esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Data Collection Algorithms  
Here, we adopt the AUV auxiliary data collection method for 3D UASNs. By transferring the network energy consumption to the AUV, this method can effectively optimize the energy usage of the whole network, and thereby lengthen the network lifetime. However, due to the large scale of 3D UASNs and the limited velocity of the AUV, the mobile element auxiliary data collection often introduces long data delay.  
In order to reduce data delay for mobile data collection in 3D UASNs, we propose two probabilistic neighborhood-based data collection algorithms: grid and probabilistic neighborhood-based data collection algorithm with layered-scan (GPN-LSCAN), and probabilistic neighborhood covering set-based greedy heuristic algorithm (PNCS-GHA). The GPN-LSCAN is appropriate for 3D UASNs without known sensor nodes' locations. In contrast, PNCS-GHA is suitable for a 3D network in which the deployment information is known in advance. In our proposed algorithms, there is no need for the AUV to traverse all the nodes for data collection, allowing the efficient reduction of data collection completion time. Both algorithms are based on probabilistic neighborhoods defined as follows.  
Based on the special communication model that has been discussed in Section 3, we define a _probabilistic neighborhood_\(\Psi_{i}\) which covers all spaces where the successful information delivery probability to node \(i\) is over \(p\). The value of \(p\in\) [0,1] represents the conservative value of the successful information delivery to guarantee information gain. As \(p\) approaches 1, it will approach deterministic transmission. As \(p\) approaches 0, the AUV may need to access the node multiple times to collect the data. In the next subsections, we discuss the two probabilistic neighborhood-based data collection algorithms in detail.
## 2 Results and Discussion
### Purification of Antioxidant Peptides from Hydrolysate of STB-G
#### 2.2.3. Gel Filtration Chromatography (GFC)  
GFC is a popular method for the preparation of proteins or peptides on their molecular size used in food products and pharmaceutical industries [41]. As shown in Figure 3B, GH-I-3 was further divided into three fractions (GH-I-3A to GH-I-3C) using a Sephadex G-25 column. The EC\({}_{50}\) value of GH-I-3B on the DPPH radical was 1.08 mg protein/mL, which was significantly higher than those of GH-I-3 (EC\({}_{50}\) value of 1.32 mg protein/mL), GH-I-3A (EC\({}_{50}\) value of 3.69 mg protein/mL), and GH-I-3C (EC\({}_{50}\) value of >10 mg protein/mL) (\(p\) < 0.05). Therefore, fraction GH-I-3B was further separated into three components (GH-I-3B1 to GH-I-3B3) using a Superdex(r) Peptide 10/300 GL column (300 mm \(\times\) 10 mm, 13-15 \(\upmu\)m) in the experiment (Figure 3C). As shown in Table 2, the EC\({}_{50}\) value of GH-I-3B2 on the DPPH radical was 0.87 mg protein/mL, which was significantly higher than those of GH-I-3B (EC\({}_{50}\) value of 1.08 mg protein/mL), GH-I-3B1 (EC\({}_{50}\) value of 2.68 mg protein/mL), and GH-I-3B3 (EC\({}_{50}\) value of 5.74 mg protein/mL) (\(p\) < 0.05). Therefore, GH-I-3B2 should contain strong antioxidant peptides and was suitable for the following separation process.  
2.4. Purification of Peptides from GH-I-3B by Reverse-Phase High Performance Liquid Chromatography (RP-HPLC)  
RP-HPLC is an effective technique applied to purify bioactive peptides in a hydrolysate mixture on their hydrophobic character [39]. As shown in Figure 4, GH-I-3B was finally purified using the RP-HPLC system on an Agilent 1260 HPLC system with a Zorbax C-18 column, and the eluted peptides were gathered separately on the chromatographic peaks. At last, five peptides with a retention time of 10.393 min (STP1), 12.096 min (STP2), 14.827 min (STP3), 17.168 min (STP4), and 17.583 min (STP5) were collected and lyophilized for amino acid sequence identification and activity evaluation.  
