esa-sceva/satcom-chunk-collection huggingface dataset:
# Secure LoRa Firmware Update with Adaptive Data Rate Techniques +
## 2 Materials and Methods
### Committing Code to Flash  
After a firmware image is received, the device must first validate it before committing and allowing the image to be executed. Validation begins by reading the image from memory, decrypting it, and then computing its CRC. The CRC computed over the entire image is compared to the CRC value that was sent during the initial exchange. If the two CRCs do not match, the firmware update process is restarted. Additional checks can be performed to increase the confidence that the firmware image is valid, such as checking it against a minimum size threshold and validating the correctness of the initial instructions.  
Once the image is validated, the system will begin to execute the new application. The process varies significantly depending on whether external or internal memory is used, as seen in Figures 5 and 6, respectively. For the external memory case, the device first confirms that there is sufficient battery to commit the code to flash, and then jumps to the boot loader to begin loading the memory. It reads and decrypts each 64-byte portion of the new firmware update, and then writes the data to flash. After each write, it reads the flash to check that there are no write errors. Upon completion, the processor will reset and jump to the starting address of the application code. In the scenario where external flash is not used, the device bypasses the flash write step since the code already resides in application space. It instead jumps to the starting address (assuming the code is properly re-based) and begins execution. For future firmware updates, it alternates the flash locations between App Code 1 and App Code 2, as indicated in Figure 6.  
Figure 5: Flash memory breakdown for using external flash.  
Before jumping to the new application, all interrupts and processor initialization are disabled and the stack pointer and vector table are re-based. We accomplish this by simply resetting the processor and setting registers in the boot loader to point to the new application code location. This ensures that everything is disabled when the boot loader hands over control to the application. Although it is possible to jump to a new application directly without resetting the processor, doing so mandates that all interrupts and initialization are first disabled which can be tedious.
# Non-Local Means Hole Repair Algorithm Based on Adaptive Block
## 2 Depth Image Inpainting Algorithm
### Hole Filling Algorithm Based on Non-Local Means
The repair weight can be described as follows:  
\[r(x,y)=\frac{g(x,y)d(x,y)}{Z_{1}(x)} \tag{3}\]  
where to ensure that \(\sum_{y\in A(x)}r(x,y)=1\), the normalization constant \(Z_{1}(x)\) is introduced. Before calculating the weights, the sizes of the search block \(A(x)\) and the repair block \(B(x)\) of the hole points \(x\) need to be manually set. The selection of the search and repair block sizes directly affects the algorithm's runtime and repair effectiveness. Therefore, it is essential to strike a balance between the repair time and repair effectiveness of the algorithm by enabling the algorithm itself to compute appropriate sizes for the repair and search blocks. This encapsulates the challenges confronted by the NLM hole repair algorithm.
# DWPIs: Dynamic-Weight Parallel Instance and Skeleton Network for Railway Centerline Detection
## 3 Dynamic-Weight Parallel Instance and Skeleton Network
### Instance-Segmentation Branch  
The target of the instance-segmentation branch in our work is to determine the location of the railway. This paper uses SOLOv2 [23] as the base architecture for this part, which converts the location prediction task into a classification task. In SOLOv2, the image is divided into \(S\times S\) cells, resulting in \(S^{2}\) location classes, and each instance can be assigned to one of them as its location category.  
The backbone of SOLOv2 used to extract the feature is the traditional residual neural network (ResNet), which requires deep architectures, making it computationally expensive. This paper uses a more advanced backbone mainly composed of ELAN [25], which is similar to YOLOv7 [26], as shown in Figure 3. ELAN, a layer aggregation architecture with efficient gradient propagation paths, is mainly composed of VoVNet [44] combined with  
Figure 2: Architecture of our network.  
CSPNet [45]. It optimizes the gradient length of the overall network with the structure of a stack in a computational block [25].  
After the backbone, this paper adds the attention module SimAM [27] at each level of the feature map to divert attention to the most important regions and disregard the irrelevant parts. It is a type of channel-spatial-attention module [46] and a significant advantage of it is that it does not introduce additional parameters.  
To assign objects of varying sizes to different levels of feature maps, SOLOV2 employs a feature pyramid network (FPN) [47] as the neck of the framework. SOLOV2 has two heads: the kernel branch which predicts the semantic category and mask kernel, and the feature branch which predicts the mask feature. Therefore, the training loss function is defined as follows:  
\[L_{\textit{ins}}=L_{\textit{cate}}+\lambda L_{\textit{mask}} \tag{1}\]  
where \(L_{\textit{ins}}\) is for the whole instance-segmentation network, \(L_{\textit{cate}}\) is for semantic-category classification, and \(L_{\textit{mask}}\) is for mask prediction.
