esa-sceva/satcom-chunk-collection huggingface dataset:
# Dissolved Oxygen Modeling by a Bayesian-Optimized Explainable Artificial Intelligence Approach
## 2 Methodology
### Evaluation Metrics  
Statistical metrics are crucial for evaluating model performance when handling large datasets. Therefore, the coefficient of determination (CD), root mean squared error (RMSE), and mean absolute error (MAE) are used. The CD assesses the goodness of fit, whereas the RMSE and MAE measure the estimation error. These metrics are commonly used as performance indicators [14; 27]. The CD is computed by using the following expression:  
\[\text{CD}=\left(\frac{\sum\limits_{i=1}^{N_{o}}\big{(}O_{i}-\overline{O}\big{)} \big{(}P_{i}-\overline{P}\big{)}}{\sqrt{\sum\limits_{i=1}^{N_{o}}\big{(}O_{i}- \overline{O}\big{)}^{2}\sqrt{\sum\limits_{i=1}^{N_{o}}\big{(}P_{i}-\overline{ P}\big{)}^{2}}}}\right)^{2} \tag{12}\]  
where \(O_{i}\) = \(i\)th observation, \(P_{i}\) = \(i\)th prediciton, \(\overline{O}\) = mean of observations, \(\overline{P}\) = mean of predictions, and \(N_{o}\) = number of observations. The CD index ranges from 0 to 1, representing the worst and best fit, respectively. The RMSE calculates the difference between the modeled and observed values as follows:  
\[\text{RMSE}=\sqrt{\frac{\sum\limits_{i=1}^{N_{o}}\big{(}O_{i}-P_{i}\big{)}^{2 }}{N_{o}}} \tag{13}\]  
The RMSE ranges from zero to infinity, with an RMSE of 0 indicating a perfect model. The MAE computes the average of all individual errors as follows:  
\[\text{MAE}=\frac{1}{N_{o}}\sum\limits_{i=1}^{N_{o}}(|O_{i}-P_{i}|) \tag{14}\]The MAE ranges from zero to infinity, with MAE = 0 denoting a perfect model.
# Silicon Integrated Nanophotonic Devices for On-Chip Multi-Mode Interconnets
## 3 Silicon Multi-Mode Routing Devices  
A complex topology is always desired to boost the performance of an on-chip data link. For example, Figure 11 shows the configuration for the multi-mode interconnects in a micro-processor with five cores. From the configuration, one can find that a situation has arisen where multi-mode routing devices are necessary to facilitate the multi-mode network; also, the multi-mode routing devices should fall into three categories: multi-mode bends, multi-mode crossings and multi-mode splitters. The multi-mode bends enable light transmission with a sharp curvature, which will be discussed in Section 3.1. Another essential building block is the multi-mode crossing, which enables simultaneous cross-over propagations for multiple higher-order modes. The silicon multi-mode crossings will be reviewed in Section 3.2. Multi-mode splitters are also necessary to realize the broadcast topology, which will be discussed in Section 3.3.
# A Review of Abnormal Crowd Behavior Recognition Technology Based on Computer Vision
## 4 Research Status of Abnormal Crowd Behavior Recognition
### Traditional Methods
#### 4.1.3 Methods Based on Dynamic Models  
Methods based on dynamic models, such as cellular automata models [38], particle system models [39], etc., predict the overall movement trend of the crowd by simulating the interaction between individuals and environmental constraints. These models can be used to establish a baseline behavior model for the movement of normal crowds. When the observed behavior significantly deviates from the model prediction, it can be regarded as abnormal. By adjusting model parameters, such as attractive force and repulsive force, it is possible to adapt to the dynamic characteristics of the crowd in different scenarios and improve the accuracy of abnormal behavior recognition.  
[PERSON] et al. [40] proposed a hybrid model of cellular automata and agents, dividing the space into multiple discrete units (cells), with each cell representing an individual or a small part of the crowd in the crowd, and dynamically updating its state according to preset rules to simulate the dynamic behavior of the crowd in normal or emergencies, such as movement, aggregation, evacuation, etc. They also combined it with the Agent model to further refine individual behaviors, considering individual differences such as gender, age, physical conditions, and psychological factors such as panic and conformity on evacuation behaviors. This model can identify which individual behaviors deviate from the norm, that is, abnormal behaviors.