Figure 3: Elution profile of STB-GH-I in diethylaminoethanol (DEAE)-52 cellulose anion-exchange chromatography (**A**), GH-I-3 in Sephadex G-25 chromatography (**B**), and GH-I-3B in a Superdex® Peptide 10/300 GL column (**C**).  
Amino Acid Sequence Analysis and Mass Spectrometry of Peptides from Gelatin Hydrolysate of Skipjack Tuna (K. pelamis) Bone  
The amino acid composition, sequences, and molecular mass of five isolated peptides (STP1-STP8) were determined using a protein sequencer and electrospray ionization mass spectrometry (ESI-MS), and the results are shown in Table 3. The amino acid sequences of five gelatin peptides (STP1-STP5) were identified as Gly-Pro-Asp-Gly-Arg (GPDGR, STP1), Gly-Ala-Asp-Ile-Val-Arg (GADIVA, STP2), Gly-Ala-Pro-Gly-Pro-Glu-Met-Val (GAPGPQMV, STP3), Ala-Gly-Pro-Lys (AGPK, STP4), and Gly-Ala-Glu-Gly-Phe-Ile-Phe (GAEGFIF, STP5) with MWs of 500.43, 544.55, 756.84, 374.39, and 739.76 Da, respectively, which agreed well with their theoretical masses (Table 3).
# Encoder-Decoder Structure Fusing Depth Information for Outdoor Semantic Segmentation
## 4 Frequency weighted intersection over union(FWIoU):
#### 3.4.1 IoU Results for Different Object Classifications within the Cityscapes Dataset  
Figure 8 shows the detailed IoU results for different object classifications within the Cityscapes dataset. The IoU results are close to 90%, and more than 90% have five categories, namely, road, building, vehicle, sky and car. This shows that the segmentation method is effective in large target segmentations. The object categories with IoU results of more than 80% include the sidewalk, bus and train. More than 70% of the object categories have walls and fences, indicating that these objects achieved good segmentation results. However, the segmentation effect for terrain, person, truck, motorcycle and bicycle was general, and the IoU of the other objects was less than 60%. The main reason is that the Cityscape dataset was mainly captured using corresponding sensors installed on autonomous driving devices. This means that the image contained more categories, such as the sky, road and cars, and fewer categories such as poles, traffic lights and bicycles. The final mIoU of our method was approximately 73%.  
Table 1 summarizes the IoU results of our method on different objects. Our method significantly outperformed SwiftNet [38] and RFNet [16] on the sidewalk, wall, fence, terrain and train categories and could achieve comparable performance on the road, vegetation, sky and bus categories.
## 2 Materials and Methods
### Methods
#### 2.3.1 Evaluation of Global DEMs for Costa Rica
The metrics we used for the statistical analysis are the Mean Error (ME), the Median Absolute Difference (MAD), the 25 th empirical quartile Q1, the Median (MED) value, which is the 50 th empirical quartile Q2, and the 75 th empirical quartile Q3. \[\text{ME}=\frac{1}{n}\sum_{i=1}^{n}(h_{i}-hr_{i}), \tag{1}\]  
\[\text{MAD}=median(|(h_{i}-\text{MED}|), \tag{2}\]  
where \(n\) is the total number of evaluated points for a given validation dataset, \(h_{i}\) refers to the DEM height of the \(i\)th evaluated point, and \(hr_{i}\) refers to the reference height of the point from the validation dataset. Additionally, these statistical metrics have the advantage that they are much less sensitive to outliers. It was also pointed out in [37] that the statistical metrics they used were the mean absolute difference and Normalised MAD (NMAD). Our tests suggested MAD to be preferable to NMAD (as used in [37] with a coefficient of 1.4826). The STD values were computed as well for differences that follow a normal distribution, e.g., in the evaluation of canopy height models, as  
\[\text{STD}=\sqrt{\frac{\sum_{i=1}^{n}((h_{i}-hr_{i})-\text{ME})^{2}}{n-1}}. \tag{3}\]  
The STD* values in DEM evaluations were computed for differences without outliers. Points were defined as outliers if the absolute differences \(|h_{i}-hr_{i}|\) were larger than 50 m. The value for the threshold is approximately the mean value for three STDs in comparisons of LVIS with nine studied DEMs. Furthermore, another relevant parameter to describe the DEMs' performance is the percentage of points that have an absolute difference from the validation dataset below a certain value, or threshold. The chosen values presented here are 5, 10, 15 and 20 m.
