esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Processing Methods
### Structure of MDISE Model
#### 3.2.1 MD Module  
To retain the global and local shallow features of the spectral data, the MD module is used to replace the first standard convolutional layer. The MD module proposed in this paper, as shown in Figure 6a, consists of four branches: a \(1\times 1\) standard convolutional layer and three \(3\times 1\) dilated convolutional layers with dilation rates of 1, 2, and 3, respectively. Dilated convolution expands the receptive field without increasing the number of parameters by introducing the concept of dilation rate [32]. For a 1D input data \(X\), the output data \(Y\) after the convolution operation of the dilated convolution kernel \(W\) is expressed as follows:  
\[Y(i)=\sum_{n=1}^{K}X[i+d(n-1)]W(n) \tag{3}\]  
where \(i\) is the index of the output data \(Y\), \(n\) is the index of the dilated convolution kernel \(W\), \(d\) is the dilation rate, and \(K\) is the number of elements in the 1D dilated convolution kernel. The three \(3\times 1\) dilated convolution kernels with dilation rates of 1, 2, and 3 have the same receptive field as \(3\times 1\), \(5\times 1\), and \(7\times 1\) standard convolution kernels, respectively. After each convolutional layer, a batch normalization (BN) layer is introduced to accelerate the model's convergence and provide regularization. Additionally, a ReLU activation function is applied after each BN layer, which is expressed as follows:  
\[\delta(x_{i})=\left\{\begin{array}{cc}x_{i}&x_{i}>0\\ 0&x_{i}\leq 0\end{array}\right\} \tag{4}\]  
where \(x_{i}\) is the input feature of the \(i\)-th channel. When \(x_{i}\leq 0\), the derivative is always 0; when \(x_{i}>0\), the derivative is always 1. Due to its simple computation, ReLU accelerates model training when using gradient descent for weight updates, while enhancing the model's non-linear representation capabilities and preventing overfitting. After passing through the four branches of the MD module, the input spectral data extracts both local and global shallow features. The number of convolution kernels in each branch is set to 32, and zero padding is applied to maintain consistent input and output feature dimensions. The features extracted from the four branches are concatenated along the channel dimension, resulting in an output with 128 channels, which is then passed to the next network layer.
## 4 Performance Evaluation
### Experimental Results
#### 4.3.3 Performance Counter Statistics  
We analyze the causes of performance improvements using the measurement data obtained from the performance counter. Since 29 benchmark programs were run with varying numbers of threads, ranging from 1, 2, 4, 8, 16, and 32. They were run on the NUMA server with the Linux kernels using different task selection policies. Lots of figures are obtained from experimental results. From the measurement of the standard deviation of each test case, shown in Figure 11b, the standard deviations for test cases using 4 or 16 threads are relatively small for various task selection policies. Since there are 32 cores in the system, a sufficient number of threads must be run simultaneously on the system. The kernel scheduler then migrates threads between nodes to balance the load when the nodes' loads are imbalanced. As the number of threads increases, more threads wait in the runqueue, and an effective task selection policy can select a more suitable one for migration. Therefore, we observe the runtime patterns for these test cases that use 16 threads. Figure 13 shows the result.  
Figure 13: Experimental results for systems using different policies. (**a**) Instructions per CPU cycle; (**b**) LLC misses per 1000 instructions; (**c**) The number of page faults; (**d**) The number of tasks that are migrated; (**e**) The number of pages that are migrated.  
Figure 13a,b shows the Instructions Per Cycle (IPC) and the Last Level Cache (LLC) misses per 1000 instructions (LLC MPKI) for systems that use different task selection policies. IPC is used as a reference to evaluate the CPU utilization rate. Cores fetch instructions or data from caches for execution. If the LLC misses, the required instructions or data are then accessed from the main memory. Hence, the LLC MPKI is used as a reference to evaluate the frequency of memory access. The results show that systems using the proposed task selection policies outperform the unmodified Linux kernel, which uses the default First-Fit policy.  
We also record the number of page faults, task migrations, and page migrations for each test case, and the results are respectively shown in Figure 13c,d. During the execution of tasks, page faults, task migrations, and page migrations increase the runtime overhead. Systems that use the proposed task selection policies all incur fewer of these operations and have a lower overall runtime overhead than the unmodified Linux kernel, so they perform better.
