esa-sceva/satcom-chunk-collection huggingface dataset:
# _Pat-in-the-Loop_: Declarative Knowledge for Controlling Neural Networks
## 3 The Model
### Visualizing Activation of Parse Trees  
Distributed tree encoders give the possibility of using _heat parse trees_ to visualize the activation of parse trees in final decisions or intermediate neuron outputs.  
To compute of _active trees_\(\overline{t}\) useful to produce _heat parse trees_, a neural network should be sliced at the desired layer. Let NN be the sliced neural network, \(\mathbf{x}=\mathbf{x}^{\mathcal{T}},\mathbf{x}^{r}\) and \(\mathbf{o}\) its output:  
\[\mathbf{o}=\text{NN}(\mathbf{W_{dt}}\mathbf{x}^{\mathcal{T}},\mathbf{x}^{\mathcal{T}})\]  
where, given an example \(x\), \(\mathbf{x}^{\mathcal{T}}\) is the vector representing the tree \(\mathcal{T}\) in the space of subtrees related to the example \(x\), \(\mathbf{W_{dt}}\) is the distributed tree encoder, and \(\mathbf{x}^{r}\) is the rest of the features associated to \(x\).  
Using parse trees \(\mathcal{T}\) in neural networks is straightforward with distributed trees. In fact, distributed trees \(\mathbf{y}^{\mathcal{T}}=\mathbf{W_{dt}}\mathbf{x}^{\mathcal{T}}\) for parse trees \(\mathcal{T}\) may be directly used in neural networks as these distributed trees are vectors.  
Our heat parse trees show the overlap of activation of subtrees in \(S(\mathcal{T})\) of specific trees \(\mathcal{T}\) related to a specific example \(x\) in a specific net. This shows how subtrees in \(S(\mathcal{T})\) contribute to the final activation \(o_{i}\), that is, a dimension of \(\mathbf{o}\). We believe this is more convenient than representing an extremely large heatmap for the list of subtrees in \(S(\mathcal{T})\) and their related value \(o_{i}\) (see Table 2).  
The computation of active trees \(\overline{t}\) for displaying heat parse trees is the following. The activation weight \(v_{r}\) of each node \(r\) represents how much the node is responsible for the activation of the overall syntactic tree for the output of the given neuron \(o_{i}\). Then, the activation value \(v_{r}\) is computed as follows:  
\[v_{r}=\sum_{\tau\in S(\overline{t})\text{ and }r\in\tau}\text{NN}(\mathbf{W_{dt}} \lambda^{\frac{|\tau|}{2}}\mathbf{\tau},\mathbf{x}_{r})\]  
where \(\mathbf{\tau}\) is the one-hot vector in the subtree space that indicates the subtree \(\tau\) and \(r\in\tau\) detects in \(r\) is node in \(\tau\).  
With the above computation of \(\overline{t}\), active subtrees \(\tau\) for the output \(o_{i}\) of a specific neuron are overlapped in single heat parse trees.  
The activation value can be calculated in other ways, for example using Layer-wise Relevance Propagation (LRP) [34]. They compute activation value \(v_{r}\) in _active tree_\(\overline{t}\) by using LRP, that is a framework to explain the decisions of a generic neural network using local redistribution rules and is able to explain which input features contributed most to the final classification. This method unfortunately does not allow you to split the network at the desired layer, so it has not been taken into account.
## 6 Moving Cell Based on C-RAN
### C-RAN Architecture for Vactrain  
C-RAN can provide a clean, centralized processing, collaborative radio, and real-time cloud computing infrastructure wireless access architecture by combining the centralized software-defined radio baseband, high-speed optical transmission network, and distributed remote wireless module (RoF, Radio-over-Fibre) [45].  