## 3 SAIS-BsDiff Algorithm
Step 3: After all S-type suffixes and L-type suffixes are inserted into the bucket, sort the L-type suffixes in the bucket from small to large again, sort the S-type suffixes in the bucket from small to large again, and change the position in the bucket to obtain the correctly sorted suffix array SA. The common suffix array sort method has a long string length and occupies significant memory space. In addition, coarse sorting in Step 2 and resorting in Step 3 result in the wastage of the operation steps. The BsDiff algorithm combined with the SAIS () function IS was adopted to select the LMS-type substring as the domain of the reduction problem, which reduces the number of strings to be processed compared to ordinary S-type suffixes replacing the original array. LMS characteristics are defined as follows [25]:  
1. LMS suffix: Suf(S, i) indicates the suffix in S starting with the LMS character and ending with the sentinel character 'S'. 2. LMS substring: String S[i...j] starts and ends with LMS-type characters and has no other LMS-type characters between them. According to this definition, the string S can be considered a combination of all LMS substrings. LMS string S[i...j] can be regarded as a partial prefix corresponding to the suffix (S, i). The lexicographic sort order is associated with S[i..j]. Thus, the following properties were obtained:  
Character: The lexicographic ordering of the suffix array (S, I) should be the same as that of the LMS string S[i..j]. For the character 'GTCCCGATGTCTACGGAS', the LMS substring is 'CCCGA', 'ATG', 'GTCA', 'ATG', 'GTCA', 'AGGAS', 'S'. All LMS substrings are sorted by radix sort and placed into a new string S1 = {S,AGGAS, ATG, CCCGA, GTCA}, as shown in Figure 6. Based on the aforementioned properties of S1, if we treat the LMS substring as a basic string block and effectively sort all LMS substrings, we can use the sequential index of each LMS substring as its name and replace the S-type suffix with a new LMS string index. Therefore, the problem of sorting the suffix array of string S is transformed into the problem of sorting the shorter-byte-length string S1, resulting in a new array {3, 2, 4, 2, 1, 0}. Owing to the existence of repeated blocks in S1, SAIS () is recursively called according to Step 5 of the SAIS function. S1 = {3, 2, 4, 2, 4, 1, 0} generates a new suffix array, S1' = {6, 5, 3, 1,  
Figure 5: Linear suffix array sort. 0, 4, 2]. At this point, there are no repeated blocks, and the call ends. S1 is mapped to the corresponding suffix array SA, and the same result as in Step 3 is obtained. The results demonstrate that the LMS substring can replace the sorting method of an ordinary linear suffix array and deduce the correct SA. This also shows that when S1 provides the lexicographic order of all suffixes of type S, the lexicographic order of all suffixes of type SA can be obtained. Because S1 is less than (1S1 /2), the length of the string is reduced to at least half its original cost. Recursive and induced methods effectively reduce the length of characters to be processed during dictionary sorting.