## 3 Results and Discussion
### Validation of the Method
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Matrix** & **Mycotoxin** & **Spiked Level** & **Recovery** & \multicolumn{2}{c}{**Precision (RSD, \%)**} \\ \cline{3-6}  & **(\(\mu\)g/L)** & **(Mean \(\pm\) SD, \%)** & **Intra-Day** & **Inter-Day** \\ \hline \multirow{5}{*}{Plasma} & \multirow{5}{*}{ZEN} & LLOQ & 86.4 \(\pm\) 2.8 & 7.3 & 10.0 \\  & & 5 & 92.1 \(\pm\) 2.3 & 2.6 & 3.0 \\  & & 20 & 91.1 \(\pm\) 3.0 & 2.9 & 3.5 \\  & & 50 & 94.2 \(\pm\) 3.4 & 6.0 & 5.4 \\ \cline{2-6}  & \multirow{5}{*}{ZEN-14G} & LLOQ & 103.0 \(\pm\) 3.5 & 6.7 & 10.4 \\  & & 5 & 102.5 \(\pm\) 1.1 & 6.0 & 4.1 \\  & & 20 & 97.6 \(\pm\) 3.4 & 2.5 & 3.5 \\  & & 50 & 95.9 \(\pm\) 1.8 & 3.8 & 3.2 \\ \hline \multirow{5}{*}{Liver} & \multirow{5}{*}{ZEN} & LLOQ & 93.9 \(\pm\) 8.0 & 14.6 & 13.2 \\  & & 5 & 93.9 \(\pm\) 4.0 & 3.4 & 5.7 \\  & & 20 & 109.7 \(\pm\) 2.9 & 3.1 & 6.4 \\  & & 50 & 104.3 \(\pm\) 2.6 & 3.7 & 3.2 \\ \cline{2-6}  & \multirow{5}{*}{ZEN-14G} & LLOQ & 102.2 \(\pm\) 4.5 & 7.4 & 7.2 \\  & & 5 & 96.3 \(\pm\) 3.0 & 2.6 & 3.3 \\  & & 20 & 103.6 \(\pm\) 1.8 & 4.2 & 2.8 \\  & & 50 & 108.7 \(\pm\) 2.4 & 6.9 & 5.9 \\ \hline \multirow{5}{*}{Kidney} & \multirow{5}{*}{ZEN} & LLOQ & 80.7 \(\pm\) 3.0 & 14.0 & 15.8 \\  & & 5 & 101.5 \(\pm\) 0.8 & 4.9 & 5.5 \\  & & 20 & 106.2 \(\pm\) 3.5 & 4.8 & 4.5 \\  & & 50 & 108.3 \(\pm\) 3.9 & 4.9 & 2.3 \\ \cline{2-6}  & \multirow{5}{*}{ZEN-14G} & LLOQ & 85.0 \(\pm\) 4.9 & 13.0 & 13.0 \\  & & 5 & 108.7 \(\pm\) 1.6 & 2.7 & 2.3 \\  & & 20 & 107.8 \(\pm\) 2.0 & 3.7 & 2.4 \\  & & 50 & 107.9 \(\pm\) 1.4 & 6.3 & 3.5 \\ \hline \multirow{5}{*}{Stomach} & \multirow{5}{*}{ZEN} & LLOQ & 90.4 \(\pm\) 6.2 & 13.6 & 11.1 \\  & & 5 & 95.6 \(\pm\) 3.6 & 3.0 & 4.4 \\  & & 20 & 99.3 \(\pm\) 1.1 & 4.4 & 1.6 \\  & & 50 & 93.8 \(\pm\) 2.7 & 8.8 & 1.9 \\ \cline{2-6}  & \multirow{5}{*}{ZEN-14G} & LLOQ & 94.9 \(\pm\) 5.9 & 9.8 & 13.3 \\  & & 5 & 104.9 \(\pm\) 0.9 & 6.6 & 2.5 \\  & & 20 & 100.4 \(\pm\) 3.5 & 4.2 & 4.1 \\  & & 50 & 100.8 \(\pm\) 4.1 & 5.9 & 3.0 \\ \hline \multirow{5}{*}{Small intestine} & \multirow{5}{*}{ZEN} & LLOQ & 88.8 \(\pm\) 3.3 & 4.1 & 11.0 \\  & & 5 & 95.0 \(\pm\) 1.9 & 2.0 & 4.8 \\  & & 20 & 94.2 \(\pm\) 4.2 & 2.9 & 12.3 \\  & & 50 & 100.2 \(\pm\) 3.1 & 3.3 & 7.5 \\ \cline{2-6}  & \multirow{5}{*}{ZEN-14G} & LLOQ & 99.6 \(\pm\) 4.1 & 5.1 & 6.2 \\  & & 5 & 100.6 \(\pm\) 2.2 & 3.4 & 7.5 \\  & & 20 & 98.2 \(\pm\) 3.6 & 3.2 & 9.0 \\  & & 50 & 100.7 \(\pm\) 1.0 & 3.3 & 4.9 \\ \hline \multirow{5}{*}{Large intestine} & \multirow{5}{*}{ZEN} & LLOQ & 86.9 \(\pm\) 4.5 & 8.5 & 12.8 \\  & & 5 & 103.5 \(\pm\) 3.0 & 2.4 & 2.9 \\ \cline{1-1}  & & 20 & 112.3 \(\pm\) 3.1 & 4.3 & 7.3 \\ \cline{1-1}  & & 50 & 98.1 \(\pm\) 8.6 & 3.9 & 3.9 \\ \cline{1-1} \cline{2-6}  & \multirow{5}{*}{ZEN-14G} & LLOQ & 95.6 \(\pm\) 3.6 & 11.3 & 5.3 \\ \cline{1-1}  & & 5 & 109.3 \(\pm\) 2.4 & 3.3 & 1.7 \\ \cline{1-1}  & & 20 & 100.4 \(\pm\) 4.7 & 6.4 & 6.3 \\ \cline{1-1}  & & 50 & 102.7 \(\pm\) 5.3 & 3.5 & 4.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Recovery rates, and intra- and inter-day precision of zearalenone (ZEN) and zearalenone-14-glucoside (ZEN-14G) (\(n=5\)).
