esa-sceva/satcom-chunk-collection huggingface dataset:
# Compressive Sampling with Multiple Bit Spread Spectrum-Based Data Hiding
## 4 Experimental Result  
We assess several evaluations in this section by simulations. The evaluation aspects of the proposed method included audio quality, security, watermark quality, watermark payload, and compression ratio level aspect. The simulations ran on ASUS notebooks using MATLAB with the following specifications, Advanced Micro Devices (AMD) Fx with 12 compute cores, 16 GB Random Access Memory (RAM), and Windows 10 operating system. There were 50 mono audio host files as the clips tested with different genres of music, sampling rate 44.1 kHz and 16 bit audio quantization. All clips were in the original wave files and licensed as free audio files for research [26]. The simulation output in this section showed the average of the simulation result. The evaluated performance parameters were the audio quality, the watermark robustness, the watermark payload, and CR. The Objective Difference Grade (ODG) represents the audio quality using Perceptual Evaluation of  
Figure 3: Finding feasible \(M\) and \(r\) to obtain payload >20 bps and CR > 1.  
Audio Quality (PEAQ) [27]. Parameter \(C\) represents the watermark payload in bps as described in (16). Parameter BER represents the watermark robustness in (34). CR represents the Compression Ratio, as explained in (6).  
We measured the audio quality between the original host audio and the reconstructed audio. The reconstructed audio quality was affected by two factors, the truncation of the diagonal matrix and the CS acquisition. The truncation of the diagonal matrix gave worse quality to the audio than the CS acquisition due to the loss of the audio signal information. The audio quality represented by ODG had a range from \(-4\) to zero, where \(-4\) meant the worst audio quality or the distortion was very annoying, \(-3\) meant the distortion was annoying, \(-2\) meant the distortion was slightly annoying, \(-1\) meant the distortion was perceptible but not annoying, and \(0\) meant the best audio quality or the distortion was imperceptible [27].
## 1 Introduction
These improvements have been translated into platforms that are in use by practitioners to aid monitoring of land cover change globally (e.g., Global Forest Watch). Continuous land cover change monitoring typically uses an unsupervised approach to detecting change by examining remotely sensed time series of vegetation activity from sensors such as Landsat or MODIS [21; 22; 23; 24]. Change is identified when vegetation activity deviates from expectations derived by fitting linear and seasonal trends. This approach has been used to great success in forest ecosystems [25; 26]. These systems are well suited for this application, as land cover change involves a very large shift in vegetation--from closed canopy forest to bare ground--that is comparatively easy to detect in the spectral information returned from satellites. Open-canopied and low tree cover ecosystems, such as grasslands and shrulands, have received less attention, presumably because land cover change in these systems is harder to detect [27]. Vegetation activity is overall much lower than in forests, with a high signal to noise ratio [28]. However, these ecosystems are the dominant land cover type in sub-Saharan Africa, make up >40% of the global total ecosystem organic carbon and contain a substantial proportion of the world's biological diversity [29]. Existing land cover change products for South Africa (e.g., [30]) summarize change over multi-year or multi-decadal time periods, and are not updated frequently enough to inform management interventions that may address the underlying drivers of change. Nor can they be used for enforcement and prosecution of illegal transformation as they lack the ability to accurately date the occurrence of an event. There is therefore an urgent need to assess the potential for continuous land cover change monitoring in non-forest ecosystems and improve the accuracy of available methods. Here I use multi-temporal high and very-high resolution remote sensing imagery to map and accurately date land cover change events over a 4 year period from 2016 to 2020 in highly biodiverse, highly threatened, shrubland habitats within the Renosterveld of South Africa. I outline a workflow using freely available and open source tools to automatically identify areas of potential change and confirm change events. I attempt to assign a date to each land cover change event and describe the feasibility thereof. I describe the spatial and temporal patterns of land cover change in the Renosterveld in relation to the drivers of change. The resulting dataset of located and dated land cover change events provides training data for algorithm development to accelerate the advancement of land cover change monitoring in the Renosterveld and similar ecosystems.
