esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Validity, Limitations, and Future Work  
Identifying and analyzing the temporal trends in historical observation is an essential aspect of ecological time series analysis. In terms of decomposing the WUE time series, the STL method can well decompose the WUE time series into inter-annual trend and seasonal components and detect a nonlinear change in long-term trends, which could not be accomplished by general linear regression models [19]. As shown by the residual terms in Figures 4 and 7, the residuals are distributed around the mean, indicating that the method extracts credible information on the trend and seasonal terms. The STL method can handle the outliers in the WUE time series and achieve robust and accurate estimates  
Figure 15: The monthly plot of the WUE time series.  
of the trend and seasonal components. The trend component could reveal the process of WUE change over time to reduce the uncertainty of the WUE time series analysis [18]. The effectiveness of the STL decomposition technique has also been demonstrated in several research papers [18; 20; 21].  
Our study illustrated the validity of the Geodetector method in identifying the influence factors responsible for the spatial variation of the WUE. Compared to traditional statistical methods, it can quantify the nonlinear effect of factors and the interaction of pair-wise factors on the spatial variation of WUE [69; 70]. _GD_ package in R, which realized an optimal parameter-based Geodetector method, could avoid tedious parameter setting and conduct more accurate spatial analysis [57].  
WUE is an essential indicator for describing the carbon-water coupling process of ecosystems. Remote sensing-based WUE retrieval could remedy the shortcomings of traditional in situ observations. In ecological studies, numerous indicators can reflect carbon sinks and water consumption. Different indicators selected in the WUE calculations may lead to different results. This is particularly evident in WUE studies at different scales [23], which makes it difficult to compare the results of different studies. The accuracy of MODIS GPP and ET products has been verified by numerous studies [71]. However, there is uncertainty in WUE retrieval due to the effect of the observational means and relatively coarse spatial resolution.  
In this paper, the study area was divided into four sub-areas for geo-detection analysis, which does not adequately reflect the spatial heterogeneity of the impact of the driving factors on the WUE. To investigate the crucial environmental factors of WUE at the pixel scale, a weighted linear combination method could be used to generate the spatial distribution maps of the explanatory power for each factor in future studies.
## 2 Results and Discussion
### Self-Assembly Behavior in Aqueous Solution
#### 2.2.3 Additional Heat-Induced Self-Assembly  
The hexaethylene glycol chain of **UBD** formed hydrogen bonds with water molecules and can undergo dehydration by thermal treatment, resulting in an entropy-driven self-assembly process [24; 25]. To examine this heat-induced self-assembly behavior of the nanoparticles, the temperature dependence  
Figure 3: TEM images of **UBD-1 (a,b)**, **-2 (c,d)**, and **-3 (e,f)**. The right panels (**b,d,f**) show enlarged images of the red dotted squares in left panels.  
Figure 2: (**a**) Size distribution of 20 mM **UBD-1** solution at 20 (blue line) and 90 \({}^{\circ}\)C (red line); (**b**) Size distribution of 10 mM **UBD-1** (red lines), 10 mM **UBD-2** (blue lines), and 10 mM **UBD-3** (green lines) solutions, below (solid lines) and above (dotted lines) the cloud point.  
of the transmittance was measured. For 10, 15, and 20 mM solutions of **UBD-1**, an abrupt decrease in transmittance was detected, with LCST at 71, 51, and 38 \({}^{\circ}\)C, respectively. On the other hand, at concentrations below 10 mM, the transmittance decreased gradually (Figure 4a). It should be noted that similar LCST behavior and concentration dependence were observed for **UBD-2** and **-3**. Namely, in the case of 5, 10, and 20 mM solutions of **UBD-2**, the LCSTs were found at 57, 29, and 26 \({}^{\circ}\)C, whereas for 3, 7, and 10 mM solutions of **UBD-3**, LCSTs at 27, 23, and 23 \({}^{\circ}\)C were observed. At a specific concentration (10 mM) of **UBD-1-3**, the LCSTs followed the order **UBD-3** < **2 < 1**, indicating that longer alkyl chains provide lower cloud points [26; 27] due to hydrophobic effects (Figure 4b). The LCST behavior of **UBD** is shown in Figure 4 and Figure S3, and the LCST values are summarized in Table 1.