# Scalable and Reliable Data Center Networks by Combining Source Routing and Automatic Labelling
## 3 The eTorii DCN
### Resilience: On-the-Fly Path Repair  
Two main approaches exist to recover communication paths after network failures, namely: restoration and protection [26]. The former considers that, when a failure occurs, the network will trigger a mechanism to generate a new path, while the latter establishes multiple backup paths a priory, so that they could be directly leveraged when the main one is not available anymore. Although the second approach has a faster recovery time, it also consumes additional resources to keep an updated list of backup paths. Therefore, there should be a trade-off of both approaches depending on the specific network scenario.  
In the specific case of eTorii, it benefits from the protection approach just by reusing the already assigned HLMACs. In particular, in eTorii, each HLMAC represents one possible path from a core switch to an end host; thus, multiple HLMACs represent multiple available paths, which means one main path and a set of backup paths, depending on the amount of stored HLMACs. For instance, in Figure 3, switch S9 has 5 assigned HLMACs, which indicates 5 different paths to reach the core switches and, more specifically, 3 paths to reach \(S1\) and 2 to reach \(S2\). Selecting an alternative path is as easy as modifying the destination HLMAC and this action could be performed at any point in the network. By limiting the amount of HLMACs assigned to each switch (as previously mentioned), we are just limiting the amount of alternative paths assigned. For example, limiting learning to 10 HLMACs will imply saving a list of only 10 labels and provides 1 main path to any node in the network plus 9 alternative routes, which seems to be a fair amount [27; 28]. Furthermore, this amount of HLMACs is independent of network size, which grants the scalability of eTorii.  
Additionally, as mentioned in Section 3.1, eTorii might assign HLMACs either with synchronized or without synchronized suffixes. Although synchronising suffixes implied additional control messages, one of the main advantages obtained is that path repair can be applied on-the-fly, when the failure occurs, as assigned HLMACs only differ in their prefix. Thus, when a route is not valid anymore, simply exchanging the prefix with another of the possible ones will instantaneously generate an alternative path, even if the message is in the middle of the network, which represents a minimum cost for rerouting.  
As a conclusion, eTorii is capable to reroute traffic on the fly just via small local forwarding, even when multiple link failures occur at once. Furthermore, this rerouting is lossless and the processing cost is low as it only implies modifying one prefix in the MAC address. In particular, the advantage of this mechanism is that it can be immediately applied without requiring to wait for a new address assignment to overcome the failure.
# Jamming and Spoofing Techniques for Drone Neutralization: An Experimental Study
## 3 System Design and Methodology
### Methodology
#### 3.2.2 GNSS BPSK Jamming  
Protocol-aware jamming is a sophisticated technique that disrupts signals by imitating and superimposing the original signal, optimizing the power-bandwidth ratio for efficient jamming. By using SDR capabilities, the jammer can precisely adjust its signal to cover the target bandwidth more effectively.  
