esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Bacterial Isolates  
LAB isolates were collected from the ileal and cecal contents of commercial and research turkey flocks in a previous study [4]. Briefly, samples were collected from birds aged 0-10 weeks from two high-performing flocks housed at the University of Minnesota and from a high-performing commercial turkey flock in Iowa. Birds in these flocks were fed standard corn:soybean diets. The isolates were derived from ileal and cecal contents, which were diluted and plated on De Man, Rogosa, and Sharpe (MRS) agar (BD Difco, Franklin Lakes, NJ, USA). The isolates were stored in 20% glycerol at \(-80\)\({}^{\circ}\)C until further use.  
For initial speciation of the turkey LAB isolates, PCR was performed with F342 and 518R 16S rRNA universal primers, 5\({}^{\prime}\)-CCTACGGGGGGCAG-3\({}^{\prime}\) and 5\({}^{\prime}\)-ATTACGCG-GCTGCTG-3\({}^{\prime}\), following a previously described protocol (Human Microbiome Project, 2012). Sanger sequencing was performed on each amplicon at the University of Minnesota Genomics Center. Following sequencing, each forward and reverse read was quality trimmed and aligned using DNASTAR version 9.0 software (Lasergene, Madison, WI, USA). The sequences were then searched against NCBI reference genomes using BLASTN for the best hit analysis to identify the closest matching bacterial species. Species were identified based on >95% sequence similarity. The isolates were later sequenced using Illumina MiSeq technology, as previously described [4]. The assembled sequences were then compared to an existing database for speciation using the entire genome's average nucleotide identity (ANI) [13].  
Reference genomes of LAB were also used to create a phylogenetic tree depicting the relationships among the isolates used in this study and existing isolates of known bacterial species. For this, the full 16S rRNA reference sequences were downloaded from NCBIand aligned with the full 16S rRNA sequences from the isolates within this study using MUSCLE [14] in the MEGA 11 software [15]. Phylogenetic analyses were performed using Maximum Likelihood with the GTR model with 1000 bootstrap iterations.
# Interferometric Wavefront Sensing System Based on Deep Learning
## 2 Method
### Wavefront Analysis Neural Network
#### 2.2.3 Training Networks  
The two networks are trained separately. Net1 carry out classification tasks, so it is appropriate to choose cross-entropy as the loss function, as formulated in Equation (25):  
\[\rm loss1=\ -\frac{1}{M}\sum_{i=1}^{M}\sum_{x,y}\rm logp(x,y). \tag{25}\]  
M is the number of samples in each batch of training data, and \(\rm p(x,y)\) is the probability value of the correct classification result. Adam optimizer [22] was used, the learning rate is initially set to 0.001, and the learning rate decay rate is 0.99. The batch size is set to 8, and the maximum training epoch is 20.  
The output result of net2 is the first 36 Zernike coefficients; to evaluate the quality of the fitting result, we choose mean square error (MSE) as the loss function, as formulated in Equation (26):  
\[\text{loss2}=\ \frac{1}{M}\sum_{i=1}^{M}\left(\hat{y}_{i}-y_{i}\right)^{2} \tag{26}\]  
where \(y_{i}\) is the logits value, and \(\hat{y}_{i}\) is the label value. [PERSON] optimizer was used, the learning rate is initially set to 0.0001, and the learning rate decay rate is 0.99. The batch size is set to 8, and the maximum training epoch is 40.  
Both networks were implemented using the TensorFlow framework (Google) and were computed on the i7 8700k CPU (Inter) and RTX 2070 GPU (NVIDIA).
# An Investigation into Audio-Visual Speech Recognition under a Realistic Home-TV Scenario
## 3 Methods
### Model Architectures  
In this paper, we also explored the impacts of the model architectures for audio and visual embedding extractors. As shown in the blue box of Figure 5, we separately replaced the audio and visual extractors with other architectures. For the audio embedding extractor, we replaced the original VGG with gate CNN [43]. For the video embedding extractor, we replaced the original Resnet with Swin Transformer [44].
