esa-sceva/satcom-chunk-collection huggingface dataset:
# Weighted-CAPIC Caching Algorithm for Priority Traffic in Named Data Network
###### Abstract  
Today, the internet requires many additional mechanisms or protocols to support various ever-growing applications. As a future internet architecture candidate, the Named Data Network (NDN) offers a solution that naturally fulfills this need. One of the critical components in NDN is cache. Caching in NDN solves bandwidth usage, server load, and service time. Some research about caching has been conducted, but improvements can be made. In this research, we derived the utility function of multiscan content to obtain the relationship between the class's weight and cache hit ratio. Then, we formulated it into the Weighted-CAPIC caching algorithm. Our research shows that Weighted-CAPIC provides a higher cache hit ratio for the priority class and the whole system. This performance is supported while the algorithm still provides the same path-stretch value as Dynamic-CAPIC. The Weighted-CAPIC is suitable to used in mobile nodes due to its ability to work individually without having to coordinate with other nodes.  
Named Data Network; cache; weight; class; priority +
Footnote †: journal:  
00 2022 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# Contrastive Refinement for Dense Retrieval Inference in the Open-Domain Question Answering Task
## 3 Results
### Experimental Results
#### 3.2.2 Retriever Performance
Therefore, after the same computation, the AOpt model will update query representation more thoroughly over a larger range, which explains why query representation can show relatively large improvements and steeper trends in retrieval recall. However, due to the relatively longer text length of passage sequences, it is more difficult to achieve precisely encoded text, and they often lack some semantic information. Therefore, passage representation can absorb additional relevant semantic information from relevant and irrelevant query representations during the AOpt optimization process while still retaining most of its original \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**NQ**} & \multicolumn{4}{c}{**TriviaQA**} \\ \hline
**Model** & **Top1** & **Top5** & **Top20** & **Top100** & **Top1** & **Top5** & **Top20** & **Top100** \\ \hline BM25 & - & - & 59.1 & 73.7 & - & - & 66.9 & 76.7 \\ DPR(single 1) & - & - & 78.4 & 85.4 & - & - & 79.4 & 85.0 \\ DPR(multi 2) & - & - & 79.4 & 86.0 & - & - & 78.8 & 84.7 \\ Hybrid 3(single) & - & - & 76.6 & 83.8 & - & - & 79.8 & 84.5 \\ Hybrid(multi) & - & - & 78.0 & 83.9 & - & - & 79.9 & 84.4 \\ GAR & - & 60.9 & 74.4 & 85.3 & - & 73.1 & 80.4 & 85.7 \\ ANCE(single) & - & - & 81.9 & 87.5 & - & - & 80.3 & 85.3 \\ ANCE(multi) & - & - & 82.1 & 87.9 & - & - & 80.3 & 85.2 \\ PAIR & - & 74.9 & 83.5 & 89.1 & - & - & - & - \\ \hline DPR * & 45.7 & 68.3 & - & - & 47.2 & 72.7 & - & - \\ Weighted-based & 51.6 & 69.6 & 78.2 & 84.3 & - & - & - & - \\ (\(\beta=0.6\), \(\gamma=-0.1\)) & & & & & & & & \\ DPR + AOpt(query) & 52.9 & 75.1 & 82.1 & 86.2 & 55.4 & 80.3 & 83.3 & 86.0 \\ DPR + AOpt(passage) & 51.3 & 72.1 & 80.4 & 85.9 & 53.0 & 77.3 & 81.1 & 85.6 \\ DPR + AOpt(hybrid) 4 & 57.1 & 74.5 & 81.2 & 85.4 & 59.3 & 79.9 & 82.3 & 84.9 \\ \hline \hline \end{tabular}  
* trained on an individual training dataset. 2 trained on combined training datasets (all except SQuAD). 3 retrieval with both BM25 and DPR. 4 text representation optimization with both AOpt(query) and AOpt(passage).
* our replication result under DPR [1] experimental settings.  
\end{table} 
Table 3: Retrieval of Top-k Results. encoding information. In other words, if query representations are continually updated, similar to passage representations, the original information in query representations will be completely covered, thereby losing its own representational ability. Therefore, for query representation optimization, we need to determine the best training rounds and stop optimizing query representation in advance.