C-RAN is considered as a promising solution to provide high communication services for passengers in vactrain scenario. Figure 6 shows the C-RAN architecture in vactrain scenario [42]. The architecture can be divided into three key components: Remote Radio Units (RAUs), optical transmission network, and the pool of baseband units (BBUs) in a datacenter cloud. All of the BBUs, together with the RAUs, can be connected by the optical network with low-latency.The RAUs, each connected to a segment of waveguide, are only responsible for the radio frequency (RF) transceiving and belongs to none of the fixed BBUs. The base band pools centralize all the digital baseband processing capabilities, which are actually a huge computing resource group.  
The key concept of C-RAN is to separate the RAUs from the baseband stations and centralize the latter to a centralized entity, namely, base band pools. Based on this, the design of RAUs could be greatly simplified, thus allowing them to be deployed in large numbers for small cells along the vacuum tube line. The leaky waveguide solutions are adopted to achieve wireless coverage inside the tube, thereby each RAU outside the tube needs to be connected to the corresponding segment of leakage waveguide.
## 4 Network Model
### System Interoperability  
There are several MTC solutions that have been developed in order to address a certain demands and services. This has resulted in a dispersion of the technical solutions, which make current M2M markets are much segmented [19].  
Therefore, in order to have interoperability between MTC applications and cellular networks service, the European Telecommunications Standards Institute (ETSI) has established a technical committee that focus on M2M Service. There are two reference terminologies for MTC: the 3 GPP--MTC and IEEE 802.16p, which include enhancements to support machine-to-machine communications (M2M). Both focus on improving network access and core networks. These two schemes are complement to M2M service by ETSI. Therefore, it is possible to combine M2M service by ETSI with any M2M from 3 GPP or IEEE, which result in a cellular-centric M2M architecture. In addition, in order to avoid market dispersion and reduce unnecessary and arbitrary standardisations, the \"oneM2M\" Partnership Project was founded in 2012, the focus is to develop \"one\" standard for M2M and IoT specification and on consolidating M2M services and standard activities into one unified framework, known as oneM2M [37].
## 7 Cellular Permeability, Proton Motive Force, and Persistence
This disruption of membrane permeability was found to potentiate the activity of bactericidal antibiotics --ampicillin, ofloxacin, and gentamicin--while sensitizing _E. coli_ to vancomycin, a Gram-positive-specific antibiotic. Furthermore, silver was able to enhance the activity of gentamicin in a mouse biofilm infection model [95]. Silver was also observed to potentiate gentamicin in the presence of CCCP, suggesting that this effect is not PMF-dependent [94]. Taken together, these findings suggest that disrupting membrane permeability of metabolically dormant biofilm cells may be capable of expanding the spectrum of activity of current antibiotics [94; 95]. Similar effects have also been observed when aminoglycos are administered after a hypoionic shock; however, the exact mechanism and therapeutic potential of this strategy is currently unclear [83]. To date, most of the studies linking cellular permeability to antibiotic tolerance in biofilms and persisters have been performed in vitro. As a result, the clinical relevance of this phenomenon is currently unclear. However, utilization of next-generation sequencing and its integration with older technologies may enable researchers to shed light on the role that cellular permeability and drug uptake play during antibiotic treatment of an infection. For example, fluorescently labeled antibiotics have been used in numerous studies to measure cellular drug uptake in vitro [75; 77; 90]. It may be possible to utilize these compounds to measure drug uptake within a population of bacteria isolated from an infection model in vivo. Furthermore, their use could allow researchers to sort bacteria using fluorescence-activated cell sorting (FACS) based on their level of drug uptake and perform transcriptomic analysis on the resulting populations. Additionally, the use of animal models would enable researchers to manipulate host metabolism to determine its impact on bacterial drug uptake and efflux during infection and antibiotic therapy.