# Enhancements and Challenges in CoAP--A Survey
## 6 Open Challenges
### Interoperability  
Enabling interoperability among different technologies is a challenging job. Similarly, with the design of the new protocol, it is important to build a mechanism for devices to speak to each other without technology barriers. The CoAP protocol is mostly based on the REST API and if, therefore, a device does not support CoAP may result in failure of communication. Therefore, the CoAPprotocols support internetworking communication with HTTP. The REST-based style of CoAP enables it to communicate to the HTTP protocol over proxies that are specifically designed for enabling interoperability among CoAP and non-CoAP devices [49; 50]. In general, it is easy to implement the mapping between CoAP and HTTP via proxies. However, it is still challenging whenever a CoAP-enabled device communicating with a non-CoAP and non-HTTP device. Various other technologies do not support HTTP and REST resulting in communication loss among the CoAP and those technologies.
# An Improved Multi-Sensor Fusion Navigation Algorithm Based on the Factor Graph
## 3 System Model for the Navigation System
### Measurement Model of the System
#### 3.2.3 Magnetometer Measurement Equation  
The magnetometer is one of the common sensors used in MUAV. It is lightweight and reliable. It can provide the direction of the magnetic field, which can be used as aiding information. The magnetometer output \(m^{b}\) is not influenced by the maneuvering acceleration. Thus, the well-compensated magnetometer data are used to measure the attitude matrix \(\mathbf{C}_{n}^{b}\) to enhance the accuracy of the attitude estimation. The magnetometer measurement equation is described as Equation (5).  
\[\mathbf{m}^{b}=\mathbf{C}_{n}^{b}\cdot\mathbf{m}^{n}+\mathbf{\omega}_{m} \tag{5}\]\[C_{b}^{n}=\left[\begin{array}{ccc}q_{0}^{2}+q_{1}^{2}-q_{2}^{2}-q_{3}^{2}&2(q_{1} q_{2}-q_{0}q_{3})&2(q_{1}q_{3}+q_{0}q_{2})\\ 2(q_{1}q_{2}+q_{0}q_{3})&q_{0}^{2}-q_{1}^{2}+q_{2}^{2}-q_{3}^{2}&2(q_{2}q_{3}-q_ {0}q_{1})\\ 2(q_{1}q_{3}-q_{0}q_{2})&2(q_{2}q_{3}+q_{0}q_{1})&q_{0}^{2}-q_{1}^{2}-q_{2}^{2}+ q_{3}^{2}\end{array}\right] \tag{6}\]  
where \(\mathbf{m}^{n}\) is the magnet vector in navigation frame. Supposing the local magnetic declination and magnetic inclination are \(\theta_{dec}\) and \(\theta_{inc}\) respectively, then \(\mathbf{m}^{n}=\left[\begin{array}{c}\cos\theta_{inc}\cos\theta_{dec}\\ \cos\theta_{inc}\sin\theta_{dec}\\ -\sin\theta_{inc}\end{array}\right]\).
# Domain Adaptation for Satellite-Borne Multispectral Cloud Detection
## 8 Results
### Ablation Studies
#### 8.2.1 Bandwidth-Efficient SDA  
The FISH Mask [80] was applied to the source cloud detectors (Section 8.1) with varying mask sparsity levels and optimisation conducted over a fixed number of 300 epochs. The entire training set of the target dataset was used to estimate the Fisher information of each weight.  
We found that only a small subset of weights were required to achieve similar model performance as updating 100% of the weights. Specifically, Figure 6(a) suggests that for CloudScout models, only 25% of the total weights need to be updated, whereas Figure 6(b) suggests that for ResNet50 models, only 1% are needed. While a higher mask sparsity level generally leads to a higher adaptation performance, CloudScout-3-S2 and CloudScout-8-L9 give us two examples of how updating 25% of the weights provides better adaptation than updating all the weights (see Figure 6(a)). These counter-intuitive results can be explained through the number of variables in optimisation problem (6): more optimisation variables could require more epochs to converge to a good local solution, so for the same number of epochs, fewer optimisation variables could lead to a better local solution.  
