esa-sceva/satcom-chunk-collection huggingface dataset:
# Open-Set Recognition of Pansori Rhythm Patterns Based on Audio Segmentation
## 5 Experiment Setup  
### Evaluation Metrics  
We evaluated the performance of model on the Passori rhythm patterns and other acoustic datasets by two evaluation metrics:  
Area Under the Receiver Operating Characteristic (AUROC): The AUROC serves as a threshold-independent metric derived from the Receiver Operating Characteristic (ROC) curve. This curve delineates the model's True-Positive Rate (TPR) and False-Positive Rate (FPR) across various threshold settings. TPR is defined as \(\text{TPR}=\text{TP}/\left(\text{TP}+\text{FN}\right)\), representing the proportion of actual positives that are correctly identified, whereas FPR is defined as \(\text{FPR}=\text{FP}/\left(\text{FP}+\text{TN}\right)\), indicating the proportion of actual negatives that are incorrectly identified as positives. The AUROC is utilized to assess the model's ability to accurately identify unknown classes. Typically, an AUROC value of 50% corresponds to the performance level of a random detector, while a value of 100% indicates an ideal detector. It is important to note that, in the context of AUROC calculations, the unknown class is considered the positive class.  
Area Under the Precision-Recall Curve (AUPRC): While the Area Under the Receiver Operating Characteristic Curve (AUROC) effectively illustrates the impact of varying thresholds on the False-Positive Rate (FPR) and True-Positive Rate (TPR), it does not account for the unbalanced base rates of positive and negative samples. Conversely, the Area Under the Precision-Recall Curve (AUPRC) is delineated by the precision-recall (PR) curve, which elucidates the relationship between precision \(=\text{TP}/\left(\text{TP}+\text{FP}\right)\) and recall \(=\text{TP}/\left(\text{TP}+\text{FN}\right)\). The AUPRC focuses predominantly on the behavior of positive samples, thus heavily influencing the metric's sensitivity to the performance in identifying these samples. It is critical to precisely define which category is considered the positive case. In alignment with the approach of Ref. [14], the unknown class is designated as the positive case in our analysis. A high AUPRC value indicates an effective performance in detecting unknown Passori rhythm patterns or acoustic events.  
It is important to note that these evaluation metrics gauge the overall efficacy of our proposed method for unknown detection. Consequently, selecting the appropriate threshold, \(\rho\), as detailed in Equations (6) or (7), is crucial to achieving specific TPR and FPR values--or precision and recall--thereby optimizing the model's performance in practical scenarios.
## 3 Results and Discussion
### Effect of Poly (MMA-BA-ST) on Drilling Fluids Properties
#### 3.4.1 Effect of Poly (MMA-BA-ST) on AV  
Figure 4 shows the change curve of AV of drilling fluids after poly (MMA-BA-ST) was added. As can be seen from Figure 4, the AV of the basic drilling fluids is 62.0 mPa-s. With the increase in the poly (MMA-BA-ST) addition amount, the AV of the drilling fluids also increases. When the addition amount is 1.0%, the AV of the drilling fluids increases to 70.5 mPa-s, which is 13.71% higher than that of the basic drilling fluids. This variation has little effect on the drilling fluids.