## 5 D-MSR Management Phases
### Coping with Dynamicity, Reservation Conflict and Interference in the Network (Phase-6)
#### 5.6.2 Handling the Resource Reservation Conflict  
In D-SAR protocol, the two nodes of an edge participating in end-to-end connections, negotiate to reserve a common unused timeslot-channel cell based on their current two-hop neighborhood schedule-matrix. By considering the intentional delay before forwarding the _Setup_ message, we allow their neighbors to update their schedule-matrix based on the new reservation. However, there is still a probability that a conflicting edge may also choose that cell, prior to receiving the new advertisements listing the changes in their neighborhood schedules. The nodes of the conflicting edges that have reserved the same cell should handle this conflict upon detection (the detection is done by observing the constant packet loss in that cell), by releasing the conflicting reserved resources. As a response, the end-to-end connection manager in the node transmits _Release_ messages toward the end-points of the connection that include the conflicting cells. Figure 16 illustrates these two potential reservation conflicts scenarios. In the first scenario, when the _Setup_ message (e.g., for traffic a) is being forwarded along the path, the same cell is chosen by edge (E, I) and its interfering edge (N, M). That is because node N did not receive the node I advertisement to update its schedule-matrix based on the new reservation on edge (E, I). This possible conflict is avoided in D-SAR protocol by the considered intentional delay in forwarding the _setup_ message. The second conflict happens, when two setup messages (that belonged to two different end-to-end resource reservation) choose the same cell simultaneously in conflicting edges (I, N) and (O, P).  
Figure 15: Releasing the allocated communication resources after node failing.
# Data Fusion in Agriculture: Resolving Ambiguities and Closing Data Gaps
## 4 Conclusions  
Considerable effort is being made toward finding effective ways at dealing with the wealth of data that are currently being generated in the agricultural environment. Considerable progress has been made, but there are many factors that still prevent techniques that are based on data fusion from being more widely employed in practice. Three of these factors appear to be particularly relevant. First, although large amounts of data are being generated, the complexity of the agricultural environment is such that the databases being generated are not enough to cover all variabilities found in practice. As a result, models and methods proposed in the literature tend to fail under real practical conditions. Second, as the datasets used in research almost invariably have some gaps, the results reported on in scientific articles need to be weighed against those limitations. Unfortunately, this is seldom the case, and often those same results serve as the basis for the development of technologies that will likely fail. Third, even if the technology is robust enough for practical use, there are many technological, economic, political, social, and environmental barriers that prevent its adoption [135].  
Despite these hurdles, the potential for growth is still there. In addition to technical advancements, there is some progress towards improving the quantity and quality of the data being collected. The practice of data sharing is steadily growing, with many research groups making datasets available under the findable, accessible, interoperable, and reusable (FAIR) principles [1; 136]. When data come from a variety of sources, the representativeness of datasets tend to be much better. Additionally, \"citizen science principles\" [137; 138]--calling for the involvement of individuals outside of the research community, in effort to build datasets--are being applied across different disciplines, with encouraging results. Once a technology truly brings benefits to potential users, adoption barriers tend to weaken. As a result of all of these efforts, the gap between academic research and practical adoption will likely continue to decrease.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Research on the Trusted Traceability Model of Taishan Tea Products Based on Blockchain
## 4 Analysis of Blockchain-Based Taishan Tea Traceability Model
### Data Hierarchical Transmission Strategy  
When an enterprise uploads data, it divides the key information into uplinked data and non-uplinked data, in which the uplinked data are divided into two levels, i.e., level 1 data and level 2 data, and the uplinked data are referred to as CH-D\({}_{\text{i}}\), level 1 data are referred to as CH-D\({}_{\text{1}}\), and level 2 data are referred to as CH-D\({}_{\text{2}}\) for ease of description. When the data are uploaded, priority is given to selecting the level 1 data to be uplinked. Pack the level 1 data of each link as P. Each data packet P contains n pieces of data. Then, we construct these n pieces of data into a Merkle tree.  
