esa-sceva/satcom-chunk-collection huggingface dataset:
# Industrial IoT Monitoring: Technologies and Architecture Proposal
## 2 Technologies for Industrial IoT
### IWSN Reports  
IWSN standard technologies make device and network state information available to a variety of entities, e.g., network neighbours or a central management device. In general, this information is shared between nodes to compute routes, allocate bandwidth, calculate link costs between network devices, or generate alarms when critical events occur (e.g., link failure, route failure, low battery level, etc.). This subsection presents the network and data link layer reports available in each of the standards being considered in this paper [12; 13; 14; 15], and describes the context in which the reports are used. Table 2 presents a summary of the referred reports, and may be used as guidance for the discussion. The ZigBee standard comprises three report types that are used for sharing network- and node-related information: (1) link status report; (2) network status report, and (3) network report. Link status reports share sensor node's neighbours incoming and outgoing link costs. The report is broadcasted by ZigBee coordinators and routers in one-hop fashion. This report is useful during network discovery, to find neighbour devices, and in the operation phase for updating the devices' neighbour table. Network status reports are sent by devices in order to report errors and other events that arise at the network layer. The report can be sent in unicast or broadcast modes over the network, and can only pertain to an event at a time. The list of the possible reported events is presented in the Table 2. Last but not least, network reports allow a device to report network events, like PAN conflict, or the radio channel condition to the coordinator. As limitation, in the tree topology, some of these packets may not be received by the ZigBee coordinator, due to the presence of routing devices, that make their own routing decisions. On the other hand, when using a star topology, all the packets will be received at the ZigBee coordinator. Contrary to what happens in ZigBee, in WirelessHART, the network manager controls all communication in the network, and only authorizes new services if resources are available. Consequently, field devices (here with full network capabilities) must share node state and network based information with the network manager. In WirelessHART, all network reports arrive to
# Indistinguishability Operators Applied to Task Allocation Problems in Multi-Agent Systems
## 5 Possibilistic Task Allocation
### Possibilistic Multi-Robot Task Allocation  
Formally, the problem to solve could be defined as follows. Let \(l,m\in\mathbb{N}\), \(R\) be the set of robots with \(R=\{r_{1},...,r_{l}\}\), and \(T\) be the set of tasks to carry out with \(T=\{t_{1},...,t_{m}\}\). Both, tasks and robots are placed in an environment.  
According to the classical RTMs (see Section 2), for each robot \(r_{k}\) and for each task \(t_{j}\), a stimulus \(s_{r_{k},t_{j}}\in\mathbb{R}\) that represents how suitable \(t_{j}\) is for \(r_{k}\) is defined. Besides, a threshold value \(\theta\) is assigned to each robot \(r_{k}\). Thus, a robot \(r_{k}\), allocated at task \(t_{i}\), will select a task \(t_{j}\) to execute with a possibility \(E_{\frac{1}{T_{Dom}^{\frac{1}{\theta}}}}\ (t_{i},t_{j})\) according to a fuzzy Markov decision chain. In the following, the stimulus of each robot \(r_{k}\) to transit from task \(t_{i}\) to task \(t_{j}\) only depends on the distance between the tasks that will be denoted by \(d(t_{i},t_{j})\). So, the stimulus of each robot \(r_{k}\) to transit from \(t_{i}\) to task \(t_{j}\) is given as follows.  
\[s_{t_{i},t_{j}}=\begin{cases}\frac{1}{d(t_{i},t_{j})}&\text{if }d(t_{i},t_{j})\
eq 0\\ \infty&\text{if }d(r_{k},t_{j})=0\end{cases}. \tag{10}\]This stimulus \(s_{t_{i},t_{j}}\) allows us to obtain, by means of the indistinguishability operator (6), the following possibilistic transition function (response function),  
\[p_{ij}=E_{T_{Dom}^{\frac{1}{2}}}(t_{i},t_{j})=\frac{\theta^{n}}{\theta^{n}+d(t_{ i},t_{j})^{n}}. \tag{11}\]  
If the same stimulus (distance) is applied to the indistinguishability operator (8), then the following possibilistic transition function (response function), is obtained:  
\[p_{ij}=E_{T_{AA}^{\frac{1}{2}}}(t_{i},t_{j})=e^{-\frac{d(t_{i},t_{j})^{n}}{ \theta^{n}}}. \tag{12}\]  
From now on, we will reference the response function given by (12) as the Exponential Possibility Response Function (EPRF) and the response function given by (11) as the Original Possibility Response Function (OPRF). Furthermore, as the transition functions EPRF and OPRF are indistinguishability operators. We will use both terms--transition possibility and indistinguishability operator--equally.  
