esa-sceva/satcom-chunk-collection huggingface dataset:
# An Analysis of the Short Utterance Problem for Speaker Characterization +
## 5 Experiments & Results
### Reduction of the Mismatch in a: Phonetic Balance
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Scenario** & **EER(\%)** & **MinDCF** \\ \hline Long-Short Random & 5.67 & 0.27 \\ Long-Short PHN Balanced & **3.62** & **0.19** \\ \hline Short-Short Random & 8.57 & 0.40 \\ Short-Short PHN Balanced & **4.11** & **0.20** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of results, EER(%) and minDCF, between Short-Random and Short Balanced dataset in SRE10 “corexet-corexet det5 female” for scenarios Long-Short and Short-Short. Figure 5: Detection Error Trade-off (DET) curves for the scenarios Long-Long (blue), Long-Short and Short-Short Random (red continuous and dashed line respective), Long-Short and Short-Short Balanced (green continuous and dashed line respective) for SRE10 “corexet-corexet det5 female” experiment. Results in Table 3 illustrate many details. First, the KL2 distance increases for both target and non-target trials as long as we move from the Long-Long experiment to the Short-Short Balanced and finally the Short-Short Random experiment. Besides, this KL2 distance is always higher in the non-target trials population than in target trials. Moreover, regardless of the utterance length or content, evaluation errors are mainly caused by the misclassification of target trials. We also see some correlation between the relative KL2 distances and the errors. In target trials the lower the distance, the lower the error in the target population. These results also illustrate that our Short Balanced dataset obtains its improvement mainly from the target trials, halving their error. With respect to the non-target trials, we see a negative correlation, with an error term deceasing as long as the KL2 metric increases. Finally, combining the information of Tables 2 and 3 we realize that the relationship between the relative KL2 distance and the error metrics EER and minDCF is not linear. We carried out a linear regression of the evaluation results (EER and minDCF) in terms of the KL2 distance. This regression was estimated in terms of the obtained results for the Long-Long and Short-Short Random experiments. When we infer the results for the Short-Short Balanced experiment according to its KL2 distance, those are significantly worse than the really obtained ones. According to this regression this experiment should have obtained 6.33% EER and 0.30 minDCF, far higher than the obtained values. Therefore, the distance/EER and distance/minDCF relationships should be steeper with higher KL2 values and more even with the lower distances.
# A Network Coding Based Hybrid ARQ Protocol for Underwater Acoustic Sensor Networks
## 4 Protocol Design
### Protocol Overview  
The overall process of NCHARQ protocol is illustrated in Figure 3. In NCHARQ, the source node first groups the original data packets into blocks, and each block contains \(K\) data packets. The original data packets \(K\) in a block size is set as a system parameter. The packets are sent hop by hop and block by block in the network. The sending nodes encode each block into \(N\) encoded packets with fountain code independently. Then, \(K_{i}^{\prime}\left(K<K_{i}^{\prime}<N\right)\) out of \(N\) encoded packets are selected randomly according to the window size estimation algorithm in node \(i\), and the sender sends the \(K_{i}^{\prime}\) encoded packets at one time. Then, it waits for the feedback from the adjacent downstream node to decide whether to send a new block or retransmit part of encoded packets in the previous block. The process of NCHARQ in sender and receiver is described in Table 1.  
Clearly, there are two main problems that remain unsolved. One is how to choose an optimal \(K_{i}^{\prime}\) so that each node can ensure high probability of data successful recovery while decreasing the number of retransmissions. As the propagation delay is much longer than transmission delay in UASNs, the delay for retransmission will largely decrease the channel utilization and result in a large end-to-end delay. The other problem is how to select the \(m\) encoded packets (including the number of packets and the selection of packets) when the retransmission process is needed. Traditional protocols like SDRT [13] and FOCAR [15] present some offline algorithms to estimate optimal sending window size; however, these algorithms cannot adapt to the dynamic environment in the underwater channel. Here, we present a practical way to estimate \(K_{i}^{\prime}\) and \(m\) efficiently.  