## 3 Results and Discussions
### Numerical Analysis
#### 3.2.2 Heat Release Rate (HRR)  
The HRR is the amount of energy released by a burning fire per unit time, influenced by fuel characteristics, ambient circumstances, and other factors [22]. It is an important parameter indicating the intensity of heat energy released by the fire, regardless of whether the fire is in still air or wind-blown conditions [23]. When wind is present, the heat feedback changes. The natural convective boundary layer caused by buoyancy is replaced by the wind-forced convective boundary layer, which may lead to more intense convective heat feedback on the fuel surface. As the HRR developing trends were similar regardless of the baffle height, the heat release rate of the pool fire with a 6 cm baffle height was set as an example in Figure 6a, while the wind speeds varied from 1 to 5 m/s. The variation trend of the average HRR of a pool fire with the wind velocity (V = 1-5 m/s) at different baffle heights in 30 s of stable combustion stage is shown in Figure 6b.  
As shown in Figure 6b, the average HRR under the different baffle heights (H = 2-10 cm) presented a similar increasing trend with the crosswind varying from 1 m/s to 5 m/s. When small-scale pool fires burn in wind-blown states, the convection heat feedback percentage increases, which becomes the major heat feedback mechanism and influences the flame characteristic [19]. In the condition of the combined effect of the baffle and cross airflow, the flow field and heat convection near the oil pool become more complicated compared to the case of wind alone [24; 25; 26]. The recirculation zone downstream of the pool shown in Figure 5 can retain heat and send back unignited fuel vapor to be burned rather than being blown away, as is the normal case without a baffle [27]. There is a turbulent heat exchange between the recirculation zones and the surrounding flow, which constantly supplies the energy required for ignition to the fuel vapor. The increase in crosswind speed may increase the velocity of the airflow in the recirculation zone and improve the mixing efficiency of fuel and oxidant. Furthermore, more high-temperature gas and fuel vapor returning to the fire zone can increase  
Figure 6: (**a**) Flow velocity vector field changes in the wind tunnel under different wind velocities (H = 6 cm) and (**b**) average HRR of the simulation scenarios.  
the HRR of the flame. As a result, the increase in the HRR exhibited an increase in flame brightness and volume, as shown in Figure 3. Under the experimental scenario of a 10 cm baffle height, an explanation for the decrease in the heat release rate may be the recirculation zone being formed far from the pool fire at a relatively low wind speed. Consequently, the heated gas cannot be kept near the oil pool in time, resulting in a drop in the heat release rate.
## 1 Introduction
Anisotropic and isotropic layers serve as informational units for describing the morphology of layered and arbitrary patterns. These informational units substantially differ from that of pixels, which is used in methods that are based on Shannon entropy [15]. Another distinctive feature of the proposed method is the presence of the reference pattern, which is comprised of layers that have an isotropic size and structure. The parameters DStr, DSize, and PCom are calculated based on this reference pattern. This means that:  
* The DStr serves as the measure of deviation from an isotropic analog in patterns with an anisotropic layer structure;
* The DSize serves as a measure of deviation from an isotropic analog in patterns that have an anisotropic layer size; and
* The PCom serves as a measure of deviation from an isotropic analog in patterns with both an anisotropic layer size and structure. The paper is organized as follows. In the Materials and Methods section, we justify that the EM could be used for quantifying the morphological characteristics of arbitrary patterns by comparing visual macro differences and non-visual micro similarities between layered and arbitrary patterns. We also illustrate that a binary pattern could be reconstructed from the EM. The parameters DSize and PCom are presented to describe the morphological characteristics of binary patterns. The outcome of experiments that are designed to showcase the effectiveness of the DStr, DSize, and PCom parameters are presented in the Results section. The Discussion section contains comparisons of our proposed method with several existing ones; descriptions of the assumptions, limitations, and pros and cons of our proposed method; and areas of potential application and future work.
# Comparative Analysis of Deep Learning Methods for Real-Time Estimation of Earthquake Magnitude
## 3 Analysis of Results  
### Comprehensive Comparative Analysis of Models  
This section provides a comprehensive and in-depth comparison of four forecasting models. By analyzing key performance metrics over time, including the coefficient of determination (R\({}^{2}\)), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), we reveal the relationship between estimation errors and key factors such as earthquake duration, depth of source, signal-to-noise ratio, and distance from the epicenter. In particular, we independently evaluate the effectiveness of single-station estimation for intense seismic events of magnitude six and above to better understand the estimation challenges and model limitations of extreme seismic events.
