^{\circ}\) at around 6 p.m., when the sun exited the FoV of the receivers, and so the BER of receiver 2 also descends to values less than \(10^{-7}\).
# Recurrent Neural Network-Based Hybrid Localization for Worker Tracking in an Offshore Environment
## 3 Materials and Methods
### Structure of RHLS
The RNN positioning module is initialized when Wi-Fi scanning is completed on the smartphone. At this time, the strength of the scanned AP and the signals received from the AP are arranged in a predetermined order. The vector, the value measured by the geomagnetic sensor, and the moving distance and direction calculated by the PDR module are input to the recurrent network positioning module. When input is made to the positioning module, the resulting value in metric coordinates is output, as explained in Section 3.4.3. If a step is recognized in the PDR module before the next Wi-Fi scan is performed, the process of passing the geomagnetic and PDR module values as inputs to the positioning module and deriving the coordinates is repeated.
# The Heat Shock Protein 60 and Pap1 Participate in the _Sporothrix schenckii_-Host Interaction
## 2 Materials and Methods
### Infection Assays in Galleria mellonella  
The role of both Hsp60 and Pap1 in fungal virulence and the ability of recombinant proteins to prevent sporotrichosis was assessed in an experimental model of invasive sporotrichosis in the alternative host _G. mellonella_[56]. The use of animals in this study was approved by the Ethics Committee of Universidad de Guanajuato (permission CBIUG-P12-2018).  
Larvae were obtained from an in-house colony [60] and fed ad libitum with an already described diet based on corn bran and bee honey [7], and only those without evident body melanization and size between 1.2 and 1.5 cm were included in the study [7; 60]. Fungal cells, in a concentration of 1 \(\times\) 10\({}^{5}\) yeast-like cells 10 \(\upmu\)L\({}^{-1}\), were injected with a Hamilton syringe equipped with a 26-gauge needle in the last left pro-leg, which was previously sanitized with 70% (\(v/v\)) ethanol, and animals were housed in Petri dishes and kept at 37 \({}^{\circ}\)C for 2 weeks [60]. The animal survival was monitored daily, and animal dehydration was avoided by adding chopped apple to the animal housing [7], while silk was removed daily to delay the transition to pupa. Animal death was defined as lack of irritability and extensive body melanization as previously defined [7; 56; 60]. Each animal group contained 30 larvae and were injected with 10 \(\upmu\)L of PBS containing 10 \(\upmu\)g, 20 \(\upmu\)g, 40 \(\upmu\)g, 80 \(\upmu\)g, or 160 \(\upmu\)g of recombinant protein. Larvae were incubated 5 days at 37 \({}^{\circ}\)C, then challenged with 1 \(\times\) 10\({}^{5}\) yeast-like cells, and monitored daily for 2 weeks. As controls, one animal group was injected only with PBS or only with the recombinant proteins.  
When indicated, 1 \(\times\) 10\({}^{5}\) yeast-like cells were pre-incubated with a working dilution of 1:2000 of either polyclonal antibodies against rHsp60 or rPap1 for 60 min at 37 \({}^{\circ}\)C; cells were washed twice with PBS and used for animal inoculation. As controls, fungal cells were incubated with a working dilution of 1:2000 of the preimmune sera before inoculation to larvae. Moreover, animal groups were inoculated with 10 \(\upmu\)L of the 1:2000 dilution of preimmune sera or the polyclonal antibodies against rHsp60 or rPap1.
