esa-sceva/satcom-chunk-collection huggingface dataset:
# LSTM Network for the Oxygen Concentration Modeling of a Wastewater Treatment Plant
## 4 Results
### Discussion  
The performance indexes are reported in Table 4 for both of the presented techniques.  
Figure 4 emphasizes the comparison of the two techniques by showing the first 6 h. This magnification makes it evident that the LSTM model is able to follow the peaks better with respect to the ARX model, which, in particular, underestimates the maximum values in this subsection.  
The quality of the predictions can be evaluated through the use of different indices, but a compact way to represent them all together is proposed here. In order to compare the proposed LSTM and the classic ARX models, the Taylor diagram [40] is considered. It graphically summarizes the similarity between a predicted pattern and the observations. Their similarity is quantified in terms of their correlation, their centered Root-Mean-Square Difference (RMSD) and the amplitude of their variations (standard deviation). These diagrams are especially useful in evaluating multiple aspects of complex models at the same time, because the Taylor diagram can represent three different statistics simultaneously in a two-dimensional space. A point is assigned to each model, and its position quantifies how closely that model's predictions match the observations. In the Taylor diagram, the model with better performance will lie nearest to the point marked \"Data\", showing which prediction better represents the observations.  
From this diagram, represented in Figure 5, it is clear that the LSTM model outperforms the ARX model: the LSTM model presents a lower correlation coefficient and lower RMSD, and has a standard deviation more similar to the observed one, with respect to the ARX model.  
\begin{table}
\begin{tabular}{c c c} \hline
**Index** & **ARX** & **LSTM** \\ \hline \(FIT\) [\%] & 41.20 & 60.56 \\ \(\rho\) & 0.833 & 0.921 \\ \(RMSE\) [mg/L] & 0.307 & 0.206 \\ \hline \end{tabular}
\end{table}
Table 4: Model comparison, results from Dataset1 simulation.  
Figure 4: Comparison of the results obtained with the LSTM and the ARX model during the first 6 h.  
In Section 3.4, the variability of the datasets was attributed to different working conditions. Probably, a proper data classification that isolates plant stops and abnormal conditions will result in better performances for both models, and in particular, for linear models such as the ARX considered in this work. On the other hand, the nonlinearity of the LSTM can include and naturally estimate the different dynamics connected to abnormal but frequent conditions. In this case, the LSTM is able to represent the general dynamics of the process under study without requiring a working point estimation or a model switching.
# Cartesian Constrained Stochastic Trajectory Optimization for Motion Planning
## 2 STOMP Algorithm  
The STOMP algorithm mainly works in joint space; therefore, the disadvantage of the whole algorithm is that it is unable to consider any cartesian constraints in trajectory generation and it is possible only to validate constraints on a result. This research analyzes opportunities of cartesian path constraints implementation, with this section briefly describing the most important parts of the STOMP algorithm.  
In the first step of STOMP, the initialization method is executed, creating a deterministic trajectory (Figure 1). The simplest case is the linear interpolation in joint space. Another option is to use a custom initial trajectory. If the initial trajectory is collision free, it can be considered to be a result of the algorithm.  
When the initialization trajectory is not collision free, the algorithm continues into a first iteration in which \(n\) different trajectories are generated. These trajectories are called rollouts (Figure 1). The next important STOMP term is timestep, which represents robot  
Figure 1: An example of initialization trajectory (red line) and rollouts (grey lines). The grey polygon between the start and the goal states represents an obstacle (Reprinted Ref. [22]).  
state or waypoint in the trajectory. STOMP works with a constant number of timesteps. Rollouts are created by a noise generator, which generates noise around the initialization trajectory in each timestep. The basic noise generator uses Normal Distribution Sampling. This method uses a parameter standard deviation (stddev) that represents an amplitude of the noise applied to each joint in the rollout. Using larger values will produce larger motion for the joints. In the context of STOMP, each joint state of a trajectory is called timestep and STOMP works with a constant number of timesteps.  
The next step is to evaluate rollouts adding cost values by cost functions (STOMP can use more functions). If more rollouts are valid, the resulting trajectory is rollout, which has the lowest cost (Figure 2). On the other hand, if no rollout is valid (e.g., the robot state is in collision with an obstacle), then the rollout with the lowest cost is chosen to the next iteration, where a new noise is generated around this chosen rollout. One of the standard STOMP cost functions is Collision Check, which validates if the rollout is collision free and the cost is computed by the kernel smoothing.  