# Visualization System for Transparency Requirement Analytics
## 4 TranspVis: The Proposed Visualization Tool for Transparency Analytics
### **TranspVis** Database  
The **TranspVis** database schema shown in Figure 1 includes various tables. Experts validate the database to ensure that all transparency requirements are met (keywords, relationships, etc.). The database contains two validating examples, namely WhatsApp and Amazon Web Services (AWS) conditions. Using **TranspVis** ([[https://transpvis.herokuapp.com/](https://transpvis.herokuapp.com/)]([https://transpvis.herokuapp.com/](https://transpvis.herokuapp.com/)), accessed on on 10 November 2022) or the Django ([[https://transpvis-back.herokuapp.com/admin](https://transpvis-back.herokuapp.com/admin)]([https://transpvis-back.herokuapp.com/admin](https://transpvis-back.herokuapp.com/admin)), accessed on on 10 November 2022) interface, experts can update the database.  
There are three primary tables in the **TranspVis** database: Application, Stakeholder, and information element (data, policy, and process). First, the Application table describes the privacy of the viewed system, such as AWS. It consists of the identifier, name, description, and transparency note (optional). For example, AWS contains the characteristics id = \"A1\", name = \"AWS\", and description = \"Amazon Web Services...\". Second, the Stakeholder table contains information such as stakeholder's identifier, name, description, label, and application it is related to. Third, the information element table shares the same attributes as the Stakeholder Element table. Two tables are considered to illustrate the relationships. The table \"Information Element Association\" describes the relationship between two data elements. It has a unique identifier and a source/target information element. In AWS, for instance, there may be a connection between personal information (information element) and cookies (information element). The \"Stakeholders Information Element Relationship\" table describes the relationship between stakeholders and information elements. It contains the identifier and type of information/stakeholder element. For example, a customer (stakeholder) can provide (relation type) personal information for this application in AWS (information element).  
**TranspVis** is primarily designed for transparency experts. However, our goal is to provide a tool that is simple to comprehend and use for both experts and non-experts. Finally, it can be used to assist a large number of people in making decisions about the use of applications.
# Polling Mechanisms for Industrial IoT Applications in Long-Range Wide-Area Networks
## 3 Materials and Methods
### Polling on Class C  
By using Class C nodes, the overhead of control messages of Class B and the randomness of scheduling inside a beacon window can be overcome. Also, the polling period can be better adapted to the exact number of nodes in the network, disregarding the strict timing parameters of Class B (see Figures 8 and 9), which requires beacon periods to be powers of two. This mode of operation can be positive for nodes that are mains-powered and thus does not need to save battery by sleeping, which reduces the problems of synchronization.  
Figure 7: Solution with multicast polling on Class B.  
The proposed polling mechanisms could be used to implement via software sleeping times in Class C nodes, which would help save energy if needed, but would break compatibility with regular Class C operation in case a downlink out of the scope of the polling system occurs. That is main reason why Class B is still interesting in terms of energy efficiency.  
Again, if nodes and GW have multicast support, groups of size \(n_{ch}\) nodes can be polled simultaneously and respond at the same time but on \(n_{ch}\) different channels. In Figure 9, node \(N_{ch+1}\) represents a node that does not fit into the first group of \(n_{ch}\) devices that can be polled at the same time.  
If the polling time \(T_{poll}\) is defined as the time between polling downlink messages from the gateway to the nodes, it can be calculated as in (7) for any case, as follows:  
\[T_{poll}=\left\{\begin{array}{ll}10\cdot\mathit{To}A_{down}\,&\mathit{ DC}_{up}<\mathit{G}\cdot\mathit{DC}_{down}\\ 10\cdot\mathit{To}A_{up},&\mathit{ DC}_{up}\geq\mathit{G}\cdot\mathit{DC}_{down}\end{array}\right. \tag{7}\]
## 2 GRU-HMM Fault Prediction Model
### GRU Updates the HMM Input State Vector  
GRU, a type of Recurrent Neural Network, addresses the performance limitations of the HMM in handling long-distance dependencies and excels in predicting time-series data. Firstly, the gearbox operation data of the on-board monitoring system of the EMU are input to the GRU as a training set for training, the result of the activation function of the GRU is input to the HMM as the initial fault probability, and the results of the training of the GRU are input to the HMM as the state transition matrix. In conjunction with the experimental testing of the model, the number of hidden layers of the GRU network was set to 2. Due to the large amount of input data, the number of GRU units used in each layer was set to 128. The computational procedure for updating the input of the GRU to the HMM is as follows:  
* Construct an initial failure probability output layer, using gearbox operation monitoring data as training set, calculate the output conditional probability \(P_{i}\in[0,1]\) for eachfault type using the Softmax activation function. The output updates the initial vector of the HMM, \[P_{i}=\frac{V_{i}}{\sum_{t=1}^{n}V_{i}},i=1,2,\ldots,n\] (4) where \(V_{i}\) is the weighting factor for each fault type and \(n\) is the number of hidden states in the gearbox.