# A Novel Method of Seismic Signal Detection Using Waveform Features
## 3 Signal Model
### Establish a Signal Model  
After extracting the characteristics of historic seismic events, the next question is how to use these features to build a model. For each signal, the corresponding feature elements are available from the time domain and the frequency domain. Using the Gaussian process, we can obtain every feature distribution. We assume that all waveform features are independent. Then the joint probability model is as follows,  
\[p(s_{i}|E)=p(\alpha_{i}|E)p(\rho_{i}|E)p(\text{log}\gamma_{i}|E)p(\text{log} \lambda_{i}|E). \tag{16}\]In the Equation (16), \(p(s_{i}|E)\) is the occurring probability value of a seismic signal in the specific area. We use \(p(\bullet|E)\) to denote the probability value for every feature. We adopt \(\log(\bullet)\) to indicate logarithmic normalization of data.  
In the following, take the amplitude feature as an example to introduce the modeling process, and similarly for other features. For a specific phase of a particular array, by observing a large number of historical event waveforms, the observation can be expressed as the sum of an unknown function about the event origin \(f_{j}^{(k)}(e)\) and a noise term \(\epsilon\) in Equation (17). Through the GP, the model of the amplitude characteristics is established as Equation (18),  
\[f_{\alpha}=f(e_{i})+\epsilon \tag{17}\]  
\[f_{\alpha_{i}}\sim GP(\mu_{\alpha,j}^{k},k_{\alpha,j}^{k}) \tag{18}\]  
According to the Equation (18), the occurrence probability of the amplitude about a new observation signal \(s_{i}\) can be predicted.  
\[p(\alpha_{i}|E)=\mathrm{N}(\tilde{f}_{*}(E),\Sigma_{*}^{f}(E)) \tag{19}\]  
in the Equation (19), \(\tilde{f}_{*}\) is mean and \(\Sigma_{*}^{f}\) is covariance of the test point computed separately by Equations (7) and (8).  
Figure 1: The signal and noise detected by short-term and long-term power averages (STA/LTA). The subgraph (**a**) shows the original signal, filtered signal, and the STA/LTA trace from top to bottom. Correspondingly, the subgraph (**b**) is the detection image of noise.
# Smart Low-Cost Control System for Fish Farm Facilities
## 4 Proposal for Smart Low-Cost Control Systems for Aquaculture
### Proposal for Smart Low-Cost Control Systems for Aquaculture Facilities with Cages in the Sea
#### 4.1.3 Nodes  
In order to develop the nodes, it is possible to opt for different options, i.e., from simple and cheap systems and modules, such as WeMos D1 module based on the esp8266 transceiver, to somewhat more expensive options but with greater processing capacities, such as the family of Raspberry Pi devices. In this case, the development of the node using the Raspberry Pi 4 module (Oxford, UK) is proposed. Information from the sensors is collected, analyzed, and transmitted to the DB. Nodes are endowed with edge computing capacities not only for improving data management but also to have the option of triggering actuators if needed.  
Raspberry Pi is a low-cost, compact format computer designed to make computing accessible to all users. All Raspberry Pi designs are based on free hardware and free operating systems based on GNU/Linux are commonly used. The main features of this model are its Broadcom BCM2711 (Cortex-A72) processor with four cores and 1.5 GHz and its IEEE 802.11 ac/Bluetooth 5.0 wireless connectivity.  
The GPIO pins on the Raspberry Pi 4 board give the SBC board capabilities that Arduino might have, but controlled from within the operating system using code in different languages, such as Python. Raspberry Pi Model A+, B+, 2B, 3B, 3B+, Zero and Zero W, and 4 have a 40-pin GPIO header. Unlike digital signals, an analog signal can take intermediate values between a maximum and a minimum. An analog-to-digital converter such as the MCP3008 (Microchip Technology, Tokyo, Japan) must be used to read this voltage on the Raspberry Pi. However, that converter does not specify values in volts; it provides a number between 0 and 1023, which corresponds to a 10-bit ADC converter.  