## 3 Problem Definition
### Decentralized Learning Challenges  
Edge servers must locally process and analyze data to meet the low-latency and privacy-preserving requirements. However, training machine learning models in such a decentralized setting introduces unique challenges as follows:  
1. **Data heterogeneity**: The data distribution across edge servers is often non-independent and non-identically distributed (non-IID), leading to model divergence during training.
2. **Resource constraints**: Edge servers have limited computational and storage capabilities, making traditional centralized training infeasible.
3. **Communication overhead**: Frequent synchronization between the central unit and edge servers increases bandwidth consumption and latency, especially in large-scale deployments.  
**Formal definition**: Let each edge server \(E_{j}\) hold a local dataset \(A_{j}=\{x_{1}^{j},x_{2}^{j},\ldots,x_{n_{j}}^{j}\}\) and a local loss function \(f_{j}(w)\), where \(w\) represents the model parameters. The objective is to minimize the global loss function as follows:  
\[F(w)=\frac{1}{m}\sum_{j=1}^{m}f_{j}(w),\quad\text{where }f_{j}(w)=\frac{1}{|A_{j}|} \sum_{x\in A_{j}}\ell(h(x;w),y) \tag{2}\]  
Subject to constraints on communication frequency and resource utilization.
## 2 Beamforming Algorithm Used in the Software Receiver
For each entity in the \(R\) matrix, \(r_{ab}=\overline{r_{ba}}\)\(a\) and \(b\) stand for the index of antenna elements. The constraint vector \(C\) is composed of a steering vector \(T\) as the first to \(N^{\text{th}}\) elements and zeros as the remaining \((M-1)N\) elements:  
\[ \begin{split} C=&\left[t_{1}\quad t_{2}\quad\cdots\quad t_{ N}\quad\underbrace{0\quad\cdots\quad 0}_{(M-1)N}\right]^{T}\\ T=&\left[t_{1}\quad t_{2}\quad\cdots\quad t_{N}\right]^{T} \end{split}  \tag{4}\]  
Traditionally, the signal of the first antenna element is set as the reference and its component in the steering vector is set to 1. The other components are set as phase shifts relative to the reference antenna, which are represented by:  
\[t_{i}=\exp\left(-\,j\left(\Delta\varphi_{i1}^{l}+\Delta\gamma_{i1}\right)\right) \tag{5}\]  
where \(\Delta\varphi_{i1}^{l}\)is the phase difference based on antenna geometry and the direction of desired satellite. \(\Delta\gamma_{i1}\)is the phase resulting from the difference of cabling and RF chain including down converter and digitization among elements of antenna array. \(\Delta\varphi_{i1}^{l}\) can be calculated as:  
\[\Delta\varphi_{i1}^{l}=\frac{2\,\bar{\varphi}_{i1}\cdot\hat{r}^{l}(\phi,\theta )}{\lambda} \tag{6}\]  
where \(\,\bar{p}_{i1}\)is the baseline vector of the \(i^{\text{th}}\) antenna and \(\hat{r}^{l}(\phi,\theta)\) is the unit vector to satellite \(l\) shown in the Figure 2.  
**Figure 2.** Antenna geometry and direction of a satellite showing calculation of \(\,\Delta\varphi_{i1}^{l}\,\). The baseline vector \(\vec{p}_{i1}\) is known from the array geometry, and \(\vec{r}^{l}(\phi,\theta)\) can be obtained through positioning. Only \(\Delta\gamma_{i1}\) needs to be re-calibrated whenever any part of hardware of antenna array is changed. From the derivation in [18], the solution of Equation (2) is:  
\[W=R^{-1}C\Big{(}C^{T}R^{-1}C\Big{)}^{-1} \tag{7}\]  
Two matrix inversions are needed to compute in Equation (7). Alternatively, the Frost algorithm [18] derived an adaptive approach which iteratively updates the weight:  
\[ \begin{split} W[0]=\frac{1}{N}\,C\\ W[i+1]=&\Bigg{(}I-\frac{1}{N}\,CC^{T}\Bigg{)}\big{(}I -\mu R[i]\big{)}W[i]+\frac{1}{N}\,C\end{split}  \tag{8}\]  
where \(\mu\) is the adaptation step size which can be constant or variable related to covariance matrix. \(i\) is the iteration count. In our software receiver, \(\mu\) is calculated by:  
\[\mu=\alpha\!\!\!/trace(R) \tag{9}\]  
where \(\alpha\) is a constant dependent on the number of entries in the \(R\) matrix.