# Unsupervised 3D Motion Summarization Using Stacked Auto-Encoders
## 6 Experimental Results  
In this section, we present several experiments to demonstrate the performance of the proposed unsupervised 3D motion summarization framework based on a stacked auto-encoder used to reduce the redundant information. The proposed stacked auto-encoder scheme is evaluated over three different dance sequences (see Section 6.2). Each choreographic sequence is executed by two humans, dancing simultaneously. We present several experiments to demonstrate (i) _the encoding capabilities_ and (ii) _the similarity of the automatically selected frames against the ground-truth_.  
As input data we use the ones presented in Section 3.1. That is, we extract for each human joint the relative coordinates and its kinematics, that is 5 elements (3 for the joint coordinates and two for the velocity and acceleration). We recall that we have 40 joints per human dancer. Thus, the total feature space is of dimension 400 (40 joints by 2 dancers by 3 coordinates per joint plus velocity and acceleration).  
Due to the presence of two dancers in the sequences, a severe noise exists. To remove it, we first pre-process the data to exclude some frames which seem to be noisily represented. This is accomplished by just thresholding the differences of the joint coordinates among few consecutive frames. If this difference is greater than a threshold, this implies that a severe difference is noticed among the successive frames revealing an erroneous performance in 3D data encoding. A dancer (and thus his/her joint coordinates) cannot be moved long within the grid space during a choreographic performance. Having refined the captured data from potential noisy inputs, then we feed the features into the proposed SAE scheme to get a compressed input signal where all redundant information will be discarded.  
Once, the stacked auto-encoder (see Equation (4)) is trained, we maintain the encoder part and project the feature values onto a latent space of lower dimension. In our experiments, we keep only 48, out of 400, feature element dimensions. This number has been selected after several experiments since it gives an acceptable performance while retaining the dimension as low as possible. A set of summarization approaches are applied, including the adopted unsupervised representational algorithms, along with other prominent methods such as k-OPTICS and Kennard Stone [29]. The last step of the analysis involves the calculation of similarity scores and the time divergence between the summarized frames and a set of selected key-frames by expert users in traditional dances (ground truth data sets). The former is calculated by the correlation scores between each frame of the original dance sequence to all the frames, provided by the sampling method. A higher score indicates a better match. Time divergence is simply calculated by the difference in frames, which is the same as the difference in times (seconds). In this case, the lower the difference is, the better the summarization performs.
## 4 Results and Discussion
### Experimental Verification
The main reason for this experimental result is the dead zone characteristics of the main control ball valve. When the input signal of the main control valve changes a little, the actuator may not act. In this way, when the output of the controller is small, the opening of the main electric valve cannot be corrected in time, resulting in a large amplitude of pressure fluctuation in the main pipe. On the other hand, when the controller parameter settings are more sensitive, the control system is prone to frequent fluctuations. Therefore, in order to avoid frequent fluctuations in the control system, the controller parameters should be set reasonably. From the analysis of the control response time, the response time of the master and auxiliary composite control is obviously lower than that of adaptive fuzzy PID and variable universe fuzzy PID control. Under the same switching fluctuation, the passing time is shorter, and the response speed is faster. The pipeline pressure control is common, but the research on pressure control in the MCCS system for fire-fighting is comparatively sparse. This paper demonstrates an exhaustive study of the variable universe fuzzy PID controller. Furthermore, the auxiliary controller of the branch pipeline is considered based on the dynamic characteristics of the switching process of the MCCS system. Therefore, a thorough study of the control strategy based on the master and auxiliary composite is illustrated for the main pipe pressure change. Additionally, the valve controller and pressure set point coordinated controller are added to the control strategy so as to apply the switching conditions. Traditional PID, single fuzzy PID and variable universe fuzzy PID can also realize the control of main line pressure. However, the outcomes reveal that the overall performance of the master and auxiliary composite control strategy is superior. The main pipe pressure control studies are examined under different delay times and different loads of the MCCS system. It is observed from the simulation and experiment results that the main pipe pressure with the proposed control strategy achieves excellent outcomes.
## 4 Performance Evaluation
### Detection Performance at [PERSON]'s and [PERSON]'s Ends  
In order to verify the signal security of the designed waveform, detection performance at [PERSON]'s and [PERSON]'s ends will be analyzed in this section. It is assumed that both [PERSON] and [PERSON] use the ED method. However, the under-sampling method was employed at [PERSON]'s end owing to the negotiation with [PERSON], and the sampling clock offset and CSI can be perfectly known. Moreover, the constant false alarm rate algorithm is applied.  