* Calculate the reset gate, which calculates the amount of stage information to be forgotten by the eigenvalues and fault type output probabilities from the previous time step, \[R_{t}=\sigma(X_{i}V_{ir}+H_{t-1}P_{ir}+b_{r})\] (5) where \(R_{t}\) denotes the reset vector; \(X_{i}\) denotes the fault eigenvalue; \(H_{t-1}\) denotes the hidden state of the fault type at the previous time step; \(b_{r}\) denotes the weight parameter; and \(\sigma\) is a sigmoid function with the value range of (0,1).
* Calculate the intermediate state values and calculate the proportion of stage information propagated by the quantity product of the reset vector and the fault output probability, \[\overline{H}_{t}=\tanh(X_{i}V_{I}+(R_{t}\odot H_{t-1})P_{i}+b_{h})\] (6) where \(\overline{H}_{t}\) denotes the intermediate state value and \(b_{h}\) denotes the weighting parameter.
* Calculate the update gate, which calculates the amount of stage information to be retained by the fault characterization value and the fault type output probability from the previous time step, \[Z_{t}=\sigma(X_{t}V_{iz}+H_{t-1}P_{iz}+b_{z})\] (7) where \(Z_{t}\) denotes the update vector and \(b_{z}\) denotes the weight parameter.
* Calculate the output value, which is used as the state transition matrix to update the parameters of the HMM, \[H_{t}=Z_{t}\odot H_{t-1}+(1-Z_{t})\odot\widetilde{H}_{t}\] (8) where \(H_{t}\) denotes the output state vector at time \(t\).
## 2 The Proposed Method  
### Bidirectional Gate Recurrent Unit  
RNN is a special neural network structure. Because of its memory function of historical information, it has unique advantages in dealing with time-series problems and has received great attention in recent years. However, a problem that cannot be ignored in traditional RNN is the disappearance and explosion of the gradient. LSTM, as an upgraded version of RNN [33], can better alleviate the problems of gradient disappearance and gradient explosion by turning gradient multiplication into gradient addition. By merging the forget gate, input gate, and output gate into a reset gate and update gate, GRU, as depicted in Figure 1, makes further improvements on the basis of LSTM performance [34]. GRU can achieve a similar prediction performance to LSTM but with a simpler structure and fewer parameters, which greatly improves the training speed.  
The reset gate in GRU determines how to combine the new input information with the previous memory through the activation function \(\sigma\), which is calculated as  
\[r_{t}=\sigma(W_{r}\cdot[h_{t-1},x_{t}]) \tag{1}\]  
where \(W_{r}\) indicates the weight from the input layer to the hidden layer of the reset gate, \(h_{t-1}\) is the hidden layer at time \(t-1\), and \(x_{t}\) indicates the input data at time \(t\).  
Candidate hidden layer status \(\tilde{h}_{t}\) contains the input information of time \(t\) and selective retention of hidden layer state \(h_{t-1}\). The calculation formula is  
\[\tilde{h}_{t}=\tanh(W_{h}\cdot[r_{t}\cdot h_{t-1},x_{t}]) \tag{2}\]where \(W_{h}\) is the weight of the hidden state.  
The update gate defines the amount of previous memory saved to the current time step. The update gate calculation formula is  
\[z_{t}=\sigma(W_{z}\cdot[h_{t-1},x_{t}]) \tag{3}\]  
where \(W_{z}\) indicates the weight from the input layer to the hidden layer of the update gate.  