The results indicate the usefulness of the FISH Mask in alleviating the uplink restrictions on model size, although this applies only to offline adaptation (Section 3.3.1), which assumes a labelled target dataset.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**S2-TRAIN**} & \multicolumn{3}{c}{**S2-TEST**} & \multicolumn{3}{c}{**L9-TEST**} & \multicolumn{2}{c}{**GAP**} \\
**Model Settings** & **ACC** & **FP** & **ACC** & **FP** & **ACC** & **FP** & **ACC** & **FP** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline CloudScout-3-S2 & 92.85 & 0.96 & 92.07 & 1.72 & 66.40 & 0.80 & 25.67 & 0.92 \\ CloudScout-8-S2 & 93.36 & 3.69 & 92.41 & 4.48 & 52.00 & 48.00 & 40.41 & 43.52 \\ ResNet50-3-S2 & 97.86 & 1.11 & 93.10 & 4.14 & 90.80 & 1.60 & 2.30 & 2.54 \\ ResNet50-8-S2 & 93.73 & 2.51 & 93.79 & 2.41 & 56.80 & 43.20 & 36.99 & 40.79 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Model performance of cloud detectors _without_ domain adaptation. GAP is the absolute difference in performance between the testing sets of Sentinel-2 and Landsat 9. The red text and green text show negative and positive effects on performance, respectively.
## 3 Method
### AE-GRU Model
#### 3.2.2 Auto-Encoder Layer  
The simplest way to predict time-series data is to train the preprocessed matrix \(X^{\prime}\) directly. However, this simplest method has two disadvantages. First, the raw features of tropical cyclone data are so large that the training speed will slow down and the performance will be significantly reduced. Second, when data with multiple features are used to predict a few parameters, the model may be overfitted, which will result in a poor generalization. However, an AE neural network can find the data characteristics adaptively and then represent the complex data in an efficient way, which will improve the training speed and accuracy. Therefore, the AE layer is proposed in our prediction model to extract features from the preprocessed data \(X^{\prime}\).  
The detailed process of the AE layer is shown in Figure 2. It contains an encoder process and a decoder process. These two processes are neural networks with the same structures. The input and output layers have the same meanings and the same number of nodes. The encoder layer can reduce the number of dimensions of the input data \(X^{\prime}\) to a hidden layer, and the decoder layer will then decode the hidden layer  
In addition, the mathematical representation of the decoder process is shown below:  
\[\begin{split}& D_{1}=f\left(W_{1}^{\prime}\times E_{n}+b_{1}^{ \prime}\right)\\ &\cdots\\ & D_{n-1}=f\left(W_{n-1}^{\prime}\times D_{n-2}+b_{n-1}^{\prime} \right)\\ & D_{n}=f\left(W_{n}^{\prime}\times D_{n-1}+b_{n}^{\prime} \right)\\ &\hat{X}^{\prime}=f\left(W_{n+1}^{\prime}\times D_{n}+b_{n+1}^{ \prime}\right)\end{split} \tag{4}\]  
where \(\left(w_{1},w_{2},\ldots w_{n}\right)\) and \(\left(b_{1},b_{2},\ldots b_{n}\right)\) represent the weights and biases in the encoder process, \(\left(w_{1}^{\prime},w_{2}^{\prime},\ldots,w_{n}^{\prime}\right)\) and \(\left(b_{1}^{\prime},b_{2}^{\prime},\ldots b_{n}^{\prime}\right)\) represent the weights and biases in the decoder process, and nis the number of encoder and decoder layers. To train the appropriate parameters, the objective function is as shown in Equation (5), where \(N\) is the number of input data for batch processing. Finally, the hidden layer \(E_{n}\) is used as an input to the GRU layer during the following processes.  