With a Raspberry, we can connect a considerable number of sensors, such as those mentioned in Figure 12, and we can complement their connectivity functions by adding gateways of the technologies that we wish to implement.  
Finally, it is interesting to consider the possibility of using Power over Ethernet (PoE) to power the entire system, as well as the use of alternative energy sources, such as a solar panel and a set of batteries, to ensure its correct operation. With all this, the node would be ready for its integration into a complex network.
## 4 Agent Training with Markov Decision Process (MDP)
### DRL-Based Solution
#### 4.2.1 Network Architecture  
Given that the state \(S(t)\) in our proposed MDP model comprises nine key environmental parameters (including a four-dimensional satellite state: ephemeris age, azimuthangle, elevation angle, and satellite-ground distance; two-dimensional epoch state: satellite identifier and cumulative observation count; two-dimensional signal state: SNR and Doppler frequency; and one-dimensional positioning state: GDOP), we represent it as a nine-dimensional feature vector.  
We employ a multi-layer perceptron (MLP) as the approximator for the Q-value function, with identical architectures for both the online network and target network. Below, we define the structure of this MLP.  
First, the input layer consists of nine neurons, matching the dimensionality of the state vector. Next, the network includes two hidden layers: the first is a fully connected (FC) layer with \(H_{1}\) neurons, activated by the rectified linear unit (ReLU), and the second is another FC layer with \(H_{2}\) neurons, also using ReLU activation. Finally, the output layer is an FC layer with 20 neurons, each corresponding to a discrete action, and if no activation function is applied, it allows direct Q-value estimation.  
In evaluation mode, the agent selects the action associated with the highest Q-value in the output layer. In training mode, the agent adopts the \(\varepsilon-\)greedy policy (see Section 4.2.3 for details) to determine actions.
# Impacts of Diversity Technique Application in Order to Reduce the Fading in IEEE 802.15.4 Networks
## 6 Conclusions  
The paper presents analytical expressions for the BER of the received signal in the IEEE 802.15.4 network operating under the impact of SC and MRC diversity in the presence of Rayleigh and Nakagami-\(m\) fading, as well as AWGN and interference. Numerical results in the form of graphs are presented. Based on the obtained results, it can be concluded that the BER values decrease with the increasing parameter \(\gamma_{0}\) and that the Rayleigh fading channel has a better performance than the Nakagami-\(m\) fading channel. The application of SC and MRC combiners has the effect of reducing BER and improving system performance. The use of MRC combiners gives a better performance than the use of SC combiners. The use of MRC combiners gives lower BER values, with better values being significantly more pronounced at lower values of the parameter \(\gamma_{0}\) and Rayleigh fading channels. Increasing the fading parameter \(m\) leads to better system performance. There are no drastic performance improvements when using combiners compared to not using combiners. However, due to the specific operation of WSNs in extremely different climatic environments, any improvement is welcome. Systems with combiners are very popular in increasingly common hybrid RF/FSO systems, so the analyzed model is very adaptable for hybrid systems.  
Based on the obtained results, the behavior of different implementations of the IEEE 802.15.4 network can be predicted for different values of network parameters and in different propagation environments. This enables the designers of mobile transmission systems to create rational systematic solutions for the desired system performance.  
The results presented in this paper can be the basis for future research on the impact of diversity techniques in order to reduce the fading of the IEEE 802.15.4 network, as well as the use of diversity systems with a combination of different numbers of combiners at the micro and macro level.  