## 1 Introduction
For example, if only half of the number \"8\" is seen, \"8\" may be misclassified as \"E\" or \"3\", as shown in Figure 1. In addition, the characters at each position of the string are relatively random, so only the short-distance dependencies not the long-distance dependencies among the feature sequences are expected. Therefore, in this paper, we propose a novel short-memory sequence-based model for variable-length reading recognition of multi-type digital instruments. In our methods, shortcut connection strategy was involved into traditional convolutional structure to form a feature extractor for capturing effective features from raw digital instrument images. Then, an RNN-based sequence module, which captured the short-distance dependencies among feature sequences by giving up capturing long-distance trend changes and increasing the sensitivity to local changes, was employed to greatly improve the robustness and generalization of the model for invisible data. Finally, a novel short-memory sequence-based model consisting of a feature extractor, an RNN-based sequence module and the CTC, is proposed for variable-length reading recognition of multi-type digital instruments. In summary, the contributions of this paper are as follows. 1. Considering the variable font styles and variable aspect ratios of different kinds of digital instrument readings, as well as the difficulty in small characters recognition, we involve shortcut connection strategy into traditional convolutional structure to form a skip connection structure for extracting more complex and advanced character feature maps, taking advantage of the powerful high-dimensional function fitting ability of residual networks and deep network optimization capability;
2. In order to reduce the connection between the characters of the string, while emphasizing the local connections, we applied an RNN-based sequence module, which reduced  
Figure 1: An example of misrecognition with incomplete information. the long-distance trending memory of the string and strengthen the short-distance dependencies among adjacent sequences, obviously improving the recognition accuracy and generalization of the model;
3. Based on the above two innovations, we propose a novel short-memory sequence-based model, consisting of a feature extractor, an RNN-based sequence module and the CTC, which achieves promising results in the task of multi-type digital instrument reading recognition and performs robustly for invisible data.
## 4 Hybrid Trust-Based Congestion-Aware Cluster Routing (HTCCR)
### CH Selection Phase
#### 4.1.4 Cluster Maintenance Phase  
The cluster maintenance phase instantly begins once the first cluster is formed. This phase begins when any of the listed problems are encountered. The proposed algorithm provides a solution to every issue addressed below.  
1. **If a CH fails due to a discharged battery:** If this issue occurs, the following steps can be taken. 1. Nodes can combine with other CHs through CH and member selection. 2. Selecting a fresh CH from the sustained nodes and by attracting other nodes to the latest cluster.
2. **If the members fail because of battery discharge:** If this occurs, the CH ignores the dead nodes. Further, it transmits messages to the members and examines their efficacy and modified characteristics. In cases where the node fails to respond with any message, the node is considered to be dead, or it may be outside the range of the cluster. Hence, the node is removed from the member list.
3. **CHs may interfere:** In this case, the fitness values of two interfering CHs are compared, and the one with the highest value is selected as the CH. Nodes belonging to the CH with the lowest value join the new cluster. The non-member nodes of the new CH are chosen depending on the values obtained, and the other nodes will combine with the new CH.  
**Data forwarding phase**  
When forwarding data, the source forwards its data to the CH within its range. The CH obtains data and forwards them to the nearest CH or to the sink in cases where the sink falls within the CH range. The priority-based scheduling of data packets and network coding are introduced to improve data delivery. These phases are detailed below.
# Weighted Cluster-Range Loss and Criticality-Enhancement Loss for Speaker Recognition
## 4 Experiments and Results  
### Dataset and Evaluation  
In this work, the VoxCeleb1 [16], VoxCeleb2 [19], and CN-Celeb [42] datasets are employed to train and evaluate our models.  
In the VoxCeleb1 dataset, there are more than 100,000 utterances coming from 1251 speakers, with around 55% of the speakers being male. These utterances are extracted from videos uploaded to YouTube, covering various accents, ages, and speaking styles. VoxCeleb2 is a much larger dataset containing more than one million utterances from 6112 speakers. Besides, there is no intersection between VoxCeleb1 and VoxCeleb2. When it comes to CN-Celeb, it is a challenging dataset containing 1000 speakers in total and focuses on Chinese celebrities. However, the utterances of some speakers are too few to train, and thus, we only select those speakers with more than 40 speech utterances. Finally, there are 810 speakers in the filtered CN-Celeb dataset. Most of the experiments in this work are conducted on the VoxCeleb1 dataset, while VoxCeleb2 is only used for further evaluating the model for speaker verification, and likewise, the CN-Celeb is merely employed in the speaker identification task.  
For the speaker identification task, both training and testing are performed on the same Person Of Interest (POI). The development and test set statistics for VoxCeleb1 and CN-Celeb are presented in Tables 1 and 2, respectively. Top-1 and top-5 accuracy are utilized to evaluate the identification performance. For the speaker verification task, the Equal Error Rate (EER) is employed as the metric to evaluate the verification performance. The development and test set statics for this part are given in Tables 3 and 4. Note that throughout the speaker verification experiments, the test set remains the same, i.e, the test set from VoxCeleb1, and only the training set is altered.