Figure 3: The overall process of network coding based hybrid ARQ (NCHARQ) protocol.
# Interspectral Error Tracking and Compensation of DSDT in Satellite Internet of Things
###### Abstract  
With the rapid growth of satellite Internet of Things (SIoT) services, existing frequency band resources are insufficient to meet future business demands. To effectively address this issue, it is necessary to enhance the utilization of existing frequency resources. However, idle frequency resources are typically scattered across multiple bands and vary in bandwidth size. Direct Spectrum Division Transmission (DSDT), dividing a complete signal into sub-spectrum signals for transmission in idle frequency bands, can take the use of fragmented spectrum resources for satellite communication. Nevertheless, the performance of DSDT depends heavily on accurate synchronization toward multiple sub-spectrums. In this paper, an algorithm for error synchronization tracking and compensation is proposed by utilizing the focusing nature of constellation. All sub-spectrums are weighed by the minimum Euclidean distance of the constellation to compensate for amplitude-frequency-phase errors simultaneously. Simulations and experimental verification demonstrate synchronization performance and feasibility of proposed method in a multi-radio frequency channels environment.  
satellite Internet of Things (SIoT); direct spectrum division transmission (DSDT); blind synchronization; satellite communication +
Footnote †: journal: Electronics Electronics  
Electronics20202132098[[https://doi.org/10.3390/electronics13112098](https://doi.org/10.3390/electronics13112098)]([https://doi.org/10.3390/electronics13112098](https://doi.org/10.3390/electronics13112098))
# A Fault Section Location Method for Distribution Networks Based on Divide-and-Conquer
## 2 Numerical Computational Model for Distribution Grid Fault Location  
### Basic Principle of Distribution Network Fault Location  
When a fault occurs in the distribution network, the FTU installed at the smart switch (including circuit breakers and section switches) detects the current crossing and uploads the fault detection results to the fault location decision servers, which determines the fault device based on the fault location algorithm. In this paper, we refer to the smart switch equipped with FTU as a node and the distribution line enclosed by the nodes as a section. The task of the fault location is to accurately locate the section where the fault occurs.  
With the help of the fault hypothesis theory, the fault location problem can be transformed into a combinatorial optimization problem. The basic principle is as follows: assuming that all possible fault scenarios to construct a solution space, find the optimal solution in the solution space, and this optimal solution  
Applying the vector \(\mathbf{J(X)}\) represents the switching function corresponding to the assumed fault scenario \(\mathbf{X}\), the vector \(\mathbf{J(X)}=[J_{1}(\mathbf{X}),J_{2}(\mathbf{X}),\ldots,J_{m}(\mathbf{X})]\). The combination of Equation (9) in matrix form and the vector \(\mathbf{J(X)}\) is shown in Equation (10):  
\[\mathbf{J(X)}=\prod\limits_{x_{k}\in\mathbf{X}}\mathbf{P}_{k} \tag{10}\]  
where \"\(\prod\)\" means the superposition operation of \"OR\" is completed for multiple same-dimension vectors consecutively.  
The distribution network shown in Figure 2 is used as an example to verify the effectiveness of the road vector in describing the causal relationship. Assuming the fault scenario \(\mathbf{X}=\{x_{3},x_{6}\}\), according to Equation (10), the vector \(\mathbf{J(X)}\) is shown in Equation (11):  
\[\mathbf{J(X)}=\prod\limits_{x_{j}\in\mathbf{X}}\mathbf{P}_{k}=\mathbf{P}_{3}\lor\mathbf{P}_{6}=[1 \;1\;1\;0\;0\;0]\lor[1\;1\;0\;0\;1\;1]=[1\;1\;1\;0\;1\;1] \tag{11}\]  
where \"\(\lor\)\" means that the corresponding elements of two same-dimension vectors undergo logical \"OR\" operations.  
Equation (11) has the same result as Equation (5), which shows that the road vector can effectively describe the causal relationship between nodes and sections.