From Equations (14) and (15), we can conclude that  
\[\begin{split} P_{fa}&=\frac{1}{\sqrt{2\pi}\sigma_{ w}}\int_{\Psi}^{\infty}\exp\Bigg{(}-\frac{(Y-\mu_{w})^{2}}{2\sigma_{w}^{2}} \Bigg{)}dY\\ &=Q\bigg{(}\frac{\Psi-\mu_{w}}{\sigma_{w}}\bigg{)}\end{split} \tag{16}\]  
\[\begin{split} P_{d}&=\frac{1}{\sqrt{2\pi}\sigma_{ sw}}\int_{\Phi}^{\infty}\exp\Bigg{(}-\frac{(Y-\mu_{sw})^{2}}{2\sigma_{sw}^{2}} \Bigg{)}dY\\ &=Q\bigg{(}\frac{\Psi-\mu_{sw}}{\sigma_{sw}}\bigg{)}\end{split} \tag{17}\]  
where \(Q(u)\) is Q function defined by \(Q(u)=\frac{1}{\sqrt{2\pi}}\int_{u}^{+\infty}\frac{\exp(x^{2})}{2}dx\). Given a predetermined false alarm probability \(\hat{P}_{fa}\), the decision threshold can be calculated by  
\[\psi^{*}=\sigma_{w}Q^{-1}(\hat{P}_{fa})+\mu_{w} \tag{18}\]  
where \(Q^{-1}(u)\) is inverse function of Q(u). Substituting Equation (18) for Equation (17), we can get \[P_{d} =Q\bigg{(}\frac{\psi^{*}-\mu_{sw}}{\sigma_{sw}}\bigg{)} \tag{19}\] \[=Q\bigg{(}\frac{\sigma_{w}Q^{-1}(\hat{P}_{fa})+\mu_{w}-\mu_{sw}}{ \sigma_{sw}}\bigg{)}\]  
For the intended user Bob, the time-bandwidth product is approximated as \(T_{\text{int}}W=1\) when the under-sampling method is employed. Furthermore, the ratio of instance symbol energy and power spectrum density is  
\[\left(E/N_{0}\right)_{Bob}=\frac{\|\mathbf{H}\|_{2}^{2}E_{s}}{NN_{0}} \tag{20}\]  
where \(E_{s}\) denotes the average symbol energy of \(s(t)\). As a result, the decision threshold at Bob's end is \(\psi_{Bob}^{*}=2Q^{-1}(\hat{P}_{fa})+2\), and the detection probability is  
\[P_{d,Bob} =Q\bigg{(}\frac{\psi_{Bob}^{*}-\mu_{sw}}{\sigma_{sw}}\bigg{)} \tag{21}\] \[=Q\bigg{(}\frac{Q^{-1}(\hat{P}_{fa})-(E/N_{0})_{Bob}}{\sqrt{1+2 \cdot(E/N_{0})_{Bob}}}\bigg{)}\]  
For the illegal user [PERSON], the time-bandwidth product is approximated as \(T_{\text{int}}W=ND\). Furthermore, the \(E/N_{0}\) at Eve's end can be written by  
\[\left(E/N_{0}\right)_{Eve}=\frac{\left|\sum_{k=1}^{N}h_{k}g_{k}\right|^{2}}{ \|\mathbf{H}\|_{2}^{2}}\cdot\frac{E_{s}}{NN_{0}} \tag{22}\]  
where \(g_{k}\) is the channel coefficient of the wire-tap channel that is independent of \(h_{k}\). In AWGN channel, \(g_{k}=1(k=1,2,\cdots,N)\) and Equation (22) is simplified as \(\left(E/N_{0}\right)_{[PERSON]}=E_{s}/(NN_{0})\). Furthermore, the decision threshold at [PERSON]'s end can be given by \(\psi^{*}=2\sqrt{NDQ^{-1}(\hat{P}_{fa})}+2 ND\) and the detection probability can be written by  
\[P_{d,Eve} =Q\bigg{(}\frac{\psi^{*}-\mu_{sw}}{\sigma_{sw}}\bigg{)} \tag{23}\] \[=Q\bigg{(}\frac{\sqrt{ND}Q^{-1}(\hat{P}_{fa})-\left(E/N_{0} \right)_{Eve}}{\sqrt{ND+\left(E/N_{0}\right)_{Eve}}}\bigg{)}\]
## 5 Experimental Result and Analysis
### Effect of Embedding Dimension  
To evaluate the influence of embedding dimension on model performance, experiments were conducted on four steganographic datasets with varying dimension configurations. The embedding dimension is critical for representing informative features and capturing intricate dependencies within the data. By assessing classification accuracy under different dimension settings, the goal is to identify an optimal configuration that achieves a balance between computational efficiency and detection effectiveness. The experimental results are illustrated in Figure 2.  