## 3 Results  
During the on-farm trial and the journey to slaughter, no sanitary problem occurred that may have altered the trial results and interpretation. Animal carcasses were subjected to veterinary inspection at the slaughterhouse, and they were all judged to be fit for human consumption. The growth parameters recorded during the on-farm trial (data not shown) did not show any significant difference among the experimental groups in any of the parameters considered (body weight, average daily gain, feed consumption, feed conversion ratio). In the fattening phase (80-160 kg BW), the average daily gain was 702 g, and the feed conversion ratio was 4.26. Tables 1 and 2 show the lesions observed on the body and tail of the animals. In particular, Table 1 shows the average lesion count per each area and the overall number of lesions on the body, while Table 2 shows the percentage distribution of animals in severity classes for tail and body lesions. With respect to lesion counts, as expected, no differences were observed at the beginning of the trial in any body region. In groups C and L1, the number of lesions was numerically lower at the end of the trial compared to the beginning (with the only exception of thigh lesions in L1), but the same did not happen in group L2. At the end of the trial, significant differences were observed among groups for thigh lesions and for the total number of lesions on the body, with L2 showing the greatest number of  
lesions for both types of lesions, significantly higher compared to C (\(p<0.05\) and \(p<0.01\), respectively). A significant difference was also observed across the three groups (\(p<0.05\)) for flank lesions; however, the multiple comparisons test highlighted only a tendential difference (\(p<0.1\)), with L2 group showing a greater number of flank lesions than C. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Treatment** & **C** & **L1** & **L2** & **SE 1** & \(p\)**-Value** \\ \hline
**Animals, n.** & 36 & 36 & 36 & & \\
**Ear** & 1.91 & 2.37 & 1.53 & 0.138 & 0.0504 \\ Beginning & 1.58 & 1.72 & 2.03 & 0.125 & 0.4869 \\ End & 1.61 & 2.42 & 2.29 & 0.196 & 0.0539 \\
**Frank** & & & & & \\ Beginning & 1.35 & 1.00 & 0.81 & 0.121 & 0.6308 \\ End & 0.78 * & 0.69 & 1.43 * & 0.144 & 0.0280 \\
**Thigh** & & & & & \\ Beginning & 1.71 & 0.97 & 1.42 & 0.147 & 0.0648 \\ End & 1.28 b & 1.50 a,b & 2.17 a & 0.182 & 0.0346 \\
**Legs** & & & & & \\ Beginning & 0.29 & 0.49 & 0.19 & 0.0598 & 0.3263 \\ End & 0.083 & 0.36 & 0.23 & 0.0568 & 0.1171 \\
**Total lesions on the body** & & & & & \\ Beginning & 8.53 & 7.71 & 6.77 & 0.369 & 0.2865 \\ End & 5.33 B & 6.69 A,B & 8.14 A & 0.429 & 0.0015 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Skin lesions (count per each body region and overall number) of the three experimental groups (C = control, L1 = lavender nebulization once a day, L2 = lavender nebulization twice a day) assessed at the beginning and at the end of the trial. Different superscripts within the same row indicate significant differences (a,b \(p<0.05\); A,B \(p<0.01\)). Means with an asterisk within the same row are tendentially different (\(p<0.1\)).
# Integrating the IoT and Blockchain Technology for the Next Generation of Mining Inspection Systems
## 5 Evaluation
### Power Consumption and Energy Requirements  
Using an Otii device ([[https://www.goitech.com/](https://www.goitech.com/)]([https://www.goitech.com/](https://www.goitech.com/)), accessed on 14 December 2021) we could measure the power consumption with an accuracy of \(\pm\) (1%+ 0.5 \(\mu\)A ) at 3.3 V, at a rate of 1000 samples per second. Then, we consider 30 s as the time needed for an inspector to read the measurement on the device. We measured three cases: (i) baseline, where the measurement is only shown on the OLED display (i.e., sensing ), (ii) BLE, where the measurement is sent to the app using BLE (i.e., communications), and (iii) Blockchain, where the device digitally signs the measurement before sending it to the app (i.e., Blockchain). Table 6 shows the minimum, maximum, average power consumption (in mA), and total  
Figure 5: Distribution of the processing times on a public blockchain (expressed in seconds).  
\begin{table}
\begin{tabular}{l r r r r} \hline \hline
**Transaction** & **Gas** & **ETH/USD 2019** & **ETH/USD 2020** & **ETH/USD 2021** \\ \hline Twin (Tool) & 200,412 & 0.36 & 0.62 & 5.57 \\ Measurement & 27,300 & 0.05 & 0.08 & 0.76 \\ App (Inspection) & 3,500,531 & 6.37 & 10.75 & 97.24 \\ Inspection Report & 157,800 & 0.29 & 0.48 & 4.38 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Estimated transaction costs (expressed in USD) using different ETH/USD based on historical prices.  
energy consumption (in mWh) as the average of 100 experiments for each case. It is important to note that no low-power consumption optimization was implemented on the device. This data shows that enabling BLE increases the average power requirement by almost 38% (from 40.5 to 55.7 mA) and the overall energy consumption by 34%. Compared to BLE, enabling blockchain technology has almost no influence on power and energy requirements.  