Conceptualization, B.J.; Methodology, B.J., J.T. and M.B.; Software, J.T. and M.J.; Validation, M.J.; Writing--original draft, B.J. and J.T.; Supervision, D.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Distributed Dynamic Cluster-Head Selection and Clustering for Massive IoT Access in 5G Networks
## 2 Description of Random Access and Data Aggregation
If the number of random access attempts has reached its upper limit, the random access procedure fails. Figure 1 shows an example of data aggregation in the cluster header devices. In the LTE-M wireless network system, 12 subcarriers in frequency or one slot in the time domain are called a resource block (RB). It can be used to carry data. As shown in Figure 1, _data1_, _data2_, and _data3_ will be placed in the buffer before a new cluster header device is to upload data. Before the cluster header device transmits data, it will upload other data in the previous buffer. The data of the devices in the cluster transmit to the cluster head device through an intra-cluster network such as Lora, SigFox, or other typical local area networks (LAN) protocol (orthogonal to cellular networks). The cluster head device then forwards the data to the base station using the LTE-M uplink channel. It can see from the above-detailed process that the cluster head device needs to perform a random access process before performing data transmission. At the same time, it is necessary to establish a connection with additional signaling to acquire the RB of the PUSCH for transmitting data. After the random access connection establishes, the cluster head device uploads the device data of the other clusters together in the previous buffer. In the transmission process, this is a new way to reduce the collision caused by the random access of a large number of devices in the cluster. This solution improves the overall energy efficiency utilization and reduces energy consumption with less signaling overhead. The solution is efficient for small data IoT devices.
###### Abstract
(2019) indicates that remote sensing signals are affected by noise, leading to pathological model inversions and reducing the accuracy of model inversions. [PERSON] et al. [PERSON] et al. (2019) used feature enhancement and active learning strategies to invert sea ice thickness, with results indicating that this method has advantages in handling high-dimensional data and noise issues, yielding an average absolute error of 8 cm in the inversion. Moreover, total phosphorus, as a non-optically active substance, has a complex relationship with remote sensing data, making it difficult for data-driven models to learn this complex mapping relationship from the original spectral data. Therefore, research on the processing of noise in remote sensing data and the extraction of effective information is essential. To address this issue, this study proposes a multilayer denoising autoencoder model to denoise remote sensing data and extract effective spectral features, achieving data dimensionality reduction and information fusion. This approach reduces the difficulty of subsequent model learning and improves the accuracy of subsequent ensemble learning models. The autoencoder [PERSON] et al. (2018) is a type of feedforward unsupervised neural network aimed at reconstructing input data to minimize the difference between the output and input data. During the process of reconstructing input data, this model can learn useful features from the input data, thereby achieving data dimensionality reduction and information integration, which can reduce the risk of model overfitting. Currently, autoencoders [PERSON] et al. (2018) have been applied in various fields such as air prediction. The multilayer denoising autoencoder is built upon the multilayer autoencoder by adding Gaussian noise, enabling the model to learn how to remove noise from the data and extract effective spectral features. Considering the different external environmental factors such as precipitation and temperature in Taihu Lake during different seasons [PERSON] et al. (2018); [PERSON] et al. (2018); [PERSON] et al. (2018), they may have a certain impact on the local total phosphorus concentration. However, previous studies have mostly only considered remote sensing reflectance as input data for water quality inversion. Therefore, this study incorporates the month of water quality monitoring as an additional input feature into the model to provide temporal information and improve the accuracy of the inversion model. Through the aforementioned water quality inversion framework based on multilayer denoising autoencoders, automated extraction of remote sensing data features and accurate inversion of non-optically active water quality indicators can be achieved. It is conducive to promoting the application of remote sensing data in comprehensive watershed water quality monitoring, enabling relevant personnel to formulate corresponding watershed environmental protection measures based on water quality retrieval results obtained from remote sensing data.
# Visual Static Hand Gesture Recognition Using Convolutional Neural Network
## 2 Materials and Methods
### Training Details  
All the experiments were completed on the Google colab CPU. Before training, the images are resized to 32 \(\times\) 32. A batch size of 64 is used for the training set, and a batch size of 32 is used for both the validation set and the test set. Each image is resized into 32 \(\times\) 32 and then enters the first convolution layer for the training phase. A learning rate of 0.001 is used with _Adam_ optimizer as our optimization function.  