## 5 Integration of Reputation on Meshtastic
### Nodes' Activities  
**The activation task** is performed as soon as the node successfully connects to the Meshtastic network. Here, the Edge Domain is considered the same Meshtastic protocol. A new packet, Mesh Packet Affiliation, is sent by the node administrator of the network (at that time) to the node joining the network. Once it has received the package, the new node becomes active with the following characteristics:  
* The **reputation score**, set to **0.5 by default**;
* The network **symmetric** AES256 key (the Meshtastic Channel key);
* The network public and private keys (network-wide encryption keys);
* The ID of the node in the network.  
A node that wants to perform a specific **resource search**--for instance, obtaining data from a specific sensor--sends a message to its neighbours, indicating the following:  
* The type of resource required;
* The time interval required to send the resource.  
Any node can respond to the request, using the same Meshtastic keys to sign the response message. The price of the resource is calculated based on the number of **HOPs** measured from the potential provider to the requesting node. Indeed, in a mesh network, the path length needed by a message to reach the destination represents a critical aspect in terms of energy consumption.  
A **resource provisioning** task is then performed after the node has selected its provider from all nodes that have sent a response. Let us suppose node **A** (the consumer) has accepted to receive a service from node **B** (the provider). Then, the trust agents of the two nodes will perform the following activities:  
* The two nodes, through their trust agent, will verify the response message with their keys;
* The consumer will start the negotiation for the requested resource;
* The provider will start the transmission of the requested resource, considering the time interval proposed to the consumer and the number of towards the consumer (the price of the resource).  
**Trust Management.** Depending on the time interval set by the mesh network, each node of the network will send a Mesh Packet via flooding including its feedback on the resource requested to a given node and its behaviour, a message encrypted using the private network key. When a node receives this message type, it achieves the following:  
* It certifies that the message is lawful to its network;
* It updates the reference node's reputation score.
# Comparison of Disturbance Compensators for a Discrete-Time System with Parameter Uncertainty
## 3 Overview of Disturbance Compensators
### Decoupled Disturbance Compensator  
The decoupled disturbance compensator is established in the discrete-time domain utilizing the sliding mode theory [13; 14], which is shown as Equation (19):  
\[u_{d5}(k)=u_{d5}(k-1)+g[s(k)-(1-q)s(k-1)+\xi\text{sign}(s(k-1))] \tag{19}\]  
where \(g\) is the control parameter.  
Stated in [13; 14] by [PERSON] and [PERSON], this method allows the disturbance compensation dynamics to be adjusted separately from the controller dynamics, since the two dynamic modes are completely decoupled. It can be derived from Equations (1) and (19) that, if \(\text{C}|f(k)-f(k-1)|\leq\Omega_{2}\) holds with \(|1-g|<1\), then the compensation error satisfies \(\left|\overline{u}_{d5}\right|\leq\Omega_{2}/g\). Hence, this compensator is suitable for a slowly varying disturbance.  
Agreeing with Equations (17) and (19), it is deduced that \(u_{d4}(k)\) can be transformed into a form Equation (20) similar to \(u_{d5}(k)\):  
\[u_{d4}(k)=u_{d4}(k-1)+[s(k)-(1-q)s(k-1)+\xi\text{sign}(s(k-1))] \tag{20}\]  
Different from SDC \(u_{d4}(k)\), an additional control parameter \(g\) is introduced into DDC \(u_{d5}(k)\), which enables the reduction of the disturbance compensation error \(\overline{u}_{d5}\).
## 4 Conclusions and Future Work  
This paper presents a new deep learning model (EnGS-DRG) to improve the forecasting accuracy of traffic flow forecasting on road networks. By employing the multi-view clustering theory to quantize the correlation degree among traffic nodes, a dynamic graph reconfiguration algorithm was designed to reduce the irrelevant spatio-temporal traffic nodes. After that, the GCN and Seq2 Seq were subtly merged into a spatio-temporal learning module. The advantages of the proposed EnGS-DRG model were validated by comparing two real traffic datasets with eight traffic forecasting baseline methods at 15, 30, and 60 min forecasting intervals.  