# Fault Detection of Resilient Navigation System Based on GNSS Pseudo-Range Measurement
## 2 Methods
### Fault Detection and Isolation Method
According to the Neyman-Pearson criterion, \(D_{0n}\) satisfies as follows  
\[D_{0n}=\sqrt{\lambda^{dP}}Q(\frac{1}{2}(1-\frac{P_{fa}}{N})) \tag{17}\]  
where \(Q(. )\) is the error function. \(\lambda^{dP}\) is the maximal eigenvalue of \(dP^{+}_{0n}(k)\), which is the covariance matrix of \(dX^{+}_{0n}(k)\). The matrix \(dP^{+}_{0n}(k)\) is expressed as  
\[ \begin{array}{ll}dP^{+}_{0n}(k)&=E[dX^{+}_{0n}(k)dX^{+}_{0n}(k)^{\mathsf{T} }]\\ &=P^{+}_{0n}(k)-P^{+}_{00}(k)\end{array}  \tag{18}\]  
with \(P^{+}_{00}(k)\) and \(P^{+}_{0n}(k)\) the covariance matrices of the estimation error for main filter and sub-filter excluding the satellite pseudo-range measurement, respectively. Considering that the probability of the simultaneous failure of each sub-navigation system is small, it can be concluded that there is no fault in the system after the isolation of the faulty pseudo-range measurements. Even if there are still outliers in the measurement of the satellite receiver, it can be eliminated based on the integrity algorithm of the satellite receiver to ensure better navigation performance.
## 3 Methodology  
The developed framework effectively incorporates current methods for geophysical data processing combined with machine learning to extract meaningful features from seismic microtremor data using the HVSR [33]. First, we handle the special analyses, especially for seismic data recorded by a plurality of stations, via steps needed to convert the data into raw form to gain further insights. Each step shifts the data closer to its ultimate purpose, providing critical features for predictive modeling, especially for site characterization in geophysical applications.
# Long Length Document Classification by Local Convolutional Feature Aggregation
## 2 Related Works
### Recurrent Neural Network in NLP  
Recurrent neural networks have shown great success for sequential data. In particular, LSTM (long-short term memory) [15] tackles the notorious problems of \"gradient vanishing\" and \"gradient exploding\" by its carefully designed gates. In the fields of NLP, the popular Seq2 Seq [16] model is based on RNN/LSTM, in which a multi-layer of an LSTM network is used as an encoder and another multi-layer of the LSTM network is used as a decoder. This kind of Seq2 Seq model has many variants and has been applied in many applications, such as machine translation [17], text summary generation [18], and Chinese poetry generation [19], among others.  
Regarding document classification, as RNN/LSTM could help to encode the sequential nature of sentence or even document, the extracted feature could then be augmented to improve the basic model. In the literature [4], the authors introduced a recurrent convolutional neural network for text classification to capture contextual information. In the literature [20], the authors improved the basic RNN/LSTM based text classification model using a multi-task learning framework. Recently, a hierarchical attention method was proposed in the literature [5], in which the word-sentence structure of the document is explicitly modeled by this hierarchical attention model.  
These methods have achieved very good performance in document classification tasks, but they are designed to access all text information, which limits those approaches applicability to relatively short documents.
## 3 Problem Statement
### Model
#### 3.2.3 Battery  
The battery is considered in the present model through the adaptation introduced by [PERSON] et al. in [9], in which they propose an MILP formulation for the battery based on theGeneralized Disjunctive Program (GDP) of [PERSON] and [PERSON] [21]. According to this representation, the state of charge (SoC) of the battery is defined by constraints (9a)-(9g).  