NUS II Dataset and Marcel datasets are the two dataset used for the training. NUS II dataset is a 10-class hand posture dataset, as shown in Figure 2. The postures are shot in and around the National University of Singapore (NUS), against complex natural backgrounds, with various hand shapes and sizes, which makes it a challenging dataset, as shown in Figure 3. The postures are performed by 40 subjects of different ethnicities from different complex backgrounds. The subjects include both males and females in the age range of 22 to 56 years. The subjects are asked to show the 10 hand postures five times each. They are asked to loosen the hand muscles after each shot in order to incorporate the natural variations in the postures. The dataset consists of 2000 images; each image has a size of 160 \(\times\) 120. Marcel dataset [25] is a 6-class hand posture dataset, as shown in Figure 4. For Marcel dataset there are 4872 training images and 659 testing images with complex and uniform backgrounds. To ensure random shuffling for the Marcel dataset, we combine both training and test datasets in the original one to be a total of 5531 images. For both datasets, they are divided into two phases. In the first phase, each one is shuffled randomly and then separated into train and test sets, maintaining the ratio of each class in both of them as the same ratio of splitting by using stratify in the train_test_Split function used to split them. In the second phase, the training set from the first phase is split again into training and validation sets by maintaining the same ratio of classes in both of them as we have already achieved in the first phase by using stratify.  
Figure 2: Sample images from NUS hand posture dataset-II, showing posture classes A to J respectively. In the first row is the images of classes (A to E) and in the second row images of classes (F to J).
## 3 Results and Discussion  
### TSP and PAH Concentrations in Fukuoka and Kanazawa  
Figure 2 shows the daily and average concentrations of TSP and PAHs in Fukuoka and Kanazawa from 1 March to 31 May in 2020 and 2021. During the monitoring period, AD events were identified in 41 and 33 days in Fukuoka and Kanazawa, respectively, by either JMA or LIDAR (Table S1). These results showed that profiles of daily TSP and PAHs were different between the two cities, and there were more AD days in Fukuoka than in Kanazawa. Average TSP concentrations of the monitoring periods in Fukuoka (15.6 \(\upmu\)g m\({}^{-3}\) in 2020, 19.2 \(\upmu\)g m\({}^{-3}\) in 2021) were lower than those in Kanazawa (22.9 \(\upmu\)g m\({}^{-3}\) in 2020, 30.1 \(\upmu\)g m\({}^{-3}\) in 2021). However, the average total PAH concentrations of the monitoring periods in Fukuoka (0.64 ng m\({}^{-3}\) in 2020, 0.92 ng m\({}^{-3}\) in 2021) were much higher than those in Kanazawa (0.30 ng m\({}^{-3}\) in 2020 and 0.31 ng m\({}^{-3}\) in 2021). It should be emphasized that the concentration ratio of total PAHs to TSP was much larger in Fukuoka (0.04 in 2020, 0.05 in 2021) than that in Kanazawa (0.01 in 2020 and 2021).  
Figure 1: Map of sampling sites. Site 1: Fukuoka National Hospital (33.53\({}^{\circ}\) N, 130.41\({}^{\circ}\) E). Site 2: Kanazawa University (36.55\({}^{\circ}\) N, 136.67\({}^{\circ}\) E).  
The monthly average concentrations were calculated in Table 1 to investigate the seasonal variation of TSP and PAHs. The total PAH concentration was in the decreasing order Mar > Apr > May in both cities. However, the average concentration was higher in 2021 than in 2020 in Fukuoka, while it was comparable in two years. The TSP concentration did not show such a clear monthly tendency. The results suggested that the seasonal variations of PAHs in Japan were influenced by long-range transported PAHs (LRT-PAHs) controlled by the synoptic-scale of meteorology [13].  
The relationship between daily variations of TSP and total PAHs was weak in Fukuoka (non-AD: \(r\) = 0.321, AD: \(r\) = 0.059) and Kanazawa (non-AD: \(r\) = 0.521, AD: \(r\) = 0.526) (Table 2). As the reason for this, the different origins of TSP and PAHs are considered. TSP was affected by AD from deserts in the Asian continent on a planetary scale: moving within the upper layer of the planetary boundary layer and the free atmosphere [22]. On the other hand, LRT-PAHs emitted from Chinese cities closer to Japan than AD sources influenced PAH concentrations at monitoring sites on a synoptic scale: moving horizontally near the surface layer [13]. The difference in transportation of LRT-PAHs and AD will be discussed further in Sections 3.2 and 3.3.