# An Improved Forwarding of Diverse Events with Mobile Sinks in Underwater Wireless Sensor Networks
## 2 Background and Related Work
### Localization Based Routing Protocols
In Depth Adaptive Routing Protocol (DARP) [25] authors considered the fact that speed of sound varies with depth and proposed shortest delay path for deep sea (about 8000 m) underwater environments. Relative Distance Based Forwarding (RDBF) is proposed in [26]. In RDBF, fitness factor is normalized by transmission range to get the value in range of [0, 1]. Holding time is also dependent on transmission range and speed of sound in acoustic environment. Multi Path Routing (MPT) proposed in [27] ensures balance between energy consumption and end to end delay for time critical underwater applications. Its a cross layer approach which employs: \((1)\) power control on physical layer according to channel status and network conditions; \((2)\) packet combining mechanism on sink for multiple copies of corrupted packets received from different paths. MPT perform well in dense deployment scenarios while suffers void regions in sparse networks. All the above mentioned localization based protocol requires some collaborative mechanism for to find the location of the deployed nodes. Localization process in harsh underwater environment is itself a research challenge. It also requires some certain number of node to act as Anchor Nodes (AN). Anchor nodes are attached with some support and their fixed location is known. Unknown location nodes communicate with more than one anchor nodes and try to estimate their own location. Error in localization can leads to erroneous communication. Hence its preferred to have localization free routing protocol for UWSN.
# Numerical Evaluation of Sample Gathering Solutions for Mobile Robots
## 5 Numerical Evaluation and Comparative Analysis
### Numerical Experiments  
All the simulations to be presented were implemented in MATLAB [48] and were performed on a computer with Intel i7 quad-core processor and 8 GB RAM.  
The numerical experiments were run for almost 15 days, and they were organized by considering the following aspects:  
1. Time complexity orders cannot be a priori given for MILP or for non-convex QP optimization problems. Thus, the time for obtaining a planning solution solving Problem 1 is to be recorded as an important comparison criterion.
2. The complexity of MILP, QP, and IH solutions does not directly depend on the size of environment graph \(G\), except for the initial computation of map \(\omega\) that further embeds the necessary information from the environment structure. Therefore, the running time of either solution is influenced by two parameters: the number of robots (\(N_{R}\)) and the number of samples (\(N_{S}\)).
3. Based on item (ii), we consider variation ranges \((N_{R},N_{S})\in[2,\ldots,10]\times[2,\ldots,50]\), with unit increment steps for \(N_{R}\) and \(N_{S}\). Please note that the cases of 1 robot and/or 1 sample are trivial, and therefore are not included in the above parameter intervals.
4. To obtain reliable results for item (i), for each pair \((N_{R},N_{S})\) we have run a set of 50 trials. For each trial we generated a random distribution of samples in a 50-node graph. To maintain focus on time complexity, we assumed sufficiently large amounts of robot energy \(\mathcal{E}\), such that Problem 1 is not infeasible due to these limitations.
5. For each trial, the MILP optimization was deemed _failed_ whenever (4) did not return a solution in less than 1 min. This is because in multiple situations we observed that if no solution is obtained in less than 30-40 s, then the MILP (4) does not finish even after 2-3 h.
6. For each trial from item (iv), the QP solution was deemed _failed_ whenever it yielded non-binary outcomes of map \(x\) (see Remark 3). The IH solution is always _successful_.
7. For each proposed solution, for each pair \((N_{R},N_{S})\) from (iii) and based on trials from (iv), we computed the following comparison criteria: * _success rate_, showing the percentage of trials when the solution succeeded in outputting feasible plans; * _computation time_, averaged over the successful trials of a given \((N_{R},N_{S})\) instance; * _solution cost_, i.e., time for gathering all samples, for each successful situation.
## 4 Experimental Analysis
### Datasets  
We test the proposed distributed ESN algorithm on four datasets, which are related to non-linear system identification, time-series prediction and image recognition. Three datasets of four are standard artificial benchmarks, and the other is sensor-monitoring data from the real world. A brief description of the four datasets is provided below.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & CPU & Xeon E5-2609 v2, 2.5 GHz, 4 cores \(\times\) 2 \\
**Hardware** & Memory & 16 GB \\  & Network & 100 MB/s \\ \hline  & Operating System & CentOS-6.5 \\  & Spark & 1.6.1 \\
**Software** & Hadoop & 2.2.0 \\  & ZooKeeper & 3.4.5 \\  & Java Development Kit & 1.7.0\_60 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Software and hardware configuration.  
Figure 4: Network topology of our platform. As a stretchable platform, the number of workers is variable on demand.  
Figure 5: Monitoring interface of our platform.  