STOMP also uses other methods for preprocessing and postprocessing of trajectories, including Update Filters and Noisy Filters (further details are discussed in [7; 23]).
# Technique Evolutions for Microorganism Detection in Complex Samples: A Review
## 2 Current Detection Methods
### Methods Based on Growth Monitoring
#### 2.1.4 Microcalorimetry  
Microbial catabolism generates heat that can be measured accurately by microcalorimetry [18]. A minimum number of microorganisms is required to generate thermal measurements above the baseline, which is generally achieved using an enrichment medium.
## 3 E3W Model  
In selecting the models that make up E3W, we chose the following four models, taking into account the fact that Chinese agricultural news headlines are short, making it difficult to extract more features.  
ERNIE: Compared with BERT, ERNIE's improved mask mechanism makes it more applicable to Chinese, obtaining state-of-the-art results in Chinese NLP tasks.  
ERNIE + DPCNN: With shorter news headlines, in order to make the extracted features richer, we applied DPCNN to the model as well. In DPCNN, the text repeatedly passes  
Figure 8: TextCNN structure.  
through the remaining half pooling blocks. In this way, the semantics of the text are continuously improved, and more features can be obtained.  
EGC: In the field of Chinese agricultural news classification, the EGC model has presented the best classification effect and, at the same time, can provide a more intuitive and better comparison effect.  
Finally, we also chose Word2 Vec + TextCNN: Word2 Vec is used to train feature-rich word vectors to compensate for the lack of features due to the short text, while TextCNN is an improved CNN model that more advantageous for text classification processing.  
For these reasons, we chose ERNIE, ERNIE + DPCNN, EGC, and Word2 Vec + TextCNN as the sub-models of E3W. Meanwhile, the GreedySoup weighting strategy was used to obtain weights for the outputs of the four sub-models appropriately, such that the four sub-models produced better results when combined. Agricultural news was divided into five categories, where we denoted by 0, 1, 2, 3 and 4 the five categories of fisheries, forestry, planting, animal husbandry, and side-businesses, respectively. We experimentally obtained the classification accuracy for each model in the different categories within the used data set, in order to obtain the best classification areas for each model. These areas represent the category in which the model has the highest classification accuracy, which is an important basis for the weighting of E3W sub-models using GreedySoup. The best classification areas for ERNIE, ERNIE + DPCNN, EGC, and Word2 Vec + TextCNN were categories 1, 0, 2 and 3, and 4, respectively.  
The E3W structure can be divided into four parts: input layer, model combination, GreedySoup Fine-tune, and final output. The overall E3W structure is shown in Figure 9.  
First, in the input layer, the text is fed into the pre-treatment module for pre-processing. The pre-processed text is then fed into the combination of models, consisting of all four models (i.e., ERNIE, ERNIE + DPCNN, EGC, and Word2 Vec + TextCNN), each of which outputs a result to the next stage. In the GreedySoup Fine-tune stage, the outputs of the four sub-models are weighted using the GreedySoup weighting strategy, which is carried out to obtain a matrix containing information on the weights of each category. Finally, in the final output stage, the category with the highest weight is selected as the final output.
## 5 Detailed System Design
### Speed and Torque Features Setting
Even if the total height of the system is lower than the level of human head, there is no chance to guarantee and control the posture of human at the contact time; therefore, skull and face value are also considered because human could kneel or bend his head down. Consequently, as worst case for the contact assessment, the cobot with the maximum effective mass \(m_{r}\) of 35.5 kg is selected (see Section 2). Selected values for the maximum allowed force refers to the transient contact, also referred to as \"dynamic impact\" [16]. To stay safe, the adopted speed limit value is the one related to the contact with face that is the minimum maximum speed that comes from the contact force assessment (Table 3). Finally, as speed profile, a trapezoidal law, divided into three equal distributed, is chosen to reduce the maximum power required for the actuation [38]. Once motion requirements are fixed, it can be computed the maximum driving torque using Equation (4). \[T_{m}=\frac{2F_{max}^{2}}{\theta km_{h}}\bigg{(}m_{h}+\frac{1}{2}M+m_{l}\bigg{)}. \tag{4}\]  
All cobot have been approximated by an equivalent cylindrical body characterized by the own mass and a radius equal to the maximum radial distance of the cobot from the axis, (\(R\)) (Figure 13). The payload is assumed as a point mass at a distance (\(R\)) from the axis. This is a conservative approximation due to an overestimation of the inertia as well as the required torque. The target position \(\theta\) depends on the target angle that the interface has to reach. It is assumed as maximum displacement \(\theta\) = 180\({}^{\circ}\). Furthermore, power losses due to bearing friction need to be taken into account for the torque computation. The bearing frictional torque can be computed as follows [39]:  
\[T_{f}=0.5\mu Fd \tag{5}\]  
where \(\mu\) is the friction coefficient related to the bearing; \(d\) is the bearing bore diameter; \(F\) is equivalent to dynamic load, given by the combination of the cobot weight and payload, multiplied by the gravity acceleration. By adding the friction torque (5) to the maximum driving torque (4), the total torque required to move each of the selected cobot is computed. As an example, eight possible combinations of DC motors and suitable gearboxes from Maxon [40] have been chosen (Figure 14) to cover the whole range of computed torque values covering more than forty selected cobot (Figure 15). Figure 13: Equivalent inertia for the cobot stowed position.