As was proved in [12] (see also [10]), when either response functions (or indistinguishability operators), (11) or (12), are used as a possibility transition, the obtained fuzzy Markov chain holds the Duan's convergence requirements. Therefore, we can ensure that the system converges to a stationary state in at most \(m-1\) steps. It must be recalled that, in general, the convergence of the probabilistic Markov chains is only guaranteed asymptotically.
## 2 Materials and Methods
### Model Architecture  
This study developed a regression model for the next Sequential Organ Failure Assessment (SOFA) score, a mortality prediction metric based on the severity of dysfunction in six organ systems. This score ranges from 0 (indicating normal organ function) to 4 (representing the most severe organ dysfunction). Since the SOFA score is based on ordinal scales associated with fatality rates, the task was framed as a regression problem, with the model aiming to predict the next SOFA score.  
The network architecture employed a multi-layer perceptron (MLP) consisting of three to five layers, including input and output layers. A dropout layer with a 50% dropout rate between the hidden layers was introduced to prevent overfitting. The output layer used the softmax activation function. Each input \"x\" represented clinical data for each time step, and the network produced a single value \"\(\mu(x)\).\" The hyperparameters were fine-tuned using the Adam optimization method on the training dataset.  
To enhance model accuracy and effectively reduce variance error, an ensemble approach comprising five distinct MLP models was adopted. In this ensemble approach, the Bagging method was utilized. Bagging is an ensemble technique in which multiple subsets of the training data are created through bootstrap sampling. Each subset is used to train a separate base model independently. After training, their predictions are combined to make a final prediction. Given the regression nature of the approach, the final prediction  
Figure 2: Two steps for feature preprocessing.  
was computed through the process of averaging the individual predictions, a prevalent technique in regression problems. This method entails aggregating the predictions from the individual base models, and the final output is determined by their averaged values. The formula for Bagging is expressed as follows:  
\[\textit{Ensemble Prediction}=\frac{1}{n}\ast\Sigma[H_{i}(\mathrm{x})] \tag{1}\]  
where '\(n\)' denotes the count of base models used in the ensemble. '\(H_{i}(\mathrm{x})\)' represents the prediction of each specific base model ('\(i\)'). Each model predicted the next SOFA score based on predicted probabilities for regression. These models were assigned varying weight volumes, and their predictions were combined into a single predictor. The ensemble method was leveraged to predict the next SOFA score for each time step, as illustrated in Figure 3.
## 3 Verification of Theoretical Analysis and Numerical Simulation
### Comparison of Homogeneous and Inhomogeneous Whole-Space Medium
Meanwhile, the electromagnetic response characteristics of the inhomogeneous medium and homogeneous whole-space medium are investigated based on numerical simulation, so the obvious difference between the response curves of the inhomogeneous and homogeneous whole-space medium is also proved.
## 6 Experimental Results
For the purpose of informing operators of changes in surface friction, this is sufficient to highlight potential issues such as significant surface corrosion within the region scanned. Scanning of a friction-stir-weld is shown in Figure 5.1 The ridges of a friction-stir welded plate, shown in Figure 10a with 0.5 mm ridges at the edge of the weld-line. Figure 5.2 Histogram plot for two different raster paths over two plates of different roughness values. Results for rough and smooth CFRP surfaces show the effects of speed and applied force on the frequency of the observed friction values as a percentage of the total number of observations. In Figure 10b, the ridges can be seen as peaks in the observed coefficient of friction. The normal force control is to maintain consistent contact between the surface and probe, with surface to probe offset values for the curved sample scan presented in Figure 5.3  
The standard deviation of 0.36 mm demonstrates the consistency of contact enabled by both the force-control implemented and the sprung tool deployed.