In summary, these results show that the device could work continuously for 3.72 h in the baseline case, 2.76 h when using BLE, and 2.75 h when adding blockchain functionality, with a 500 mAh rechargeable LiPo battery. In the context of a real mining inspection, and based on the information described in Section 4.2, an inspection should perform between 60 and 70 measurements. Thus, if each measurement is 30 s, the total operation time of the connected tool, using BLE and blockchain, will be only between 30 to 35 min.
# Unconstrained Bilingual Scene Text Reading Using Octave as a Feature Extractor
## 2 Related Work
### Scene Text Detection  
Traditional and deep-learning machine-learning methods are used to detect texts from a natural image. In [1; 3; 23; 24; 25], scene text detection methods have been presented to detect and bind text areas from a natural image, but this approach has manual computation problems. [PERSON] et al. [25] presented sliding-window-based methods measured by shifting over the image and determining text proximity based on local image highlights. In [26; 27], a connected component analysis method was presented to detect scene texts using Stroke Width Transform (SWT) and Maximum Stable Extreme Region (MSER), respectively. However, these approaches are limited when it comes to detecting text regions from distorted images.  
Recently, deep-learning techniques improved several machine-learning problems, including scene text detection and recognition problem. [PERSON] et al. [1] presented a Connectionist Text Proposal Network (CTPN), which uses a vertical anchor mechanism that jointly predicts location and text/no-text scores of each fixed width. [PERSON] et al. [14] introduced Segment Linking (SegLink), which is an oriented scene text detection method that segments and then links the text to complete instances using a linkage  
Figure 1: The architecture of the proposed bilingual end-to-end scene text reader model.  
prediction. [PERSON] et al. [28] presented a novel rotation-based framework to detect arbitrarily oriented texts found in natural images by proposing region proposal network (RPN) and rotation RoI pooling. A deep direct regression-based method for detecting multi-oriented scene text has been presented in [29]. Efficient and accuracy scene Text detector (EAST) [5] has been introduced to effectively detect words or text lines using a single neural network.
## 4 Proposed Protocol
### Ranked Federated Averaging  
The Federated Averaging process is shown in Figure 7. The server will group and then tally the points of the model updates based on the number of **'accept'** and **'reject'** votes, resulting in \(S_{T}\), which reflects the overall score of the model update from the respective trainer \(t\). After the score tally is calculated, the \(T\)-highest scoring model updates will  
Figure 6: The sequence diagram of the voting procedure.  
be selected for federated averaging. ). At the end of the process, the owner will apply punishment to lower-scoring trainers and voters who voted for the punished trainers.  
1. The owner requests the result of the GetHighestScores() method (as shown in Algorithm 2) to the \(SC\) to obtain the highest scores of trainers based on the tally of accept and reject votes.
2. The \(SC\) invokes the GetHighestScores() method and returns \(S_{T}\) (the array of trainer IDs with the highest scores) to the owner. The value of \(T\) is equal to \(N/2\) which represents half of the total amount of trainers, so only the top half are selected.
3. The owner requests an array of \(\beta_{ipfs}\) to the \(SC\) based on the trainer IDs in \(S_{T}\).
4. The \(SC\) returns \(\beta_{ipfs}=\{\beta_{ipfs1},\beta_{ipfs2},...,\beta_{ipsfT}\}\) to the owner.
5. For each IPFS address, the owner requests respective model updates to IPFS based on the selected \(\beta_{ipfs}\) addresses.
6. IPFS returns \(MU=\{MU_{1},MU_{2},...,MU_{T}\}\) to the owner.
7. The owner then produces \(\Omega\), the resulting model from averaging the model updates (based on Algorithm 3).
8. The owner runs a hash function on the averaged model, producing \(H_{\Omega}\).
9. The owner sends \(\Omega\) to IPFS.
10. IPFS stores \(\Omega\).
11. IPFS generates \(\Omega_{ipfs}\), a unique cryptographic hash for \(\Omega\), and returns it to the trainer.
12. The owner submits \(H_{\Omega},\Omega_{ipfs}\) to the \(SC\).  
Figure 7: The sequence diagram for the federated averaging process.  