## 3 Results  
### Seasonal Variation Patterns of InSAR Coherence and SAR Backscattering Coefficient  
Seasonal variation of InSAR coherence, SAR backscattering coefficient in VV and VH polarizations are shown in Figures 3-5 respectively, for all the observed crop fields and the three patches of forest. For convenience, the dates of the coherence data in Figure 3 represent the acquisition date of the earlier SAR image in each InSAR image pair. It is observed that coherence for the three forest patches remained at a low level (\(\sim\)0.2) throughout the season, whereas that of crop fields showed a wide dynamic range with clear seasonal patterns. The coherence of crop fields was low (0.2-0.3) in the early (before May) and the late (after late October) season. It was at a higher level during the start (May-June) and the end of the growing season (September-October), with a relatively higher coherence at the start of the season than at the end of the season. Coherence generally dropped to a lower level in the mid-season.  
The VV and VH backscattering coefficients of the crop fields also exhibited apparent seasonal patterns, with a relatively lower level at the start and the end of the growing season, and a higher level in the middle of the season (August). This trend was more apparent for VH than for VV polarization, and was in accordance with the general seasonal growth cycle of the crops. Large fluctuations throughout the season and great variability among fields were also observed, more for the VV polarization than for VH polarization. For the forest patches, the variation of VV and VH was relatively small throughout the season, with an average of \(-\)8.5 dB and dynamic range <3.2 dB for VV, and average \(-\)14.3 dB and dynamic range <3.9 dB for VH. VV and VH for the forest were higher than that of the crop fields at the start and the end of the growing season. In April, the backscattering of  
Figure 3: Typical seasonal variation of InSAR coherence for the study area during May 6–December 1 2019: (**a**) crop fields and (**b**) forest.  
crop fields was remarkably lower than the rest of the time and that of forest, apparently due to wet snow during spring snow melt.  
Figure 4: Typical seasonal variation of VV backscattering coefficients for the study area during May 6–December 1 2019, measured by Sentinel-1 C-band SAR. (**a**) crop fields and (**b**) forest.  
Figure 5: Typical seasonal variation of VH backscattering coefficients for the study area during May 6–December 1 2019, measured by Sentinel-1 C-band SAR. (**a**) crop fields and (**b**) forest.
# Evaluating the Metabolomic Profile and Anti-Pathogenic Properties of _Cannabis_ Species
## 3 Results and Discussion
### _neoformans_  
Other findings revealed that _Cannabis_ petroleum ether extract has effectively displayed modest activity against _Candida albicans_[114].  
Some recent studies suggest that a 6.25 mg mL\({}^{-1}\) n-butanol leaf extract of _C. sativa_ has proven to completely control the growth of _A Aspergillus versicolor_[115]. Furthermore, a significantly high percentage of mycelial growth inhibition was observed in seed-borne phytopathogenic fungi _Alternaria_ species [116]. Despite numerous research studies confirming the antifungal activities of _Cannabis_, most studies investigated mostly extracts from its aerial. Studies with the extracts of roots and stems have not been extensively carried out. Therefore, it is highly probable that extracts from roots and stems possess pronounced antifungal potential against a wide spectrum of resistant pathogenic fungi that cause devastating economic loss in agriculture.  
More recently, there has been growing scientific evidence suggesting that the synergistic interaction of _Cannabis_ compounds with common antibiotics has the potential to exert antimicrobial action on resistant pathogens [117]. Some previous studies have demonstrated that the synergistic effect between cannabinoids and terpen may contribute to more effective treatment [118]. However, this synergistic interaction effect is not as extensively investigated in antifungal and cytotoxic activities. In addition, this knowledge appears to have not been given sufficient attention, especially in its application in medicine, industry, or agriculture. Moreover, it is widely known that since the emergence of diseases in fish farming, the use of herbal products to control fish pathogens in aquaculture is an alternative and current practice as chemicals or drugs used to treat infected fish are limited and have harmful effects to the environment and humans who are end consumers of fish products.  