## Communication protocol
### Evaluation Concept  
The flight tests were carried out on the test site of the University of the Bundeswehr Munich and divided into five experiments (V1-V5). V1 covered the basic system functions of the MMT and UTM, whereas V2 contained the transmission and approval of flight plans as well as the monitoring during the flight using the UTM network. The focus of V3 was on the collaborative interface with ATC regarding the compliance with entry/exit rules, the remote switching of the transponder modes and codes as well as ATC initiated emergency landing procedures. For that purpose a virtual controlled airspace (100 \(\times\) 50 \(\times\) 25 m) with four reporting points was integrated into the UTM network. Experiments V4 and V5 focused on the collision avoidance for cooperative intruder based on telemetry data from the UTM network as well as from ADS-B.  
For this purpose, a simple Detect-and-Avoid (D&A) approach was implemented. In addition to the current position and the next waypoints, each UAS transmits the status of its own energy supply (state of charge (SoC) in percent). The D&A subsystem analyzes the received traffic information every 0.5 s. In the event of a shortfall of the horizontal or vertical minimum distance to the nearest flying traffic, a decision is made on the basis of the remaining energy reserve. The aircraft with higher SoC must pause the flight (transition into hovering), whereas the other may continue its flight. Safety distances were defined to be 50 m horizontally and 100 ft vertically for a maximum horizontal airspeed of 5 m/s. This intruder scenario was first tested with a real ownship and a simulated intruder (V4), followed by a real intruder (V5). A detailed list of the investigated parameters in the respective experiments is shown in Figure 8. During the evaluation, a wide variety of data was recorded on the individual system components (Figure 9). These included:  
* Signal strengths at LTE base station
* All inbound and outbound traffic at the UTM server
* ADS-B In data at a stationary ADS-B receiver
* Screen recordings of user interfaces
* Video recordings of UAV's flight movements  
Figure 8: The five conducted flight experiments with the respective examination contents.
## 4 Proposed Model
### Model Training Algorithms  
The training algorithm for the residual VGG-based Siamese network is shown in Algorithm 1. Firstly, vibration signals from healthy states and four types of faults in induction motors are measured and converted into two-dimensional images using signal processing techniques. This conversion provides more feature information and standardizes the images to enhance the model's accuracy and convergence speed. Secondly, the training dataset \(T\) is processed to pair the same class or different classes of images, forming images \(X\), with each pair containing two images. Subsequently, key training parameters such as epochs \(e\), batch size \(b\), and parameters for the Adam optimizer are determined. To promote effective learning and convergence, a learning rate adjustment mechanism is implemented, reducing the learning rate by half every 20 epochs. This strategy helps to rapidly approach the optimal solution in the initial phase and fine-tune the adjustments later to avoid excessive oscillations and ensure stable convergence of the model.  
During each iteration \(i\) of the model training, the residual VGG (feature extraction model) \(f_{w}\) extracts features from the images, and the residual learning architecture prevents convergence failures. The image features are then captured by the feature extraction layer and output as low-dimensional feature maps. These feature maps are transformed into feature vectors \(O_{1}\) and \(O_{2}\) through the global average pooling layer. The Euclidean distance \(ED_{w}\) and Manhattan distance \(MD_{w}\) are used to calculate the distances between two images, and the sigmoid activation function determines their similarity. Finally, the contrastive loss function \(L^{(t)}\big{(}w,\ Y,\ \hat{I}_{1},\ \hat{I}_{2}\big{)}\) calculates the classification loss based on the Euclidean and Manhattan distances, and the Adam optimizer adjusts the model parameters \(w\) based on the loss value to optimize the model.