## 3 Experiment and Results Analysis
### Experimental Results and Analysis  
#### 3.4.1 Results and Analysis of Experimental Comparison  
In order to assess the performance and generalization capability of the CW-CBGC model proposed in this study for identifying audit entities, we compared its accuracy, recall, and F1 value evaluation metrics with those of six other models on both our self-constructed audit dataset (Audit) and a publicly available dataset (Resume). The experimental results are presented in Table 5.  
Combined with Table 5, it can be inferred that: (1) the BERT-BiLSTM-CRF model, RoBERTa-BiLSTM-CRF model and this study's model for obtaining dynamic word vectors by the pre-trained model have significant improvement in accuracy and F1 value compared with the BiLSTM-CRF model for obtaining word embeddings using Word2 Vec, indicating that the pre-trained model can better capture rich semantic features, effectively solve the polysemous word problem, and enhance the model's ability to recognize entities. (2) The RoBERTa model adopts dynamic masking to learn different semantic information and word-level semantic representations to improve the model entity recognition effect, and the F1 value is slightly improved compared with the BERT model. (3) The Focus Loss Function enhances the model's ability to recognize difficult-to-divide entities through the weighting factor \(\alpha\) and the modulation factor \(\gamma\) on the basis of the cross-entropy loss function, and the F1 value of the model improves by 0.86%; however, there are labeled anomalous and noisy entities in the difficult-to-divide entities, and focusing on these types of entities will lead to overfitting of the model, and the GHM loss function balances the weights of the entity categories while reconciling the anomalous or loss of noise entities to prevent model overfitting and further improve the model recognition accuracy of audit entities. (4) The model in this study integrates Chinese glyph pinyin features and word-level information features into the character vector and is superior to the other comparison models in the three evaluation indexes, further proving that the model can effectively identify named entities in the field of government audit, and the model has better recognition performance, with an F1 value of 97.23%. (5) The model proposed in this paper outperforms other comparative models on the publicly available Resume dataset, achieving an impressive F1 score of 96.26%. This result demonstrates the model's remarkable generalization capability across different domains.
# An Architecture to Manage Incoming Traffic of Inter-Domain Routing Using OpenFlow Networks
## 5 Proof of Concept
### Signaling Overhead  
The evaluation metrics for the SDN protocols were highlighted by the survey of [PERSON] et al. [4] who indicated that the number of messages and the size of the messages were criteria for the measurement scalability of the SDN solutions. Based on this claim, the signaling overhead was measured of the proposed architecture for the size of East/Westbound messages.  
The dataset of CAIDA contained IPv4/IPv6 Prefix-to-Autonomous System (AS) mappings [39]. With that dataset, it was possible to verify the size of the messages required to update the network state of a customer. Thus, from the CAIDA database, the AS that owned the most number of prefixes on the Internet (in 2017) was extracted, which was value of 5416 prefixes. This value was the worst case possible as no other AS would require a bigger network state message. Furthermore, with that quantity of prefixes (worst case message), a JSON message was generated using the East/Westbound component, and the size of this message was measured.  
The bandwidth required to transmit the size of the message in the worst-case scenario, and various intervals of time were used with the goal of diluting the size of the message through those intervals, hence decreasing the bandwidth needed. The results are presented in Table 2, where the various intervals of time were adopted, and the bandwidth required for transporting the worst-case message.  
This was applied through ZIP compression to reduce the bandwidth required for the worst-case message (see Table 2). Therefore, the quantity of bandwidth required to transmit the biggest network update was relatively small when considering peer-to-peer communication, especially if some compression was applied. Furthermore, gaining better control of the utilization of available bandwidth for inbound traffic had the potential to overcome the waste of bandwidth in the use of signaling messages between a domain that deployed the proposed architecture and its customers.
## 5 Engineering Application
### Comparative Analysis  
The geological coal conditions in the Huainan and Huaibei coal mines are basically the same, both belonging to the traditionally defined fractured, soft, low-permeability, and gas-prone coal seams. Due to the difficulty of drilling holes in coal seams, both mines have adopted the development method of horizontal well segmentation fracturing. In direct tests, the gas production effect in the Huaibei coalfield was significantly better than that in the Huaianan coalfield.  