Some previous research studies observed that various plant extracts and essential oils can inhibit the growth or even cause the death of pathogenic fish fungus at comparatively low concentrations [119; 120]. For instance, _Origanum otites_ and _Thymbra_ have been observed to possess potent antifungal properties against _Saproleginia parasitic_ known to cause saproleginiasis diseases and considerable economic problems on hatched salmon in aquaculture facilities [121]. In addition, Terminalia _catappa_ has been reported to exhibit the highest antimicrobial effect of minimum inhibitory concentration (MIC) of 25 and 12.5 mg mL\({}^{-1}\) against _S. parasitic_[122]. However, currently, no scientific reports have been published concerning the antifungal potential of _Cannabis_ against fish fungal pathogens and there is also no investigation on the possible synergistic interaction between standard antimicrobial drugs and _Cannabis_ compounds. Consequently, the urgent identification of new molecules exerting novel modes of action that can work alone or in combination with synergistic compounds is required [20].
# Resource Assignment Algorithms for Autonomous Mobile Robots with Task Offloading
## 4 Proposed Optimization Algorithms
### Selfish Reformulation: MLAT Algorithm  
Upon neglecting the finite-resource constraints due to (4) and (5), the \(\max\{\}\) operators in (17) disappear, and the obtained latency may be rewritten as  
\[\begin{split}\tilde{L}_{i}(f_{i},\mathbf{S}_{i})=& \ell_{i}f_{i}+\sum_{k=1}^{N_{\text{BS}}}\sum_{m=1}^{N_{\text{DC}}}[(l_{m}+d_{ k,m})D_{i,k}K_{k,m}+l_{\infty}(1-D_{i,k}K_{k,m})]S_{i,k,m},\\ i=1,\ldots,N_{\text{AV}}.\end{split} \tag{23}\]  
Additionally, we reformulate (3.2) as a multi-objective problem,  
\[\begin{split}\min_{\mathbf{t},\mathbf{S}}&\left(\tilde {L}_{1}(f_{1},\mathbf{S}_{1}),\ldots,\tilde{L}_{N_{\text{AV}}}(f_{N_{\text{AV}} },\mathbf{S}_{N_{\text{AV}}})\right)\\ \text{subject to}&\mathbf{f}\in\{0,1\}^{N_{\text{AV}}} \\ &\mathbf{S}\in\{0,1\}^{N_{\text{BS}}\times N_{\text{DC}}N_{\text{AV }}}\\ & f_{i}+\sum_{k=1}^{N_{\text{BS}}}\sum_{m=1}^{N_{\text{DC}}}S_{i,k, m}=1,\;i=1,\ldots,N_{\text{AV}},\end{split} \tag{24}\]  
which can be viewed as a selfish (noncooperative) reformulation by letting each AV choose its own minimum-latency assignment. Separability of the objective function and of the constraints \(\min L_{i}(f_{i},\mathbf{S}_{i})\) leads to a simplified solution: since \(l_{m}+d_{k,m}\ll l_{\infty}\) in (23), the minimization requires \(D_{i,k}K_{k,m}=1\), yielding the solution  
Figure 5: Graphical overview of the mutation operation in the GA algorithm.  
\[f_{i}^{*} =\begin{cases}0,&\min_{k,m:D_{i,k}K_{k,m}=1}(l_{m}+d_{k,m})<\ell_{i}, \\ 1,&\text{else},\end{cases} \tag{25}\] \[\mathbf{S}_{i}^{*} =\begin{cases}\mathbf{E}_{k_{i}^{\prime},m_{i}^{*}},&\min_{k,m:D_{ i,k}K_{k,m}=1}(l_{m}+d_{k,m})<\ell_{i},\\ \mathbf{0}_{N_{\text{BS}}\times N_{\text{DC}}},&\text{else},\end{cases} \tag{26}\]  
where \(\mathbf{E}_{k^{\prime},m^{\prime}}\) is a matrix whose element in position \((k^{\prime},m^{\prime})\) is equal to \(1\) and zero elsewhere, and  
\[(k_{i}^{*},m_{i}^{*})=\begin{cases}\arg\min_{k,m:D_{i,k}K_{k,m}=1}(l_{m}+d_{k, m}),&\min(l_{m}+d_{k,m})<\ell_{i},\\ (0,0),&\text{else},\end{cases} \tag{27}\]  
\(i=1,\dots,N_{\text{AV}}\), \(k=1,\dots,N_{\text{BS}}\), and \(m=1,\dots,N_{\text{DC}}\). Note that this solution does not consider overloading of the resources, because the minimization is performed over \(L_{i}(f_{i},\mathbf{S}_{i})\) in (23); therefore, the true AV latency \(L_{i}(\mathbf{f}^{*},\mathbf{S}^{*})\) in (17) may be higher than \(L_{i}(f_{i}^{*},\mathbf{S}_{i}^{*})\). The algorithm complexity order is \(\mathcal{O}(N_{\text{AV}}N_{\text{BS}}N_{\text{DC}})\).