# Evaluation of Nitrogen and Phosphorus Removal
## 3 Results
### Nutrient Concentrations
#### 3.3.2 Phosphorus  
Overall, the treatment system was successful at reducing TP and SRP concentrations, but P reduction was most effective within the bioreactors rather than the tanks (Figure 5 and Supplementary Materials Table S2). In 2019, the System Inflow median TP concentration was 18.1 (range: 10.3-34.9) mg L\({}^{-1}\) and 12.84 (range: 7.0-28.6) mg L\({}^{-1}\) for SRP. The median concentration of TP measured at the Tank Outflow was 16.30 (range: 12.4-24.7) mg L\({}^{-1}\) and 14.72 (range: 10.7-21.4) mg L\({}^{-1}\) for SRP. There was not a statistically significant difference comparing the difference in median P concentrations measured from System Inflow and Tank Outflow.  
The WB Outflow median TP concentration was 12.1 (range: 9.72-19.5) mg L\({}^{-1}\) and 10.27 (range: 7.8-18.16) mg L\({}^{-1}\) for SRP. There was a reduction for both median TP and SRP concentrations compared to the Tank Outflow measurements, and those difference were found to be statistically significant (\(p=0.002\), \(p<0.001\), respectively). Median concentrations from the EB Outflow were 13.1 (range: 11.0-27.3) mg L\({}^{-1}\) for TP and 11.58 (range: 6.75-21.6) mg L\({}^{-1}\) for SRP, and there was not a statistically significant difference compared to Tank Outflow concentrations.  
Figure 4: Box and whisker plots of nitrogen (N) species concentrations from each sampling location. Bottom and top boxes indicate 25 th and 75 th percentiles, respectively, the middle line shows the median, whiskers show the min and max, and points denote any outliers. TN = total N, NH\({}_{4}\)\({}^{+}\)-N = ammonium-N, NO\({}_{2}\)\({}^{-}\)+NO\({}_{3}\)\({}^{-}\)-N = nitrite + nitrate-N.
# Application of a Geotextile in the Treatment of Post-Subsidence in Karst Areas
## 4 Results and Discussion  
### Effects of Anchorage Length of Geotextile  
The anchorage length of the geosynthetic is a key factor [30]. Numerical models M1 to M6, having anchorage ratios \(L/B\) of 0, 0.25, 0.35, 0.50, 0.75, and 1.0, respectively, were used to investigate the effects of the anchorage length of the geotextile on the treatment of subsidence by presenting the soil pressure, tensile force and deformation of geotextile, and soil displacement at the final state of subsidence.
## 6 Conclusions  
This study evaluated the performance of ALS- and DAP-based methods in canopy cover and LAI estimation using many tropical forest plots. As the DAP data produced by the SfM algorithm cannot offer a detailed enough 3D canopy like ALS data, the canopy cover and the associated LAI estimates were remarkably larger than the ALS-based estimates. Differences in correlation among different forest types and tree species were also found (R\({}^{2}\) of canopy cover and LAI range from 0.64 to 0.89 and from 0.54 to 0.87, respectively), regardless of the overall R\({}^{2}\) of 0.80 for canopy cover and overall R\({}^{2}\) of 0.76 for LAI. We explored the relationship between airborne LAI estimates and field-measured AGB and found better correlation between ALS-based LAI and AGB, indirectly validating the credibility of the ALS-based method. Different forest types and tree species exhibited different correlations where the optimal correlation occurred in the mixed coniferous and broad-leaved forests. The comparison between airborne and Sentinel-2-based LAI estimates again verified the value of the ALS-based method, given its better consistency with the widely used satellite-scale hybrid retrieval method. This study provides an insight on the ability of the existing 3D data in canopy variable estimation.  
Data curation, L.L.; Formal analysis, X.Z. and J.J.; Investigation, Y.Z.; Methodology, C.L.; Validation, F.W.; Writing--original draft, C.L. and Y.Z.; Writing--review & editing, F.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results
### Fermentation of Shuffled Recombinant Strains  
Biomass and toyocamycin production are shown in Figure 1A,B. The biomass (mycelial dry weight) increased from 0 to 90 h for both 1628 and R3-33 in the GYM. The mycelial dry weight was highest (4.18 mg/mL) for 1628 at 96 h, while the highest mycelial dry weight was 4.61 mg/mL at 90 h for the R3-33, indicating the cell growth rate was similar for wild type strain and R3-33 from 12 to 96 h. At the same time, a dramatic increase of toyocamycin production occurred in this period for the mutant R3-33. At 0-36 h, the secondary metabolic toyocamycin could not be generated in both wild type and R3-33, because it is the primary phase for strain growth. Then, the toyocamycin production increased sharply from 36 h to 84 h for R3-33, and the highest production reached 644.5 mg/L at 84 h, which was 10.8-fold of the production in wild-type _S. distatochromogenes_ 1628 (59.67 mg/L) at 84 h. The productivity of R-33 reached 139.80 mg/g DCW at 96 h, which is significantly higher than that of wild-type 14.27 mg/g DCW. Therefore, the mutant R3-33 has a significant difference in cell growth and toyocamycin production.  