Finally, the hidden state decides how to combine a past hidden status and the current candidate information. The calculation formula is  
\[h_{t}=(1-z_{t})*h_{t-1}+z_{t}*\bar{h}_{t} \tag{4}\]  
The standard GRU network only considers the forward influence of historical information on future time series, overlooking the potential implicit information that may exist during network communication. To address this limitation, the BiGRU [35] model is introduced as a modification to the conventional GRU. By processing sequences of observations from both forward and reverse directions, as illustrated in Figure 2, the BiGRU network gains additional insights into the sequence. The primary objective of this approach is to extract maximal information from a given subsequence of observations, thereby providing an enhanced reference value for RUL prediction. Consequently, this paper adopts the BiGRU model in order to achieve this goal.  
Figure 1: Basic GRU logical structure diagram.  
Figure 2: Structure diagram of building BiGRU.
# An Overview of CubeSat Missions and Applications
## 9 Review Conclusions and Discussion
### Chemical and Biological Experimentation in Space Using CubeSat Platforms  
In recent years, CubeSats have been also used as platforms for in-space chemical and biological experimentation, given their low-cost and autonomous operation. Insights provided by these studies can help the understanding of the impact of prolonged space exploration.  
Several missions have conducted biological experimentation, demonstrating how microorganisms, such as _E. coli_ and yeast respond into microgravity conditions. These pioneering missions collected useful data regarding gene expression, microbial growth, as well as drug resistance in space, with direct implications for human health during spaceflight. Other missions focused on the survivability of bacteria in long-term missions, confirming their enhanced resistance in space environment. This poses a potential challenge that needs to be investigated for future missions. Equipped with lab-on-chip technologies, other CubeSats have demonstrated their competence as compact biological research platforms. Finally, CubeSats have also been used to study how space radiation and cosmic rays impact microorganisms' DNA as well as how they influence the degradation of organic molecules.  
This type of CubeSat mission may be of the greatest importance regarding the future of space exploration. Understanding how the space environment impacts microorganisms, DNA expression, the degradation rate of molecules, and drug resistance will play a significant role in creating compensation strategies, making the overall space missions safer for humans. Any upcoming mission for prolonged time periods should incorporate all the insights from these experiments as well as strategies to mitigate the observed challenges.
## 4 Experimental Results and Analysis
### Computational Complexity  
We further evaluate the proposed model by comparing it with other contrastive methods. We compare different models in terms of parameters, epochs, and inference time.  
As shown in Table 1, the proposed LCANet SHM method has the highest accuracy among other contrastive methods in damage identification. In terms of parameters, 1D-CNN has more than 1.44 million parameters, which use one-dimensional sequences as model inputs. 2D-CNN has more than 23.15 million parameters, which is the highest among the four models. As two-dimensional convolution operation requires far more parameters than one-dimensional convolution, 2D-CNN has more parameters using two-dimensional data as input. The ResNet designed with the residual structure has 11.37 million more parameters, which is about half of the 2D-CNN. However, the proposed LACNet only has 59,285 parameters, less than 1/24 of that of 1D-CNN. In addition, LACNet requires only eight training sessions for accurate recognition, while 1D-CNN requires at least 40 sessions to reach the same recognition level. This is because one-dimensional convolution filters have limited coverage and poor feature-extraction ability for longer sequences. In terms of inference speed, LCANet is much faster than 2D-CNN and ResNet, but a little slower than 2D-CNN. There are two reasons for this: First, the use of group convolution and depthwise convolution can be used to effectively reduce model parameters. However, it has a reduced feature-extraction capability and requires more channels to improve performance. In general, the inference speed of LCANet is acceptable. Having only trained eight epochs,  
Figure 12: Visualization of model output.  
the proposed LACNet method can reach 100% recognition accuracy, which is 80%, 29.7%, and 53.3% higher than 1D-CNN, 2D-CNN, and ResNet, respectively.  
In summary, the proposed SHM method is an effective and efficient method for damage detection in a composite structure.
# Acoustic Backscatter Communication and Power Transfer for Batterless Wireless Sensors
## 4 Evaluation
### Larger Structures  
Prior work--including the test presented in the above sections--was only evaluated on small and isolated structures, e.g., small plates or pipes. In this work, we investigate the potential of wireless acoustic energy transfer on more realistic real-world structures. We, therefore, attached several transducers to a steel beam supporting a crane shown in Figure 17, with transducer distances of 1, 2, 3, 4, and 5 m. We used our reader to generate a carrier wave and measured the signal amplitude at the different transducers.  