As illustrated in Figure 2, increasing the embedding dimension generally leads to improved classification accuracy across all evaluated datasets; however, the performance gains tend to plateau beyond a dimension of 20. Specifically, for the Geiser dataset [17], the peak accuracy of 92.46% is observed at dimension 20. For the Miao (\(\eta=1\)) dataset, the highest accuracy (86.68%) is achieved at dimension 35. In the case of Miao (\(\eta=2\)) [18], accuracy continues to increase with larger dimensions, reaching 92.45% at 35. The Miao (\(\eta=4\)) dataset [18] demonstrates the most significant benefit from higher dimensions, achieving 94.79% at 35. Nonetheless, the limited performance improvement beyond dimension 20 indicates that, for less complex datasets, lower-dimensional embeddings can achieve comparable detection performance with reduced computational cost, suggesting that a dimension of 20 offers a practical trade-off.
# Satellite-Observed Global Terrestrial Vegetation Production in Response to Water Availability
###### Abstract  
Water stress is one of the primary environmental factors that limits terrestrial ecosystems' productivity. Hense, the way to quantify gobal vegetation productivity's vulnerability under water stress and reveal its seasonal dynamics in response to drought is of great significance in mitigating and adapting to global changes. Here, we estimated monthly gross primary productivity (GPP) first based on light-use efficiency (LUE) models for 1982-2015. GPP's response time to water availability can be determined by correlating the monthly GPP series with the multiple timescale Standardized Precipitation Evaportranspiration Index (SPEI). Thereafter, we developed an optimal bivariate probabilistic model to derive the vegetation productivity loss probabilities under different drought scenarios using the copula method. The results showed that LUE models have a good fit and estimate GPP well (R2 exceeded 0.7). GPP is expected to decrease in 71.91% of the global land vegetation area because of increases in radiation and temperature and decreases in soil moisture during drought periods. Largely, we found that vegetation productivity and water availability are correlated positively globally. The vegetation productivity in arid and semiarid areas depends considerably upon water availability compared to that in humid and semi-humid areas. Weak drought resistance often characterizes the land cover types that water availability influences more. In addition, under the scenario of the same level of GPP damage with different drought degrees, as droughts increase in severity, GPP loss probabilities increase as well. Further, under the same drought severity with different levels of GPP damage, drought's effect on GPP loss probabilities weaken gradually as the GPP damage level increases. Similar patterns were observed in different seasons. Our results showed that arid and semiarid areas have higher conditional probabilities of vegetation productivity losses under different drought scenarios.  
LUE-GPP; SPEI; copula function; conditional probability Article
## 3 Results
### Between-Days Analysis  
This analysis was performed with two-fold and seven-fold validation schemes. For two-fold validation, each pair of days were tested so that one day was used for training and the other for testing. The result was calculated as the average of all pairs. For seven-fold validation, six days were used for training and one day for testing with seven repetitions. Results for both schemes are shown in Figure 8 as the average of 10 healthy and six amputee subjects.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Healthy iEMG Data**} \\ \hline
**Day** & **D1** & **D2** & **D3** & **D4** & **D5** & **D6** & **D7** \\ \hline
**D1** & - & 5.68 & 6.63 & 6.03 & 5.7 & 5.15 & 6.25 \\
**D2** & 21.69 & - & 5.36 & 4.75 & 4.79 & 3.93 & 4.54 \\
**D3** & 23.42 & 17.93 & - & 5.19 & 5.03 & 4.78 & 5.24 \\
**D4** & 21.96 & 19.5 & 18.16 & - & 5.16 & 5.2 & 5.33 \\
**D5** & 24.68 & 21.03 & 20.52 & 18.98 & - & 3.05 & 3.58 \\
**D6** & 23.59 & 20.19 & 20.96 & 18.73 & 17.66 & - & 3.12 \\
**D7** & 23.85 & 20.53 & 19.03 & 19.65 & 16.55 & 15.9 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Ampute sEMG Data**} \\ \hline
**Day** & **D1** & **D2** & **D3** & **D4** & **D5** & **D6** & **D7** \\ \hline
**D1** & - & 14.56 & 16.23 & 14.4 & 15.47 & 13.21 & 12.18 \\
**D2** & 34.75 & - & 14.32 & 13.51 & 12.96 & 11.54 & 11.48 \\
**D3** & 38.19 & 34.03 & - & 16.4 & 15.84 & 12.73 & 13.31 \\
**D4** & 32.27 & 31.77 & 34.36 & - & 13.49 & 11.83 & 12.11 \\
**D5** & 36.1 & 33.21 & 34.79 & 30.56 & - & 10.34 & 10.78 \\
**D6** & 31.45 & 30.42 & 30.76 & 27.42 & 24.18 & - & 8.57 \\
**D7** & 34.12 & 31.01 & 32.27 & 27.92 & 25.38 & 20.91 & - \\ \hline \hline \multicolumn{6}{c}{**Amputee iEMG Data**} \\ \hline
**Days** & **D1** & **D2** & **D3** & **D4** & **D5** & **D6** & **D7** \\ \hline
**D1** & - & 15.84 & 17.41 & 18.64 & 16 & 16.46 & 15.32 \\
**D2** & 36.15 & - & 14.96 & 16.99 & 13.39 & 13.76 & 13.53 \\
**D3** & 39.66 & 34.25 & - & 16.54 & 13.76 & 13.83 & 13.74 \\
**D4** & 38.16 & 33.58 & 33.91 & - & 15.11 & 15.2 & 14.43 \\
**D5** & 38 & 34.23 & 35.01 & 32.42 & - & 11.79 & 12.78 \\
**D6** & 36.07 & 33.56 & 34.9 & 32.55 & 29.47 & - & 13.16 \\
**D7** & 36.75 & 31.99 & 34.11 & 31.18 & 28.97 & 27.72 & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: SSAE vs. LDA performance and sEMG vs. iEMG data comparison for transradial amputee subjects. In each matrix, the upper diagonal triangle represents the mean classification errors obtained with SSAE and the lower diagonal triangle represents errors obtained with LDA for corresponding pair of days.  
SSAE achieved lower error rates than LDA for both the two-fold and seven-fold validation. As the training data increased from one to six days, the error rate of SSAE decreased by 9.50 and 13.03 percentage points for sEMG and iEMG, respectively. The decrease in error rate with increasing training data was smaller for LDA (6.39 and 5.16). Moreover, in both the two-fold and seven-fold analyses, sEMG patterns proved to be more robust and outperformed the iEMG (\(p<0.01\)).
## 4 Results and Discussions
### Synthesis
#### 4.4.3 On the Random Choice of Operators  
The choice of the best evolutionary operators is a difficult problem as it depends in a nonlinear fashion on the objective function and on the search budget [46]. In many ways, it can be considered most of the research in evolutionary computation deals with this question, and the current study is no exception. When it comes to choosing between several operators, the boundary or the full domain mutation, or the equal weight or no crossover, we found that a simple strategy is robust and well-performing, even though it may not be the overall best: choose randomly between the options while allowing each of them to act alone. The alternating mutation applies at random either the boundary or the full domain mutation. The random crossover has effects that range between those of the equal weight crossover and those of no crossover. Such a randomized choice of operator has the advantage of keeping the possibility that each operator contributes to candidate solutions, sometimes without the addition of the effect of the other operators. This constitutes an element of robustness with respect to changing functions and population compositions. It comes at the cost of missing the best operator tuning for a specific task.
## 2 Measurement Environments and Methods
### Measurement Equipment  
A logarithmic periodic antenna and spectrum analyzer were used for noise measurement. The logarithmic periodic antenna is commercial and has many outstanding advantages, such as wider operating bandwidth and superior gain for all frequencies in the frequency band range. The operating frequency range of this antenna is 300 MHz to 8 GHz. The size of the antenna is \(355\times 497\) mm, and the polarization mode is linear polarization. In actual measurement, the antenna was elevated to 1.4 m high. The antenna was measured in horizontal and vertical polarization modes.  
We conducted the measurements in frequency and time domains. The frequency domain measurement was done to analyze the occupancy of radio spectrum resources. We extracted the central frequency band, bandwidth, and power level of the noise. By using the time domain measurement, the time waveform of the electromagnetic noise was recorded at certain frequency bands. We analyzed the statistical properties in the time domain. The actual measurement environments are shown in Figure 2.  
Figure 1: Real scenes of the three positions. (**a**) Manual spot welder; (**b**) Welder robot; (**c**) Office area.  
Figure 2: Actual scene of the measurement.