## 2 Materials and Methods  
### Equipment  
The accuracy of speech recognition in noise of a smartphone-based RM system (iPhone set to LL) was compared with a clinically available RM system (Phonak Roger On with Roger Focus II), both designed for TH users. The equipment used for this comparison is described in Table 2 and illustrated in Figure 1.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Function** & **Name** & **Device** \\ \hline Sentence and noise stimuli input & & ThinkPad P70 (Lenovo) \\ Otter VTT & & Latitude 7480 (Dell) \\ \hline Stimuli transmission & Audometer & Grason Stadler GSI 61 \\  & Speakers & Two free-field corner speakers \\  & KEMAR & KEMAR Dummy-Head \\ \hline Transmitter & & iPhone 12 (IO5 15.0, Apple) \\ Receiver & Smartphone-based RM system & Right ear AP (1 st Gen., Apple) \\ \hline Transmitter & & Roger On \\ Receiver & Phonak Roger RM system & Roger Focus II \\ \hline NOTE: VTT = voice-to-text transcription; KEMAR = Knowles Electronics Manikin for Acoustic Research; RM = remote microphone; AP = AirPods Pro. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Devices used in the study.
## 3 Methodology  
### Workflow  
The overall set of procedures composing the proposed approach is shown in Figure 1.  
Figure 1: Set of procedures composing the workflow of the proposed approach.  
As the proposed approach is based on the application of ML algorithms, the time series (i.e., Channel Impulse Response (CIR)) is transformed in a feature space by applying a feature extraction process using the following features: variance, skewness, kurtosis, Shannon entropy, quantile with a threshold of 0.7, and quantile with a threshold of 0.9. These features are applied to the original time domain representation of the CIR as well as the magnitude component and the phase component of the spectral domain representation using the Fast Fourier Transform (FFT) algorithm for a total of 18 features. These features are used in this approach because they have been applied to the same problem in the literature [6; 23; 24]. The feature space is normalized between 0 and 1, then it is labeled with LOS and NLOS labels and split into a training and test data set (3/4 of the data set is for training and 1/4 of the data set is for testing). Then, poisoning attacks are applied to the training data set. As described in the threat model given in Section 3.2, three attacks are implemented: the classical label flipping attack [25], the perturbation attack, and perturbation based on a template created by the best 100 samples for each class (LOS and NLOS). The knowledge and tools needed by the attacker to implement the attack are described in detail in Section 3.2. Different poisoned data sets are created for various values of the percentage of training samples \(T_{P}\) and the severity of the attack \(S_{P}\) (which is the amount of applied perturbation to the feature values). \(S_{P}\) is not applicable to the label flipping attack. The OD algorithms are applied to the poisoned feature spaces. Three algorithms are adopted in this study: the titular SDO algorithm [26], the iForest algorithm [27], and the OCSVM algorithm [28]. These generate a list of outlier scores for all the samples of the poisoned feature spaces. The assumption is that the highest scores (which indicate an anomaly) can be used to identify the poisoned samples while the lowest scores can be used to select the legitimate samples, which have not been tampered with. This assumption can be unpaired by the presence of anomalies in the initial data set, which will be selected as poisoned samples but they are actually legitimate (this is one of the limitations of the proposed approach). The highest scores are used as the first evaluation metric to identify the capability of the approach and the OD algorithm to identify the poisoned samples while the lowest scores are used to sanitize the poisoned data set and the classification accuracy of the ML is the second evaluation metric. See Section 3.4 for further details. For the second metric, the results of the ML classification are compared between its application to the poisoned feature space and the sanitized feature space.