\[\mathrm{L}\left(\widetilde{X^{\prime}},X^{\prime}\right)=\sum\frac{\left( \widetilde{X^{\prime}}-X^{\prime}\right)^{2}}{N} \tag{5}\]
# Authenticated Multicast in Tiny Networks via an Extremely Low-Bandwidth Medium
## 3 Establishing Group Keys
### Summing Strategies  
Suppose that we have a predefined family of subsets of \(\mathcal{N}\), say, \(S_{1},\ldots,S_{m}\subseteq\mathcal{N}\). Let the corresponding multicast keys \(K_{1},\ldots,K_{m}\) be preloaded on \(S\). For node \(i\in\mathcal{N}\), the key \(K_{j}\) is preloaded on node \(i\) if and only if \(i\in S_{j}\). If an encryption key for the group \(\mathcal{R}\) is to be established, the sender \(S\):  
1. Chooses a group key \(K\) at random;
2. Selects a set of \(w\) indices \(I\) such that \(\mathcal{R}=\bigcup_{i\in I}S_{i}\);
3. Broadcasts \(w\) messages, with the \(i\)th message being a ciphertext of \(K\) obtained with the key \(K_{i}\), i.e., \(\mathsf{Enc}_{K_{i}}(K)\).  
As \(\mathcal{R}=\bigcup_{i\in I}S_{i}\), each node of \(\mathcal{R}\) can decrypt at least one message and obtain \(K\), while a node from \(\mathcal{N}\setminus\mathcal{R}\) learns nothing about \(K\) (assuming that the encryption scheme is semantically secure).  
Note that the sets \(S_{i}\) selected by \(I\) in this solution need not be disjoint; however, the number of sets should be chosen to be as small as possible in order to minimize the number of messages in the third step. We refer to this strategy as the _summing strategy_.
## 3 Results
### Evaluation of SIF Simulations
#### 3.3.2 Variable FQE Estimated from SIF Observations  
Table 6 lists the variable FQE values estimated by minimizing the systematic deviations in \(\mathrm{SIF_{A}}\) and \(\mathrm{SIF_{B}}\) simulations for each fieldwork day. For further illustration, Figure 11 displays the seasonal cycle of \(\mathrm{fqe2}\) and \(\mathrm{fqe1}\)/\(\mathrm{fqe2}\) from the erecting to wheat's flowering period in 2015 and 2016. As illustrated, from wheat's erecting to its flowering stage, the \(\mathrm{fqe2}\) value gradually decreased from 0.013 to 0.007, while the \(\mathrm{fqe1}\)/\(\mathrm{fqe2}\) value exhibited an opposite trend and increased from 0.05 to 0.5.  
Figure 10: The correlation and relative root mean square error (RRMSE) values between simulated and measured \(\mathrm{SIF_{A}}\)/\(\mathrm{SIF_{B}}\) with fixed FQE: (**a**) the correlation and RRMSE for 106 spectral measurements and (**b**) the RRMSE values for each fieldwork day in 2015 and 2016.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**2015**} & \multicolumn{6}{c}{**2016**} \\ \cline{2-11}  & **April** & **April** & **April** & **April** & **April** & **April** & **April** & **April** & **May** & **May** \\  & **3** & **13** & **14** & **24** & **25** & **8** & **9** & **18** & **3** & **4** \\ \hline simulated \(\mathrm{SIF_{A}}\)/\(\mathrm{SIF_{B}}\) & 1.690 & 1.853 & 1.857 & 1.705 & 1.698 & 1.885 & 1.916 & 1.859 & 1.705 & 1.732 \\ \hline measured \(\mathrm{SIF_{A}}\)/\(\mathrm{SIF_{B}}\) & 1.370 & 1.686 & 1.590 & 2.470 & 2.237 & 1.522 & 1.605 & 1.990 & 2.213 & 1.997 \\ \hline RE & 23.37\% & 9.88\% & 16.80\% & \(-\)30.95\% & \(-\)24.08\% & 23.90\% & 19.39\% & \(-\)6.56\% & \(-\)22.97\% & \(-\)13.26\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: The simulated and measured ratio of daily SIFA and SIFB averages, as well as the relative error (RE) between them with fixed FQE for ten fieldwork days in 2015 and 2016.  