# GNSS-Assisted Visual Dynamic Localization Method in Unknown Environments
## 4 Dataset Validation
### Data Processing and Analysis
#### 4.1.3 Motion Capture Coordinate System and World Coordinate System Transformation Matrix  
In accordance with Section 2.2, the transformation matrix between the motion capture coordinate system and the world coordinate system were solved as follows:  
\[R_{W}^{n}=\begin{bmatrix}0&0&1.0000\\ -0.4721&0.8815&0\\ -0.8815&-0.4721&0\end{bmatrix}\]
# SCMC: An Efficient Scheme for Minimizing Energy in WSNs Using a Set Cover Approach
## 5 Conclusions  
In this paper, we extensively studied the problem of minimizing the energy consumption and maximizing the lifetime of WSNs. For this purpose, an effective approach was presented using an optimization model. The proposed approach controls the energy depletion of sensor nodes and minimizes the routing path using the residual energy of the nodes. The corresponding optimization objective functions were solved using linear programing, and the simulation results showed that the proposed scheme (SCMC) outperforms MO-SCP, MDP-MSC, and LEACH in terms of WSN lifetime and throughput. As a future project, we intend to apply our approach to various base station scenarios. Additionally, the use of a multi-objective optimization model would be of great interest.  
**Acknowledgments:** Project funded by Research Translation Center (RTC), Grant No. GP-CCIS-2013-11-17.  
**Conflicts of Interest:** The author declares no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
# Dual Attention Equivariant Network for Weakly Supervised Semantic Segmentation
## 3 Model
### CSM Module
#### 3.2.1 Channel Attention Module  
The channel attention module can be used to learn the interdependency and importance of channels to aid the backbone network in extracting the requisite regions for the semantic segmentation tasks in the channel dimension; accordingly, this enhances the model's capability of capturing some important but easily overlooked features. As shown in Figure 3, the channel attention module first passes the features to both the max pooling and average pooling layers to obtain the global information of the features. This can capture the statistical characteristics of different channels in the feature map and provide insights into the importance and contribution of each channel within the entire feature map. Then, a fully connected network is used to learn the weights of channels to enable the weighted fusion and adjustment of the features from different channels. The two outputs of the fully connected network module are then added in an element-wise manner to facilitate the full utilization of the global contextual information and local detailed information to extract the features of target objects accurately. Finally, the fused result is fed into a sigmoid function. The above operations can be expressed as follows:  
\[Ca(f)=\varphi(MLP(Avg(f))+MLP(Max(f))), \tag{2}\]  
where \(f\) is the output of classification network, \(Avg\) is average pooling, \(Max\) represents maximum pooling, \(MLP\) is the fully connected layer, and \(\varphi\) represents sigmoid function.
# Rethinking Modbus-UDP for Real-Time IIoT Systems
## 4 Experimental Results
### Effect of Data Link Errors
The effect of datagram retransmission is indeed visible in Figure 8c,d as non-zero histogram bars located at approximately \(x=4\) ms, which is consistent with a nominal retransmission timeout \(t^{\text{(U)}}=3\) ms, as specified in Section 3, plus protocol stack overheads. Similarly, the worst-case round-trip time of around 8 ms can be attributed to double retransmission.
## 3 Aging of Lithium-Ion Batteries  
### Aging Mechanisms of Lithium-Ion Batteries  
The aging mechanisms of lithium-ion batteries are very complicated. With use, various aging mechanisms lead to the loss of active materials and the increase of internal resistance. Tracing back to the sources, the interaction between the electrolyte, the anode, and the cathode, as well as the degradation of the electrolyte itself, are where the aging phenomenon mainly comes from.  
Since the aging behavior of batteries has a lot to do with the current degradation status, even if the operating conditions remain the same throughout, they will be completely different at every moment. Based on some research, it can be classified into two categories according to the features of use: calendar aging and cycling aging [10; 11; 12], and the aging formula of the lithium iron phosphate battery (LFP battery) quoted in this article can refer to Equations (1) and (2) [13].