\[\text{SoC}_{s,t+1} \geq\text{SoC}_{s,t}+\frac{i_{s,t}\times e_{c}}{60\ Q}-M\times(1-b_ {s,t}),\ \forall t \tag{9a}\] \[\text{SoC}_{s,t+1} \leq\text{SoC}_{s,t}+\frac{i_{s,t}\times e_{c}}{60\ Q}+M\times(1-b_ {s,t}),\ \forall t\] (9b) \[\text{SoC}_{s,t+1} \geq\text{SoC}_{s,t}+\frac{i_{s,t}\times e_{d}}{60\ Q}-M\times b_ {s,t},\ \forall t\] (9c) \[\text{SoC}_{s,t+1} \leq\text{SoC}_{s,t}+\frac{i_{s,t}\times e_{d}}{60\ Q}+M\times b_ {s,t},\ \forall t\] (9d) \[i_{s,t} \leq M\times b_{s,t},\ \forall t\] (9e) \[i_{s,t} \geq-M\times(1-b_{s,t}),\ \forall t\] (9f) \[b_{s,t} \in\{0,1\},\ \forall t \tag{9g}\]  
where \(i_{s,t}\) is the total current of a satellite \(s\) in a given time step \(t\). Notice that this current can be either positive or negative, indicating the direction of the energy balance \(b_{s,t}\), which is then used to define the efficiency to be used in the SoC approximation, in other words, the efficiency parameter \(e_{c}\) is considered if the energy balance is positive, while the efficiency parameter \(e_{d}\) is used if the energy balance is negative (i.e., the battery is discharging).  
The overall SoC variation is limited by constraint (10), which implies restraining battery usage.  
\[\text{SoC}_{s,t} \leq 1,\forall s,\forall t \tag{10a}\] \[\text{SoC}_{s,t} \geq\rho,\ \forall s,\forall t \tag{10b}\]  
Finally, as stated in (11), for each instant of time, the sum of the resources demanded by the active tasks is restricted to the sum of the maximum available power from the solar panels and a portion of the energy resources available in the battery.  
\[\sum_{s=1}^{S}\sum_{j=1}^{J}q_{s,j}x_{s,j,t}\leq r_{t}+\gamma\ V_ {b}\ (1-\lambda_{s,t}),\ \forall t \tag{11a}\] \[0 \leq\lambda_{s,t}\leq 1,\ \forall t \tag{11b}\]  
where \(\lambda\) is a decision variable regarding the amount of resources to be used from the battery, considering a fixed battery voltage \(V_{b}\) and a fixed battery draining current \(\gamma\), in other words, a total of \(\gamma V_{b}\) units of power are used.
## 4 Discussion
This lack of annotated images can also result in lower-quality datasets, affecting the training quality for learning a neural network and the detection capacity of the trained network. As detection is not entirely accurate, its use results in some images being classified incorrectly. False positives can be identified with a new classification from a neural network that is better trained for this task. For example, this is possible if the classification network is trained with a larger dataset or better annotations. Datasets with more annotations for classification are common due to the greater ease of annotation than annotation for detection. This article evaluated the performance gain of neural networks for identifying concrete pathologies. The authors presented a possible use for neural networks in a previous article, and in this article improvements have been proposed and experimented with to achieve better metrics. Regarding detection, using YOLO in version 4 resulted in 0.12 mAP. Reviewing the dataset allowed us to identify some inconsistencies, basically incorrect annotations due to the rotated image, which can be seen in Figure 4. In addition to corrections, the annotations were put to better use with additional annotations where possible. This dataset was then trained with the same version of YOLO, version 4, to observe the learning gain. The result was 0.13 mAP, which was only a small gain because the number of inconsistencies was insignificant. However, revising the dataset is fundamental for the neural network to learn correctly. The last detection experiment on the revised dataset is performed with YOLO, version 8. This experiment shows whether or not we will gain from updating the technology used with the neural network. The result is 0.14 mAP, representing the same gain as the revised dataset. A good literature review is also a fundamental step towards understanding what is new in the research area and adopting the new standard to achieve the best possible performance. The classification experiment in this article uses the same technology as the detection experiment, and we can demonstrate that this technology allows both options and segments to be carried out efficiently and can even be performed in combination. Previously, the performance was 99.43% accurate using a customized neural network, which is already excellent. The experiment in this article used YOLO, version 8, for classification and obtained a performance of 99.65%, which is equally significant and shows a slight gain in accuracy. The review by specific classification allows, as mentioned, verification of false positives that occurred by detection. YOLO has shown itself to be an extremely versatile technology, providing the three tasks of computer vision with artificial intelligence: classification, detection, and segmentation.