Compared to the smaller specimen we used in the previous evaluations, the amplitudes are smaller in the large beam. When choosing the resonant frequencies, amplitudes in the large beam reach up to 400 mV over 1 and 2 m distances and still about 300 mV at 5 m. However, the observed amplitudes also suggest that the acoustic waves experience little attenuation when traveling through the metal beam, i.e., a fivefold distance increase from 1 m to 5 m only corresponds to a 25% decrease in amplitude.  
Unfortunately, the wirelessly transmitted power over this large structure cannot charge our prototype tag fully. However, several improvements can still enable batteryless tags on such large structures. First, increased sender powercan provide sufficient energy. Second, the used transducers and their frequency band do not fit well to the large beam, with a thickness of more than 2 cm. Larger transducers combined with lower carrier frequencies likely improve the efficiency of the wave excitation strongly. Third, the harvester can also be optimized to work with less available power in a duty cycle, as discussed in Section 4.1.2. In essence, larger structures may require more transmit power by the reader, but once a strong enough wave is excited in a structure, it can cover large distances.  
Figure 16: Using simulated signals, the influence of the channel impulse response on the output noise of the decoder is characterized (**left**), where IR1 is the recorded channel impulse response from the 3 m channel at \(f_{\mathrm{c}}=225\) kHz, and IR2 is from the 2 m channel at \(f_{\mathrm{c}}=210\) kHz. Furthermore, the PLL gain is an important parameter to choose depending on SFE variability (**right**). The simulation generated signals with an SNR of 15 dB during this evaluation.  
Figure 17: Experiment on a large structure with transducers placed at distances 1, 2, 3, 4, and 5 m. The graph shows the amplitudes of the ten best frequencies over different distances.
## 3 Problem Formulation  
### Optimization Problem  
We aim for a joint solution optimizing for the user association and transmit power allocation with the objective of maximizing energy efficiency, which is defined as the achievable sum rate per consumed power, in the downlink of the two-tier HetNet while considering QoS guarantee and wireless backhaul link capacity constraints. The problem can be formulated as  
\[\max_{\{c_{jk}\},\{P_{j,k,f}\}} \frac{1}{P_{T}}\sum_{k=1}^{K}\rho_{k}\] (4a) subject to \[C_{1}:\sum_{j}c_{jk}=1,\;c_{jk}\in\{0,1\},\;\forall j\in\mathcal{ J},\;k\in\mathcal{K} \tag{4b}\] \[C_{2}:0\leqslant\sum_{k\in C_{j}}\sum_{f\in\mathcal{F}_{k}}P_{j,k, f}\leqslant P_{SBS_{j},max},\;\forall j\in\mathcal{J},\;k\in\mathcal{K}\] (4c) \[C_{3}:\rho_{k}\geqslant\
u_{k},\;\forall k\in\mathcal{K}\] (4d) \[C_{4}:|\;\mathcal{C}_{j}\;|\leqslant|\mathcal{C}_{j}|_{max},\; \forall j\in\mathcal{J}\] (4e) \[C_{5}:\rho_{j}^{SBS}\leqslant R_{j}^{SBS},\;\forall j\in \mathcal{J} \tag{4f}\]  
\(C_{1}\) in (4b) assumes that each UE is served by only one SBS, and \(C_{2}\) in (4c) refers to transmit power limit at the \(j\)th SBS with \(P_{SBS_{j},max}\) the maximum power available at the \(j\)th SBS. \(C_{3}\) in (4d) indicates the QoS requirement for each UE, where \(\
u_{k}\) is the capacity threshold for UE \(k\). \(C_{4}\) in (4e) is the cluster size constraint with \(|\mathcal{C}_{j}|_{max}\) the maximum allowable number of users in \(\mathcal{C}_{j}\). This ensures that UEs in the same cluster are assigned different subchannels to avoid intra-cluster interference. \(C_{5}\) in (4f) indicates the backhaul link capacity constraint, where \(R_{j}^{SBS}\) is the maximum achievable downlink data rate for SBS \(j\). Note that the subchannel assignment is assumed to be known and is not considered in this work.  
The strategy in (4a) attempts to maximize the energy efficiency by finding the optimal user association and power allocation, which is generally a challenging problem with various unknowns and hybrid unknown spaces (continuous power and discrete clustering) in the system. Furthermore, the optimization problem in (4a) deals with a _one-shot_ scenario at a certain time instant which needs to be re-evaluated when the network evolves to the next time instant. To tackle the challenges, we are therefore motivated to resort to the techniques of reinforcement learning (RL).