In GPS systems, BPSK modulates data by changing the phase of the carrier signal. Protocol-aware jamming generates a jamming signal that mimics BPSK modulation and tricks the GPS receiver into interpreting the jamming signal as legitimate. This can severely compromise the accuracy of the positioning data. The corresponding GNU radio script is shown in Figure 4.  
The script begins with a Random Source block that generates a sequence of random bits at the chip rate of 1.023 Mbit/s, using a 100 M sample period. An interpolation factor of 4 matches the 4.092 MHz sampling rate of the PlutoSDR Sink, ensuring the data rate is appropriate for transmission (4 samples per chip (bit)). The remaining blocks map the bits to a \(+1\) and \(-1\) BPSK constellation.  
Finally, the PlutoSDR Sink transmits the modulated BPSK signal via the SDR, operating at the GPS L1 center frequency. Additional settings include a 32.768 k sample buffer size, non-cyclic transmission, and 0 dB TX1 attenuation. This configuration effectively implements a BPSK jammer that generates a random signal, modulates it, and transmits it on the GPS L1 frequency using the SDR.
# Summarization of Remaining Life Prediction Methods for Special Power Plants
## 2 Research Status at Home and Abroad
### State of Research on Data-Model-Fusion-Driven RUL Prediction Methods
For special power plants, whose performance degradation process mainly depends on dynamic parameters, the determination of the stochastic degradation model with a complex task profile is important. For real-time RUL prediction, a breakthrough in the data-model fusion method is also urgently needed. In addition, the acquisition of dynamic data is difficult, and it is necessary to design a reasonable measurement method.
# MDPN: Multilevel Distribution Propagation Network for Few-Shot Image Classification
## 2 Related Work  
### Few-Shot Learning  
Few-shot learning (FSL) can be broadly categorized into three ways: (1) using external memory; (2) introducing previous knowledge into the model initialization parameters; and (3) using training data as prior knowledge.  
The first way to use external memory is to store training characteristics in an external memory and then compare test features with the features read from the external memory to predict the label of the test sample. [PERSON] et al. [5] first put forward the idea of using external memory to perform FSL problems in 2016, and their proposed memory-augmented neural network (MANN) can overcome the concerns with LSTM [6] instability. MetaNet [7], proposed by [PERSON] et al., combines external memory and meta-learning. [PERSON] et al. [8] proposed a memory matching network that uses storage support features and the corresponding category labels to form \"key-value pairs\" in a memory module. [PERSON] et al. [9] proposed a lifelong memory module that uses the k-nearest neighbor (KNN) to select k samples that are closest to the query sample and predicts the label of the sample. However, it should be noted that the extra storage space will increase the cost of training.  
The second strategy, known as meta-learning, enables the model to learn how to learn by embedding prior knowledge into the model initialization parameters. MAML [10], a gradient-based method proposed by [PERSON] et al. in 2017, designs a me-ta-learner as an optimizer to update model parameters with only a few optimization steps when given novel examples. The MAML-based Meta-SGD [11] algorithm can learn both the direction and the pace of optimization. Additionally, [PERSON] et al. [12] proposed Reptile in 2018, which greatly reduces the computational complexity by avoiding the computation of two derivatives in MAML. MetaOptNet [13] proposed replacing the nearest-neighbor method with a linear classifier that can be optimized for convex optimization learning.  
The ways of using training data as prior knowledge are further divided into finetuning-based methods and metric-based methods. The goal of the former is to train the model using a lot of auxiliary data and then fine-tune it using the target few-shot dataset. The latter's goal is to create a network that can distinguish between several classes by doing feature distance analysis. Many classical networks for few-shot classification are based on metric-based methods. MatchingNet [14] generates a weighted nearest neighbor classifier by computing the mapping distance between the support set and the query set. ProtoNet [15], proposed by [PERSON] et al., extracts prototype features from samples of the same category and then predicts them by comparing the Euclidean distance between query features and prototype features. RelationNet [16] uses an adaptive nonlinear classifier to measure the relationship between support features and query features.