The seasonal cycles of fqe2 and fqe1/fqe2 were consistent with variations of the systematic deviation in the \(\mathrm{SIF_{B}}\) and \(\mathrm{SIF_{A}}\)/\(\mathrm{SIF_{B}}\) simulations (as mentioned previously), respectively. Specifically, the fqe2 value was close to its literature-fixed value of 0.01 only at the jointing period, while at the erecting period the value was larger (0.011-0.013), and at the booting or flowering period the value was lower (0.007-0.009). Also, the fqe1/fqe2 value was close to its literature-fixed value of 0.2 only at the jointing period, while at the erecting period the value was lower (0.05-0.1), and at the booting or flowering period the value was larger (0.3-0.5). These results confirm the seasonal FQE values, showing that the seasonal cycles of FQE values between 2015 and 2016 were greatly consistent.
## 4 The Role of Metallurgy Slags in Sustainable Construction  
Metallurgy slags, the by-products of the metal smelting process, are increasingly being viewed not just as waste but as valuable resources for sustainable construction practices [197]. These slags are composed of a mixture of oxides, silicates, and other metal derivatives, which have significant potential in various construction applications. Historically considered a disposal challenge, these materials are now recognized for their potential in reducing the construction industry's reliance on virgin raw materials, thereby supporting environmental sustainability [198]. In the iron and steel industry, the production of metallurgy slags is particularly notable, as the sector generates large quantities of this material during the steelmaking process. In 2023, global production of iron slag was estimated to range between 330 million and 390 million tons, while steel slag production was estimated to be between 190 million and 290 million tons [199]. The rising production of these slags highlights the need for effective management and reuse, particularly in the construction sector, where they can serve as substitutes for traditional materials, reducing environmental impacts and enhancing the sustainability of construction practices. Table 3 provides a summary of various construction applications for metallurgy slags, while the next sections detail their technical benefits and environmental impacts, underscoring the versatility and potential of these materials in the construction sector. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**SRM** & **Application** & **Dosage** & **Reference** \\ \hline SS (EAF) & Coarse aggregates in asphalt mixtures & 66.21–67.30\% & [200] \\ \hline SS & Fine aggregates and filler in bituminous mixtures & 10\% & [201] \\ \hline SS (BoF) & Coarse aggregates in road surface layers and asphalt pavements. & 15\% & [202] \\ \hline SS (EAF) & For wearing courses of asphalt pavements & (Not clear) & [203] \\ \hline SS & Aggregates in the production of asphalt mixes & 25–50–75–100\% & [204] \\ \hline SS & Fine or coarse aggregate fractions in asphalt mixes & Up to 100\% & [205] \\ \hline SS & Fine aggregates in asphalt concrete and stone matrix asphalt & 37 vol\% (AC)–22 vol\% (SMA) & [206] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Applications of metallurgy slags in construction.
## 3 Analysis of Shield Tunneling Parameters
### Excavation Speed  
The correlation between the excavation speed and the shield tunnel ring number is illustrated in Figure 13. Table 9 presents the statistical characteristics of the excavation speed across three distinct stratum conditions.  