(1) NARMA-10 dataset. It is a non-linear system identification application, where the input \(x[n]\) is white noise distributed in the interval [0, 0.5], and the output \(d[n]\) is computed from the following equation [54]:  
\[d[n+1]=0.3d[n]+0.05d[n]\left[\sum_{i=0}^{9}d[n-i]\right]+1.5d[n-9]d[n]+0.1 \tag{16}\]  
Then, the output is squashed into [\(-1\), \(+1\)] by the \(\tanh(\cdot)\) transformation:  
\[d[n+1]=\tanh(d[n+1]-\hat{d}) \tag{17}\]  
where \(\hat{d}\) is the empirical mean of the overall output.  
(2) The second dataset is a three-dimensional time-series prediction task based on the Lorenz attractor, defined by the following differential equations [39]:  
\[\left\{\begin{array}{l}x_{1}[n+1]=\sigma(x_{2}[n]-x_{1}[n])\\ x_{2}[n+1]=x_{1}[n](\eta-x_{3}[n])-x_{2}[n]\\ x_{3}[n+1]=x_{1}[n]x_{2}[n]-\zeta x_{3}[n]\end{array}\right. \tag{18}\]  
where \(\sigma=10\), \(\eta=28\) and \(\zeta=8/3\) (the default settings). The task in Equation (18) is sampled every second, and the input vector is \(\left[\begin{array}{cc}x_{1}[n]&x_{2}[n]&x_{3}[n]\\ \end{array}\right]\), while the required output is a one-step-ahead prediction of the variable \(x_{1}\), namely:  
\[d[n]=x_{1}[n+1] \tag{19}\]  
(3) The third dataset is real-time monitoring data of smart meters in an electric power system [55]. Concretely, it is real-world electric data from the circuits of one resident home, and it is sampled every second.  
(4) Nowadays, unstructured data such as images and videos are accumulated rapidly in Big Data. It is necessary to inspect the performance of the proposed framework when the input is an image, namely the input is high-dimensional data (supposing each pixel in the image is regarded as a dimension). We choose the Mixed National Institute of Standards and Technology (MNIST) dataset [56], the popular database of handwritten digits, as the fourth dataset.  
Additionally, as a standard practice in ESN implementations, we supplemented the original input with an additional constant unitary input to all four datasets [53].
## 2 Modeling and the Proposed Method
### Estimation of Attitude Angle
#### 2.2.2 The Size of the Target Is Unknown  
When the size of the target is unknown. It is known that \(\left|AB\right|\) depends on the side of the cube, which means that \(\left|AB\right|\) becomes an unknown as well. For synthetic aperture ladar (SAL), one can obtain two TF spectra under two different LOSs. By utilizing the two TF spectra together, the two unknown (\(\left|AB\right|\) and \(\alpha\)) problems can be resolved. We call it the double edge ratio method (DERM), which is as follows: For a given LOS angle \(\alpha\), one has the maximum Doppler frequency shift is  
\[|f_{1}|=\frac{4}{\lambda}|AB|\pi f_{s}sin\alpha \tag{18}\]  
For another LOS angle (\(\alpha+\Delta\)), the corresponding maximum of Doppler frequency is given by  
\[|f_{2}|=\frac{4}{\lambda}|AB|\pi f_{s}sin(\alpha+\Delta) \tag{19}\]  
Combining Equation (18) with Equation (19), one can easily obtain  
\[|f_{2}|sin\alpha-|f_{1}|sin(\alpha+\Delta)=0 \tag{20}\]  
Then, \(\alpha\) can be solved as  
\[\alpha=-arctan\bigg{(}\frac{|f_{1}|sin\Delta}{|f_{1}|cos\Delta-|f_{2}|}\bigg{)} \tag{21}\]  
By utilizing two TF spectra from different LOS angles and the double edge ratio method (DERM), we can obtain the attitude angle \(\alpha\). According to the relationship between the attitude angle and the radius of gyration (see Equation (21)), the radius of gyration \(\rho\) can be acquired as  
\[\rho=\frac{|f_{max}|\lambda}{4\pi f_{s}sin\alpha} \tag{22}\]  
For the cube-shaped target, the lengths of the sides are equal. Thereby, we can furthermore obtain the length of the side as \(L=\sqrt{2}\rho\).