# Airborne GNSS Reflectometry for Water Body Detection
## 4 Segmentation of the GNSS Signal
### Segmentation of Airborne GNSS Measurements  
We show in Figure 9 the reflectivity measurements obtained with our GNSS-R receiver embedded on a gyrocopter. The surface reflectivity increases with the water content of the soil. Figure 9 shows the changes in the signal reflectivity associated to the reflections from land and from water bodies. In order to detect and localize water bodies, we apply an automatic segmentation of the signal using the reflectivity measurements based on the change detection and localization algorithms presented in the previous sections. Firstly, the changes in the reflectivity of the GNSS signal are detected with the Kalman-CUSUM algorithm (Figure 9a). Then, these changes are localized using the proposed MLLE approach (Figure 9b). Finally, the signal is divided into different segments associated to different mean reflectivity levels based on the localized change positions (Figure 9c).  
Figure 8: Change detection and localization. (**a**) change detection, (**b**) change localization.  
Figure 9: Segmentation of a GNSS signal obtained in an airborne experiment. (**a**) detection, (**b**) localization, (**c**) segmentation.  
The results obtained in Figure 9 show the feasibility of our approach to segment a real reflectivity signal obtained in an airborne experiment. In this context, the proposed approach is used for the detection and localization of in-land water body surfaces. This evaluation is realized with signals of reflectivity obtained for different satellites along the trajectory. The airborne experiment is described in the next section.
## 3 Results
In accordance and coherently with what was observed previously [40; 45; 46; 52], the value of the \(\gamma\) parameter increases with the decrease in the extracellular space where the water diffuses, and it decreases with the increase in the magnetic susceptibility difference between adjacent tissues. The \(\gamma\) vs. cell packing plot indicates that considering the extracellular space, which in gray matter has a \(\phi\) of about 40%, as the cell packing increases (corresponding to a decrease in extracellular space), \(\gamma\) also increases. This trend highlights the potential of \(\gamma\) as a biomarker to detect cell swelling, a key event during neuronal activation. However, \(\gamma\) also increases when the magnetic susceptibility difference (\(\Delta\chi\)) between tissues decreases, which is what happens during activation. This suggests that \(\gamma\) is influenced not only by microstructural properties, but also by the tissue composition and magnetic properties, correlated with changes in blood oxygenation or iron content. These results indicate that \(\gamma\) could serve as a valuable tool to assess changes due to the vascular component as well. To disentangle the two \(\gamma\) dependencies (neuronal and vascular effect during activation) and investigate the role of the neuronal component through cell swelling, it would therefore be necessary to design animal experiments using drugs that block the vascular effect during activation combined with DW-fmri acquisition sequences that use efficient bipolar gradients that greatly decrease \(\gamma\)'s dependence on magnetic susceptibility differences. Figure 3: \(\gamma\) behavior as a function of cell packing \(\phi\) in extravascular tissue and physiological condition (different \(\Delta\chi\)). The \(\gamma\) vs. \(\phi\) graph shows that when increasing the cell packing, i.e., decreasing the extracellular space, \(\gamma\) increases. This feature of \(\gamma\) suggests it to be a potential candidate for investigating the cell swelling occurring during neuronal activation. On the other hand, the \(\gamma\)-value decreases as the magnetic susceptibility difference between the tissues increases.
###### Abstract  
GMRES is one of the most powerful and popular methods to solve linear systems in the Krylov subspace; we examine it from two viewpoints: to maximize the decreasing length of the residual vector, and to maintain the orthogonality of the consecutive residual vector. A stabilization factor, \(\eta\), to measure the deviation from the orthogonality of the residual vector is inserted into GMRES to preserve the orthogonality automatically. The re-orthogonalized GMRES (ROGMRES) method guarantees the absolute convergence; even the orthogonality is lost gradually in the GMRES iteration. When \(\eta<1/2\), the residuals' lengths of GMRES and GMRES(m) no longer decrease; hence, \(\eta<1/2\) can be adopted as a stopping criterion to terminate the iterations. We prove \(\eta=1\) for the ROGMRES method; it automatically keeps the orthogonality, and maintains the maximality for reducing the length of the residual vector. We improve GMRES by seeking the descent vector to minimize the residual in a larger space of the affine Krylov subspace. The resulting orthogonalized maximal projection algorithm (OMPA) is identified as having good performance. We further derive the iterative formulas by extending the GMRES method to the affine Krylov subspace; these equations are slightly different from the equations derived by [PERSON] and [PERSON] (1986). The affine GMRES method is combined with the orthogonalization technique to generate a powerful affine GMRES (A-GMRES) method with high performance.  
GMRES; absolute convergence; orthogonality of consecutive residual vector; maximal decreasing length of residual vector; restarted and re-orthogonalized GMRES; affine GMRES; orthogonalized maximal projection algorithm  
65F10; 15A06