However, this paper still has its limitations: (a) The forecasting model only considers traffic data and does not take into account external factors affecting the traffic flow. (b) As  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Dataset** & **Intervals** & **Metrics** & **HA** & **ARIMA** & **VAR** & **SVR** & **FNN** & **FC-LSTM** & **STGCN** & **DCRNN** & **EnGS-DGR** \\ \hline \multirow{6}{*}{PEMS-BAY} & \multirow{2}{*}{15 min} & MAE & 2.88 & 1.62 & 1.74 & 1.85 & 2.20 & 2.05 & **1.36** & 1.38 & 1.39 \\  & & RMSE & 5.59 & 3.30 & 3.16 &  
the traffic data are not sufficient, manual data interpolation is still required. (c) For a single traffic flow forecasting model, it is difficult to balance the forecasting errors. In the future, the occasion of dynamic graph reconfiguration could be defined as an event-driven approach involving traffic factors such as traffic congestion and accidents. Then, a data interpolation module will be supplied to reduce the data interpolation work in data preprocessing. Meanwhile, the deep learning models that utilize the ensembling of different GCN-based traffic forecasting methods will be discussed to capture the spatio-temporal features for either short-term traffic forecasting or long-term traffic forecasting.  
Conceptualization, S.-Y.H. and J.Z.; methodology, S.-Y.H. and Q.Z.; validation, Q.-W.S. and Q.Z.; writing--original draft preparation, Q.Z.; writing--review and editing, S.-Y.H. and J.Z.; supervision, Y.-H.C.; project administration, J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under Grants 61903156 and 61873324, the Natural Science Foundation of Shandong Province for Key Project under Grant ZR2020 KF006, the Natural Science Foundation of Shandong Province under Grant ZR2019 MF040, the University Innovation Team Project of Jinan under Grant 2019 GXRC015, the Higher Educational Science and Technology Program of Jinan City under Grant 2020 GXRC057, and the State Scholarship Fund of the China Scholarship Council.  
Not applicable.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Interval Time** \\ **Comparison** \\ \end{tabular} } & \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**Threshold**} & \multicolumn{3}{c}{**Day 2**} \\  & & & **CON (Mean \(\pm\) SD)** & **NAB (Mean \(\pm\) SD)** & **ES (CI 95\%)** \\ \hline \multirow{4}{*}{60\({}^{\prime}\)Post-0\({}^{\prime}\)Post} & GPx (U) & \(-\)0.20 & 150.49 \(\pm\) 400.30 & \(-\)37.03 \(\pm\) 662.86 & \(-\)0.42 (\(-\)1.62; 0.77) \\  & GR (U) & \(-\)0.20 & \(-\)182.87 \(\pm\) 311.50 & \(-\)42.79 \(\pm\) 489.40 & 0.41 (\(-\)0.75; 1.56) \\  & Cat (U) & \(-\)0.20 & 4.54 \(\pm\) 13.78 & \(-\)2.18 \(\pm\) 6.66 & \(-\)0.44 (\(-\)1.17; 0.29) \\  & LDH (U) & 0.20 & \(-\)28.18 \(\pm\) 44.97 & 11.33 \(\pm\) 33.65 & 0.80 (0.01; 1.59) \({}^{*}\) \\ \hline \multirow{4}{*}{60\({}^{\prime}\)Post-Pre} & GPx (U) & \(-\)0.20 & 32.26 \(\pm\) 417.05 & \(-\)124.95 \(\pm\) 463.97 & \(-\)0.34 (\(-\)1.27; 0.59) \\  & GR (U) & \(-\)0.20 & \(-\)37.52 \(\pm\) 677.05 & \(-\)116.70 \(\pm\) 856.56 & \(-\)0.11 (\(-\)1.10; 0.89) \\ \cline{1-1}  & Cat (U) & \(-\)0.20 & 7.38 \(\pm\) 15.86 & \(-\)0.16 \(\pm\) 6.41 & \(-\)0.43 (\(-\)1.21; 0.36) \\ \cline{1-1}  & LDH (U) & 0.20 & \(-\)51.87 \(\pm\) 40.49 & 19.43 \(\pm\) 48.68 & 1.59 (0.62; 2.56) \({}^{*}\) \\ \hline \multirow{4}{*}{15\({}^{\prime}\)Post-15\({}^{\prime}\)Pre} & ThQ (\({}^{\circ}\)C) & 0.20 & 0.76 \(\pm\) 0.42 & 1.09 \(\pm\) 0.49 & 0.72 (\(-\)0.39; 1.82) \\ \cline{1-1}  & ThG (\({}^{\circ}\)C) & 0.20 & 0.37 \(\pm\) 0.50 & 0.44 \(\pm\) 0.71 & 0.12 (\(-\)1.14; 1.39) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Values variation of plasma enzyme activity and lower limb thermography after non-alcoholic beer supplementation on the second running day. CON, n = 10; NAB, n = 10. \({}^{*}\) Large ES, 0\({}^{\prime}\) value is not included in CI. In the rest of results, the ES was small or trivial, without clinical relevance. On the third and last running day (Table 4), the only clinically relevant result was in the GPx antioxidant enzyme, since when comparing the non-alcoholic beer supplementation effect 1 h after its intake (60\({}^{\prime}\)Post-0\({}^{\prime}\)Post) in this enzyme activity relative to the CON group, the ES was large (d = \(-\)1.02). Considering the ES negative sign and that the IC was quite far from '0', this suggests that non-alcoholic beer intake could be very likely harmful as regards its clinical interpretation. This is also strengthened by the other interval results (60\({}^{\prime}\)Post-Pre), where for the same GPx enzyme, the ES was medium and negative (d = \(-\)0.60), with one of the IC limits quite close to '0' (0.14). Other results with a medium ES in the 60\({}^{\prime}\)Post-0\({}^{\prime}\)Post difference were observed in the GR antioxidant enzyme (d = \(-\)0.58, according to Table 2) and in the LDH enzyme (d = 0.59, as in Table 3), suggesting a harmful action probability with NAB vs. CON. The rest of the results showed a small ES (e.g., ROI temperature means by thermographic assessment) or a clinically irrelevant ES for all other enzymatic activities.
## 4 Methodology
### Measures  
This section describes the measures that were used in this study. Both the _SPP knowledge_ and _SPP skills_ were measured using a reversed single-item question from [PERSON] [10] that measured the procurers' perceived insufficiency of knowledge regarding GPP. The question was reversed to _\"I have enough knowledge to professionally procure sustainably\"_ to increase the fit with the other questions. This question was also rephrased into _\"I have enough skills to professionally procure sustainably\"_ to measure the perceived degree of skills. The items were both measured on a five-point Likert scale, ranging from completely disagree to completely agree.  
_Affective commitment to change_ was measured using the eight-item scale developed by [PERSON] and [PERSON] [55]. This scale has been used very frequently, and its robustness has been proven [56], including in the context of measuring the commitment to SPP [10]. The scale has six items that are measured on a five-point Likert scale, ranging from completely disagree to completely agree. Two items were phrased negatively and were reversed prior to the analysis. The following two items were reversed: _\"I think that the management is going in the wrong direction by implementing SPP\"_ and _\"my organisation's procurement is better off without SPP\"_. The scale has a Cronbach Alpha of 0.876.  
The perceived _organisational learning capacity_ of the organisations was measured using the scale by [PERSON], [PERSON] and [PERSON] [54]. The scale has been validated in other studies and found to be robust. The scale consists of 14 items related to idea development, room for risk taking, as well as interaction with the environment, communication, and participation in decision-making. An example of an item is _\"people here receive support and are encouraged to present new ideas\"_ or _\"employees are encouraged to communicate\"_. The items are measured on a five-point Likert scale, ranging from completely disagreeto completely agree. OLC is an organisational-level construct, unlike the other variables. However, like [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] (2016), we chose to measure it on an individual level. As [PERSON] et al. (2016) explained, this also fits the scale's original proposal, as [PERSON] et al. (2016) intended that individuals in organisations would fill out the form and evaluate the OLC level. While there is an increasing amount of (multilevel) studies that aggregate individual level perceptions to the level of the organisation, for our study it does not matter if individual employees agree about the level of OLC. We are interested in testing if employees perceive that there is a high level of OLC and whether that affects the perceived implementation of the various types of SPP. We have therefore opted not to conduct a multilevel study. While the original scale by [PERSON] et al. (2016) consisted of five components, a direct Oblimin factor analysis found only three components in our study. Each component has a total Eigen value of more than one. A Kaiser-Meyer-Olkin (KMO) test showed that the data is suitable for a factor analysis (0.873) (K