1. The main reasons for this difference lie in the geological conditions. The coal seam thickness in the Luling parameter well of Huaibei coalfield was 13.50 m, belonging to the stable ultra-thick coal seam in the entire region. In contrast, the coal seam thickness in Huainan coal mine was only 6.70 m, with significant variations in coal thickness.
2. Furthermore, based on the statistical data of the gas storage ratio from the parameter wells, the average gas storage ratio in the Panxie coal mine of Huainan was only around 0.55, while the average gas storage ratio in the Luling coal mine of Huaibei was around 0.64, reflecting the relatively low gas saturation of coal seams in the Huainan coalfield. From the permeability data of coal seams in the Huainan coalfield,  
Figure 20: Drainage and production curve of the LG01 well group in the Luling coal mine.  
it can be observed that the permeability of coal seams was low, with a maximum measured permeability of only 0.011 md, indicating extremely poor permeability of coal seams. Although the permeability of the No. 8 coal seam in Luling coal mine was also low, ranging from 0.18 to 0.99 md, it was much higher than that of the Panxie coal mine in Huainan.
3. Finally, due to the shallower burial depth, the bottom depth of Luling coal mine in Huaibei was approximately 742 m, while the burial depth of Panxie coal mine in Huainan was deeper, averaging around 1100 m. This resulted in increased pumping pressure for fracturing operations in Huainan, making it difficult to increase the displacement. The displacement in Luling coal mine in Huaibei generally exceed 10 m\({}^{3}\)/min, while the average displacement in Panxie coal mine in Huainan was around 8.2 m\({}^{3}\)/min.
## 2 Materials and Methods
### Spatial Auto-Correlation Analysis  
In this study, we used the Global Moran's I index to detect the spatial auto-correlation of LCE to analyze the degree of aggregation or dispersion of the spatial features in the GZA. The calculation method of Global Moran's I can be expressed as follows [17]:  
\[Moran's~{}I=\frac{n\sum_{i=1}^{n}\sum_{j=1}^{n}w_{ij}(Z_{i}-\overline{Z})\left( Z_{j}\overline{Z}\right)}{\sum_{i=1}^{n}\sum_{j=1}^{n}w_{ij}\sum_{i=1}^{n} \left(Z_{j}-\overline{Z}\right)^{2}} \tag{8}\]  
where n is the number of spatial units, Zi and Zi represent the carbon emissions of spatial units i and j (1 \(\leq\) i \(\leq\) 23,972; 1 \(\leq\) j \(\leq\) 23,972), and Wij is the spatial weight matrix of the adjacent prefecture-level units i and j.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Change Type** & **Definition** \\ \hline Significantly increasing & s \(>\) 0, \(\mid\)z \(\mid\) \(>\) 1.96 \\ Slightly significantly increasing & s \(>\) 0, \(\mid\)z \(\mid\) \(<\) 1.96 \\ No-significantly changing & s \(=\) 0 \\ Slightly significantly decreasing & s \(<\) 0, \(\mid\)z \(\mid\) \(>\) 1.96 \\ Significantly decreasing & s \(<\) 0, \(\mid\)z \(\mid\) \(<\) 1.96 \\ \hline \hline \end{tabular} Note: At the significance level \(\alpha=0.05\), a significant change occurs when \(\mid\)Zi \(\mid\) \(>\)1.96, and an insignificant change occurs when \(\mid\)Zi \(\mid\) \(<\) 1.96.  
\end{table}
Table 3: Statistics of change trend types.  
Additionally, the Local Moran's I index was used to detect the local spatial auto-correlation, which can be expressed as follows:  
\[Local\ Moran's\ I=\frac{n(x_{i}-\overline{x})\sum_{j=1}^{n}w_{ij}(x_{j}-\overline{x })}{\sum_{i=1}^{n}\left(x_{i}-\overline{x}\right)^{2}} \tag{9}\]  
where n denotes the number of space units; xi and xj represent the carbon emissions of space units i and j; and Wij is the weight. When I > 0, it indicates that there is a positive spatial auto-correlation between adjacent regions, showing a 'high-high (H-H)' or 'low-low (L-L)' type, indicating that there is a high (low) concentration of carbon emissions between adjacent regions. When I < 0, it indicates that there is a negative spatial auto-correlation between adjacent regions, which is 'high-low (H-L)' or 'low-high (L-H)' type, indicating that high (low) carbon emission regions are surrounded by low (high) carbon emission regions [35].