The different culture nutritional conditions could affect the biomass and the secondary metabolites. Therefore, five kinds of medium were used to obtain higher toyocamycin production, including GYM, 2\(\times\)GYM, TPM, CP, and M3G. The highest toyocamycin production in different culture mediums is shown in Figure 1C. The lowest toyocamycin production was 410 mg/mL in CP culture, while the highest production reached 1173.6 mg/L in TPM, which was twice that of the production in GYM (_p_ < 0.01). No difference in production was shown in M3G and GYM (_p_ > 0.1). The significant difference between CP, GYM, 2\(\times\)GYM, and TPM indicate that the nutrition condition was crucial for toyocamycin production. The most suitable culture medium was TPM.  
The stability of positive phenotype R-33 is critical for the industrial fermentation. Therefore, the selected colonies R3-33 were cultured for four generations and measured the toyocamycin production. All the generations showed similar biomass and toyocamycin production (688.4-694.3 mg/L), which suggested that the mutant R3-33 is genetically stable and suitable for the practical use (Figure 1D).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Strains**} & \multicolumn{3}{c}{**Genes**} \\ \cline{2-4}  & _rpsL_ & _rsmG_ & _rpoB_ \\ \hline SD10 & - & Cys59 Ter & - \\ SD88 & - & - & Pro437 Lys \\ SD99 & - & - & - \\ SD143 & - & 40A delection (frameshift) & - \\ R1-31 & - & 40A delection (frameshift) & - \\ R1-41 & - & Cys59 Ter & - \\ R2-8 & - & Cys59 Ter & - \\ R3-33 & Gly122 Val & Cys59 Ter & - \\ \hline \hline \end{tabular} \(\cdot\): not detected.  
\end{table}
Table 3: The relative genotype of stains after genome shuffling.
## 3 Key Technologies in Trust Management for WSNs
### Trust Evidence  
In this section, we answer RQ2: What trust evidence is required to identify DoS attacks effectively? In resource-constrained WSNs, collecting large amounts of trust evidence leads to increased computational load, storage and communication overheads, and higher network latency [4]. To identify the trust evidence sufficient for DoS identification, we analyzed the trust evidence used by each of the trust models in Appendix A, Table A1, and the main impact features of DoS attacks on the network in Table 2.  
As shown in Table 3, the trust evidence used in the majority of the trust models reviewed belongs to two categories: communication trust evidence and energy trust evidence. Communication trust evidence includes the PSR, the PRR, and the PFR. Energy trust evidence is provided by the ECR.  
Notably, some trust models are used as evidence of additional trust features, such as the packet loss rate or the packet delivery success rate between two nodes [15; 39; 40]. For the purposes of this study, we consider these metrics as link quality indicators rather than as trust metrics (see Section 4 for a further discussion).  
A comparison of Tables 2 and 3 indicates that the trust evidence in Table 3 would be sufficient to identify ten of the attack types in Table 2. The exceptions (the Sybil attack, the on-off attack, and the bad/good-mouthing attacks) are not manifested explicitly in higher energy consumption or noticeable changes in packet transmission rates. However, these attacks can be countered through adjustments to the trust value, as shown in Section 3.4.  
As the main purpose of a WSN is to collect real-time data for further analysis [8], the accuracy of the data is critical: even if the network traffic characteristics and the energy consumption indicators at the WSN nodes are normal, the network is still unreliable or practically unavailable if the collected data are incorrect or not real-time. To include this quality requirement, we added 'data trust evidence' as a third category in Table 3; the data trust evidence is data accuracy (DA).