## 2 Materials and Methods
### Data Analyses
#### 2.4.3 Sampling Rate and Social Networks  
To estimate the optimal sampling rate for detecting an individual's nearest neighbours, four sampling approaches were examined. These included recording intervals of (1) 5 s (1 sample every 5 s--the initial frequency was every second but it was impossible to compute continuous 1 s data due to the processing power of the PC), (2) 5 min (1 sample every 5 min), (3) 1 min of recording at 1 s intervals and 4 min off, and (4) 1 min of recording at 1 s intervals and 9 min off. These sampling intervals were tested as battery power would be minimally affected by reducing the sampling interval to every 10 s or every 30 s, however, 4 min off would extend the battery life by a factor of 5. The data collected during the second day of the study (a total of 6 h and 30,240 observations based on a continuous sampling rate of 5 s) were used for this analysis. As a small number of individuals were used (\(n=7\)), it was assumed that all the animals would be close enough to each other at least once to capture all the neighbours for each individual. The nearest neighbours for all animals were detected using the function of edge_nn of spatsoc package [29] while considering three different threshold distances at a maximum of: 10 cm, 20 cm, and 30 cm apart. The number of neighbours for each animal was then counted and plotted based on the different sampling rates and threshold distances using ggplot2 [41] in R [40].
## 4 Experiment and Result
### Data Description  
Jilin-1 Dataset  
The experiment used Jilin-1 KF satellite data from Jilin-1 series. The satellite can obtain the panchromatic resolution of 0.75 m, multispectral resolution of 3 m, and width of more than 136 km. It is the world's largest submicron optical remote sensing satellite with high resolution and super-large amplitude, high-speed storage, high-speed data transmission, and other characteristics. Related scholars [49; 50; 51] have carried out some research work on remote sensing intelligent information extraction based on Jilin-1 data.  
The Jilin-1 cropland dataset was mainly obtained from high-resolution remote sensing image data of Northeast China (Figure 7), and the timespan was from spring to autumn. The category labeling data were correspondingly cropped from the project vector results through a sliding window. The size of each image pair was 1024 \(\times\) 1024, and there were three categories: dry land, paddy field, and others.
# Publishing Anonymized Set-Valued Data via Disassociation towards Analysis
## 3 Neighbor Datasets: a Road-map for Distance Evaluation
### Dataset Formalization  
This work focuses on unordered records of sets of distinct data. For instance, a summary of visited cities during last month for some travelers, sets of unique words used in web queries for collection of persons, etc. There is no interpretation of the sens of the data neither in terms of semantic nor in terms of values. Data in records are either equal or distinct. They are thus considered as unique. For instance, if the same city is visited twice by a traveler, it will only be counted once in the summary of cities visited during the period under study. Moreover, the order (between records or between innermost data) is not taken into consideration. There is thus no notion of temporality in the data.  
From a mathematical point of view, the considered data are multisets of finite sets of discrete data. Multiset allows duplicated records: two distinct user may have indeed visited the same cities, and have then the same data.  
Multisets of sets may be efficiently represented by a forest of trees. Figure 4 gives, for example, two trees representing the same multiset.  
Figure 4: Two trees representing multiset \(\{\{a,b,c\},\{b,c,d\},\{a,b\}\}\).  
Each path from the root to a leaf defines a set, and conversely. Storing multiset of sets as is thus motivated by the objective of computing distance between two multisets as a distance between two trees.  
However, as shown in Figure 4, each multiset of sets can possibly be represented in various ways. In this case, using tree edit distance (TED) will not lead to a distance because the distance between a multiset and itself will not be zero, which would contradict the separation property. The next section presents how this problem is tackled.