## 3 State of the Art of Burst Containments
### Literature Study and Qualitative Analysis  
Table 1 shows an overview of burst test data, which was gathered during a comprehensive literature study. The overview of FESS burst tests found in literature and the related qualitative analysis has led to the following conclusions:  
* The number of published results of burst tests of FESS is not sufficient to allow a statistically significant qualitative analysis. Furthermore, the requirements and ambient conditions for the different tests vary significantly making it impossible to derive generally valid, proper design guidelines.
* The so-called spin tests or over-speed tests (with the goal to investigate the rotor rather than the housing) were mostly carried out in over-dimensioned, bunker-like test facilities. Investigation of designated light-weight FESS containments during vehicle crash was so far only conducted in [14], whereby the rotor was crashed but did not actually burst.
* In 2015 a publicly-funded research project on the safety of FESS housings called FlySafe was launched by the UK Research Council [15]. However, the focus is set on the investigation of the failure mechanisms of high-performance fiber composite rotors [16].  
Figure 4: Eight most relevant aspects of FESS housing / containment design.  
Some related research was conducted in the field of turbo machinery. The findings of [PERSON] [22] from the 1970s, for instance, are still relevant today in burst containment design and constantly being modified and expanded [23; 24]. NASA and the U.S. Department of Transportation have also been active in the field of (aircraft) turbine containment safety design [25; 26]. More recent studies dealing with turbine disc failure were published by the Zhejiang University in China [27]. Some notable FESS-specific investigations regarding containment structures were conducted by the Center for Electromechanics at University of Austin, Texas and are described for example in [20; 28; 29].
# A Systematic Literature Review of LoRaWAN: Sensors and Applications
## 5 Conclusions  
The number of articles that have been placed in the \"Other works\" category is equivalent to 52% of the analyzed papers, which indicates that LoRaWAN is a relatively new technology for which many applications are still expected to be carried out in various real environments. In this sense, measuring the performance of this technology and generating proposals for improvement is essential for its widespread adoption in real applications. Based on this background, in Figure 2, it can be seen that 21% of the papers correspond to improvements in LoRaWAN technology in terms of energy consumption and data transmission, which indicates an interest of the scientific and technological community in the improvement of this technology.  
The classification of the papers in the different practical applications presented in Figure 4 indicates that this technology can be used in more than one application that includes areas such as health, agriculture, or security. In this aspect, although the use of LoRaWAN technology is still limited compared to other traditional wireless communication technologies, its use could be widely extended with the extension of the field of IoT and smart cities.  
Figure 4: Frequency of use of software tools.  
Figure 5: Most used board and module types and brands.  
In conclusion, it can be inferred that the research questions have been successfully addressed, with the exception of the primary areas of study within the realm of LoRaWAN. This exception arises due to the fact that only 48% of the papers analyzed correspond to LoRaWAN applications.  
Additionally, it can be concluded that the most used brand for boards and modules in real applications is Semtech Corporation with 15 projects, closely followed by Arduino and Raspberry Pi with 12 and 11 projects, respectively, positioning them as market leaders in the LoRaWAN applications that were studied.  
The improvements that have been implemented in LoRaWAN correspond to security, energy performance, and packet delivery; additionally, there is a trend toward improvements in energy performance.  
In this literature review, it has been observed that LoRaWAN is a promising technology that enables low-energy-cost communications. In addition, thanks to spread spectrum modulation, there is wide coverage and greater signal penetration through buildings and other obstacles. By working on unlicensed frequencies, the cost of contracting is avoided, making it an interesting option for many applications. For all this, LoRaWAN is a low-cost, long-range, and low-economic- and energy-cost technology, ideal for applications that require low data rates, such as environmental sensors, smart meters, asset tracking, and remote monitoring.  
The publication of this research article was funded by Escuela Politecnica Nacional.  
Not applicable.  
The authors declare no conflict of interest.