13. The \(SC\) stores \(H_{\Omega},\Omega_{ipfs}\).  
14. The \(SC\) relays the successful transaction event to the owner of the model.  
```
1:functionGetHighestScores
2:\(N\leftarrow\) (number of trainers)
3:\(S\leftarrow\) (array of trainer scores)
4:\(S^{\prime}\leftarrow\) sortDescending(\(S\)) \(\triangleright\) sort the array in descending order from the highest value first, provided as a function in the smart contract
5:\(S_{T}\leftarrow\) slice(\(S^{\prime}\),\(0\),\(N/2\)) \(\triangleright\) returns a section of an array, starting from the index specified in the second argument and ending at the index specified in the last argument
6:return\(S_{T}\)
7:endfunction
```  
**Algorithm 2** Get Highest Scores  
```
1:functionFedAvg
2:\(M_{0}\leftarrow\) (initial global model)
3:\(T\leftarrow\) (total amount of selected model updates)
4:\(MUL\leftarrow\) (array of model updates)
5:\(\Omega\leftarrow\frac{M_{0}}{T}\sum_{t=1}^{T}MUL_{t}\)
6:return\(\Omega\)
7:endfunction
```  
**Algorithm 3** Federated Averaging
###### Abstract  
The Argo buoy detects marine environmental data by making profile movements in the ocean and transmits the profile detection data to the shore base through the communication terminal. However, due to the large volume of data collected from profile detections and the continuous operation of the terminal, the remote communication of buoys is characterized by lengthy communication times and significant power consumption. A low-power Beidou transparent transmission terminal is designed to solve these problems in this paper. The terminal performs low-power operation management and power management for terminal remote communication. After the end of a communication process, the microcontroller turns the Beidou module power off and enters STOP mode. Before the next communication process begins, the serial port wakes up the microcontroller, which powers up the Beidou module. Before the remote communication, the microcontroller compacts the profile detection data collected by the buoy to reduce the quantity of remote communication data. In this paper, a variety of data compression methods are used to compare the compression rate, and the best compression method is selected according to the format characteristics of the data. The results show that the Beidou transparent transmission terminal of the Argo buoy can realize low power consumption for remote communication in ocean exploration. The terminal reduces power consumption by 77.282% per communication, and the average number of remote communications for each profile detection data are reduced by 55 times. The low-power Beidou transparent transmission terminal improves the battery life and is conducive to the long-term operation of the buoy.  
argo buoy; low-power design; remote communication; power management Article
## 3 Turbo Equalization Concept
### Log-MAP Equalizer
\begin{bmatrix}0&\mathrm{I}\end{bmatrix} ^{\mathrm{T}}\) of the BG2 matrix. \(\mathrm{Z}_{\mathrm{c}}\) represents the expansion factor. From Equation (19) it follows that the parity bits are computed as follows:  
\[\mathrm{P}_{1}^{\mathrm{T}}=\mathrm{Am}^{\mathrm{T}}\mathrm{B}^{-1}, \tag{20}\]  
\[\mathrm{P}_{2}^{\mathrm{T}}=\mathrm{Em}^{\mathrm{T}}+\mathrm{CP}_{1}^{\mathrm{ T}}, \tag{21}\]  
Concerning the LDPC decoding, the min-sum algorithm was used in this paper. This algorithm can be seen as a message propagation algorithm on the associated factorial graph. The messages passing through the branches of the Tanner graph are probabilities. The principle of belief propagation is the application of [PERSON] rule locally (on each bit) and iteratively to estimate the a posteriori probabilities of each bit [44]. The min-sum algorithm is a soft-decision algorithm that consists of message passing between variable and check nodes [45]; the iterative steps of the min-sum algorithm, for each iteration, are according to [46]. The min-sum decoder stops immediately whenever a valid codeword has been found, checking whether the parity-check equations are satisfied \(\left(\mathrm{cH}^{\mathrm{T}}=0\right)\) or the maximum number of iterations has been reached.