First, each datum is computed by the SHA256 hash function to obtain N leaf nodes, where Node\({}_{\text{i}}\) has the value of SHA256 (CH-D\({}_{\text{i}}\)). Secondly, the neighbor nodes Node\({}_{\text{1}}\) and Node\({}_{\text{2}}\), their parent nodes Node\({}_{\text{[1,2]}}\), and the value of Node\({}_{\text{[1,2]}}\) is Hash\({}_{\text{[1,2]}}\) = SHA256(Hash\({}_{\text{1}}\mid\mid\)Hash\({}_{\text{2}}\)). According to the above method, Node\({}_{\text{[3,4]}}\), Node\({}_{\text{[5,6]}}\), etc., are continuously generated. Next, for the leading nodes Node\({}_{\text{[1,2]}}\) and Node\({}_{\text{[3,4]}}\), their parent node Node\({}_{\text{[1,4]}}\) is generated upwards, and the value of node Node\({}_{\text{[1,4]}}\) is Hash\({}_{\text{[1,4]}}\) = SHA256(Node\({}_{\text{[1,2]}}\mid\mid\)Node\({}_{\text{[3,4]}}\)). According to the above method, keep generating Node until Node\({}_{\text{[i,i-3]}}\). Finally, the N leaf nodes are synthesized into a root node.  
After the first-level data transmission of each planting and production link is completed, the second-level data of the link will be transmitted, which can ensure the completeness and accuracy of the data, and, at the same time, avoid confusion and errors in the data transmission process, as well as avoid data blocking.
## 3 Dynamic Modeling of Servo System
### Transmission Error Model  
In the study of transmission errors, spectrum analysis and order spectrum analysis are important means to analyze the composition of transmission errors, which can obtain the components of transmission errors at different frequencies or orders. The actual transmission error spectrum contains octave components of several dozen orders, and although the transmission error amplitude decays rapidly with the increase of the order, the phenomenon of the fluctuation of transmission error with the angular position cannot be accurately described by using the superposition of the second order harmonics only. Therefore, in this paper, the periodic fluctuation of transmission error is expressed by the following equation  
\[\theta_{e}=\sum_{i=1}^{n}A(i)\cos(2\pi f(i)\frac{\theta_{l}}{\dot{\theta}_{l}} +\phi(i)) \tag{7}\]  
where \(f\) is the frequency of transmission error in the angular position domain, \(A\) and \(\phi\) are the amplitude and phase of transmission error, respectively, and \(n\) is the number of transmission error principal components.  
The following commutation relationship exists between the angular position domain frequency \(f\) and the order \(E\)  
\[E=\frac{2\pi f}{\dot{\theta}_{l}} \tag{8}\]The transmission error model can be obtained by associating (7) and (8)  
\[\theta_{e}=\sum_{i=1}^{n}A(i)\cos(E(i)\theta_{l}+\phi(i)) \tag{9}\]  
By substituting the order for the angular position domain frequency, the transmission error model does not contain the angular velocity term of the system, avoiding the influence of velocity acquisition noise on the transmission error model.  
By modeling the linear mechanism and nonlinear factors such as friction, backlash, and transmission error, the dynamic model of the ISP servo device is obtained as shown in Figure 2.
# Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring
## 5 Data Fusion for Site-Specific Crop Parameter Monitoring  
A wide variety of data sources collected by modern technology, such as sensor technology and ground/space-borne/aerial platform outcomes, could be used as supplementary information to enhance a lightly sampled variable. However, a data fusion method could benefit the supplementary features of different data sources and merge them into a logically rapid manner [187]. Data fusion can then support the improvement of recognition of site-specific variables, such as N, chlorophyll, LAI, and AGB.
phyl is analyzed using extract-based and digestion or combustion methods, or a fusion of two [199].  
The fusion of a low-cost multispectral sensor using vegetation indices (Vis) and near infrared (NIR) can precisely estimate the chlorophyll content of a diverse set of crops (Table 1). It further has the unique ability to use hyperspectral sensors with the fusion of thousands of spectral bands in between each other, which showed highly effective performance in chlorophyll estimation [200]. [PERSON] et al. [201], [PERSON] et al. [202], [PERSON] et al. [203], [PERSON] et al. [204], and [PERSON] et al. [205] have significantly contributed to the development of chlorophyll estimation methods using hyperspectral sensors integrated with spectral bands (Table 2).  