Analysis of Figure 13 and Table 9 reveals that the excavation speed has undergone two main stages in the entire process. (1) Growth Stage (220-589 rings): During this phase,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Stratum Serial** & **Mean** & **Standard** & **Median** & **Minimum** & **Maximum** & **Skewness** \\
**Number** & **Value** & **Deviation** & **Median** & **Value** & **Value** & **Value** & **Skewness** \\ \hline I & 1.250 & 0.038 & 1.230 & 1.202 & 1.397 & 2.074 \\ II & 1.344 & 0.038 & 1.333 & 1.046 & 1.486 & \(-\)0.478 \\ III & 1.366 & 0.071 & 1.386 & 1.121 & 1.486 & \(-\)1.906 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Statistical characteristics of different strata with the cutter speed.  
Figure 13: The curve of the excavation speed changing with the ring number.  
the shield tunneling excavation speed experienced a rapid increase from 14.57 mm/min to 64.67 mm/min. The growth rate reached 0.136 mm/min per ring. (2) Fluctuation Stage (56-219 rings, 590-730 rings): The excavation speed fluctuated between 11.17 mm/min and 37.96 mm/min during rings 56 to 219. In stratum I and stratum II, the excavation speed was controlled at approximately 25.92 mm/min and 26.91 mm/min, respectively. In stratum III, characterized by stable soil with low resistance and nearing the end of the tunnel, the shield could maintain a high tunneling excavation speed, averaging 42.68 mm/min.  
In contrast to other strata [36; 37], volcanic ash formations exhibit more complex geological conditions and lower stratum stability. Shield construction in such areas is susceptible to ground deformation and subsidence. To ensure construction safety and stratum stability, the excavation speed of a slurry shield in volcanic ash strata is typically slower than in other strata, aiming to minimize the stratum disturbance. Due to the fragile formation stability of volcanic ash strata, the shield tunneling excavation speed is too fast, which will produce great vibration, which will have a great influence on the stability of the strata. The adjustment of the site tunneling excavation speed is a combination of engineering experience and site data-monitoring feedback. At the same time, soil improvement, real-time monitoring and risk management are used on site to ensure construction safety.
## 4 Result and Discussion
### Influence of the Wall Material on the Response Rate of the System during the Discharge Phase  
Given the influence of the solid layer, which forms during the discharge phase as highlighted above, the effect of the material used for the encapsulation of the sphere was also investigated.  
Figure 11: Comparison between the temperatures simulated corresponding to thermocouple (**a**) T1, (**b**) T2, (**c**) T3, and (**d**) T4 during the solidification phase of the PCM, k = 0.22 W/mK, and the solidification phase of composite materials (PCM+foam) with different thermal conductivities, 1 and 10 W/mK (T\({}_{wall}\) = 71 \({}^{\circ}\)C).  
Two materials with significantly different thermal properties were selected:  
* Aluminum;
* High-density polyethylene (HDPE).  
The physical properties of the two materials are shown in Table 2.  
Figure 12 compares the temperature trends in the correspondence of the four monitored points inside the sphere. As might be expected, the temperature of the PCM within the aluminum capsule (wall thickness of 1.5 mm) decreases more rapidly than in the case of HDPE encapsulation, only at the wall proximity (Figure 12d). Instead, the effect of the external material is negligible at inner locations and the system overall response time is almost independent of the wall material. This happens because of the comparable (in the case of HDPE) or much lower (for aluminum) thermal conductivity of the PCM compared to that of the containment material, so that, as soon as the solidification starts and a layer of solid PCM with increasing thickness forms, the thermal resistance of the wall material to heat conduction plays a secondary role on the overall heat transfer efficiency of the system.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & \(\rho\) [kg/m\({}^{3}\)] & \(\epsilon_{\text{p}}\) [kJ/kg K] & **k [W/m K]** \\ \hline Aluminum & 2719 & 0.87 & 202.4 \\ \hline HDPE & 965 & 2.25 & 0.49 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Properties of wall materials [40; 41].  
Figure 12: Comparison of the temperature trend inside the sphere using two different wall materials (aluminum and HDPE): (**a**) T1; (**b**) T2; (**c**) T3; (**d**) T4.