# A Framework for Assessing the Interference from NGSO Satellite Systems to a Radio Astronomy System
## 3 Framework of the Interference Analysis
### Space-Time Distribution of Satellite EIRP
#### 3.1.2 Generating the Beam Activation Probability  
The beam activation probability of an NGSO satellite system depends on the beam scheduling strategy, the available communication resources, and the user distributions. For the Walker constellation, the number of satellites visible to user terminals varies with its latitude, and the communication demand vary from region to region. In order to better match the actual NGSO satellite beam scheduling status, different beam activation probabilities should be set for satellites over different regions.  
The NGSO satellites provide services to their users through an efficient allocation of time slots, which is shown in Figure 5. The diagram shows that a satellite is providing time slots to users within its coverage during a time window. The black squares indicate that the satellite is providing communication services to its corresponding user at this time, while the other time slots corresponding to other users are white. The diagram shows the NGSO satellite beam serving three user terminals in seven time slots. The beam is active in 5 slots and the probability of activation of the beam in this duration is \(\hat{\bar{5}}\). Here, the concept of beam activation probability is introduced; however, the beam activation probability cannot be determined, as it needs to be determined by the satellite operator according to the actual situation of the satellite system.  
Assuming that the activation probability of the satellite beam is \(P_{a}\), the expected EIRP of the for cell \(G_{ij}\) can be expressed as the following equation,  
\[\overline{EIRP}_{ij}=P_{a}\int_{-\infty}^{+\infty}ep_{i,j}(e)de. \tag{32}\]  
The value of \(P_{a}\) in the case of an overloaded user requirement is usually 1.
# An Efficient Satellite Resource Cooperative Scheduling Method on Spatial Information Networks
## 1 Introduction
First, we construct a satellite network graph based on the current satellite and its surrounding satellite resource status information. Then, we design the resource allocation algorithm with the least cost (time) consumption based on the graph theory. Moreover, according to the allocation result, we reallocate the resource request queue to the satellites with idle resources for processing. The satellite resources assigned to the resource request operation can meet the current resource demand, but there is still contention for inter-satellite link resources. The optimization of global link resources is proposed according to a tiny spatial information network and the maximum flow theory in the graph. Finally, we achieve the goal of minimizing the time consumption of task execution on spatial information networks. Our contribution can be summarized as follows:  
1. We propose the RQRCS method that mainly includes a multi-satellite resource scheduling model and algorithm based on the dynamic reconstruction of a multi-satellite cooperative resource request queue. We aim to minimize the execution time of the resource request queue and solve the problem of poor timeliness of spatial information network resource scheduling to achieve the goal of rapid response to the resource request of the task. 2. In order to prevent the resource scheduling process from relying too much on the ground-based stations and causing significant waiting time delays, we investigate an autonomous resource allocation strategy on satellites. First, we obtain information on the status of idle storage and observation resources on the neighboring satellites of the current satellite and construct a small-satellite network. Then, we construct a minimum cost flow calculation model for dynamic maps to calculate the mission data transmission route with the shortest time to solve the problem of fast and cooperative allocation of multi-satellite resources. 3. To solve the problem of contention for spatial information network link resources, we calculate the transmission route and the maximum data flow at the current moment according to the link resources usage and the amount of data to be transmitted. We transform the global link optimization problem from a small spatial information network to a multi-objective maximum network flow problem. Then, we design the maximum flow allocation of inter-satellite link resources algorithm to improve the utilization of link resources and reduce the time consumed by resource contention.
## 4 Experiments
### Dataset Descriptions
#### 4.1.2 Hongqi Canal Dataset  
The second dataset, Hongqi Canal dataset, contains two high-resolution multispectral remote sensed images which focus on the region of Yellow River Estuary near the city of Dongying in China, as shown in Figure 3. The bitemporal images, which have the spatial resolution of 2m and the size of \(539\times 543\), were acquired by GF-1 satellite on 9 December 2013 and 16 October 2015, respectively. It mainly describes the river changes of the Hongqi Canal settled in Xijiu village.