The fusion of images acquired by a UAV merged together with different sensors is a widely accepted trend in recent years because the data fusion of images develops chlorophyll estimation by integrating benefits of diverse spectral, spatial, structural and thermal data available in different sensor technologies [205]. For example, the fusion of spectral and LiDAR structural information was studied for chlorophyll estimation. However, the fusion of spectral index data collected from multispectral sensors and canopy temperature data collected from thermal sensors is considered an improved method. This method could be used for either a single or several crops and is considered the best choice for chlorophyll analysis [204].
# Analysis of BDS-3 Onboard Clocks Based on GFZ Precise Clock Products
## 2 Methods and Data Collection
### Preprocessing for Clock Offsets  
Considering that the precise clock offsets are daily estimated and released, the preprocessing of clock offsets for each satellite is conducted day by day. In this paper, the detection and elimination of outliers are divided into two steps. First, the clock offset data are transformed into frequency data. The frequency data are computed by the following equation:  
\[f_{i}=\frac{L_{i+1}-L_{i}}{\Delta t} \tag{1}\]  
where \(f_{i}\) denotes the frequency data at the epoch \(i\); \(L_{i+1}\) and \(L_{i}\) denote the clock offset data at the epoch \(i\) and at \(i+1\); and \(\Delta t\) denotes the time between epoch \(i\) and \(i+1\). Second, the median absolute deviation (MAD) is applied to detect the outliers. Assuming that the clock frequency series is \(\{f_{i}\}\), \(i=1,\ 2,\ \dots,\ N,N\) is the number of the daily frequency series. A deviation limit in terms of the MAD is described as follows [19]:  
\[\mathrm{MAD}=\mathrm{Median}\left\{\frac{\left\lfloor f_{i}-m\right\rfloor}{0. 675}\right\}m=\mathrm{Median}\{f_{i}\} \tag{2}\]  
where \(m\) is the median value of the clock frequency series. The MAD estimator is approximate to the standard deviation for normally distributed data. If the clock frequency value is outside the range of \(\left[m\ -n\times\mathrm{MAD},\ m+n\times\mathrm{MAD}\right]\), it will be marked as an outlier. The integer \(n\) is set as 6 in this paper.
# Flow Duration Curves from Surface Reflectance in the Near Infrared Band
## 2 Materials and Methods
### Methods
#### 2.2.2 FDC Estimation  
The FDC is an information on the percentage of time that a discharge is equally or exceeded during a reference period, which is, in other terms, an empirical flood frequency distribution of the discharge record. Thus, for each site, the discharge time series are sorted in descending order assigning a corresponding exceeding probability to each value calculated as a percentage of the observation period. Similarly, the same ranking and assignment is done to the C/M series extracted from MODIS images. In this latter case, we refer to the reflectance ratio duration curve, RDC. Because the intent of the current investigation is to evaluate the existence of the potential correlation among RDCs and FDCs, through the probability axes, we assume that, at each site, the discharge and the reflectance ratio are linked to the same exceedance probability. Fitting this link, we can estimate the discharges even if they are no longer available in time, only using the C/M time series. The validity of such assumption is investigated comparing both the discharge temporal series and the FDCs based on observed records and those simulated through C/M values. Figure 3 shows the concept: Panel 3 (a) underlines how RDCs and FDCs are linked by the same frequency axes. This relationship is built in the period of calibration. Panel 3 (b) and 3 (c) show the use of the procedure in the validation period, when the river discharges are not available but can be estimated assuming the same frequency of occurrence of C/M values. Specifically, the analysis is divided in a calibration and validation period. In order to have robust FDCs, a period of 13 years from January 2003 to December 2015 is used for calibration purpose, while the remaining 4 years, from January 2016 to December 2019, are considered for validation.  
Figure 3: Conceptual scheme of the procedure between ground-based and remote sensing data. (**a**) schematic representation of the relationship between Discharge Q and remote sensing data, C/M through their duration; duration curve in terms of remote sensing, C/M (**b**) and discharge Q (**c**).