esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrating Assessment in a CSCL Macro-Script Authoring Platform
###### Abstract
As proposed by IMS-LD (IMS-LD Consortium 2003 [13]), the life cycle of a collaboration script comprises four different phases: 1--Design; 2--Instantiation; 3--Enactment; 4--Evaluation. Although this definition is more than 20 years old, there is no commonly accepted CSCL platform to accommodate all of these phases. This clearly denotes the difficulty of the task, taking into consideration all the aforementioned parameters. Moreover, some of the proposed platforms for the definition of the design phase of a script may require from their users an extensive IT knowledge (e.g., complex XML definition of scripts) or they offer a limited set of predefined patterns. These characteristics may fuddle instructional designers or teachers to author their own scripts or adapt others' scripts to the needs of a specific classroom. Other tools try to facilitate users by providing graphical user interfaces to their functionality. For example, _Web Collage_[8] is a software IMS-LD authoring tool extending the ideas of its predecessor tool named \"Collage\" [14] with the integration of assessments capabilities. As its predecessor, it relies on predefined Collaborative Learning Flow Patterns (CLFPs) to synthesize new collaborative activities by combining well-known pedagogical practices. These predefined patterns will enable novice designers to exploit the knowledge and experience of the experts who designed those patterns. The idea of predefined patterns is also followed for assessment design patterns offering a useful amount of expertise using eminent assessment procedures. In this paper, we will present the assessment-related aspects of COSTLyP, which is a platform that aims to overcome some of the difficulties one meets when dealing with CSCL script development [15]. The original research question for the creation of COSTLyP is to provide useful and meaningful means for educational designers or teachers to author and deploy CSCL scripts with assessment provisions in a formal manner. This manner should encode CSCL scripts in a consistent and concise way and provide a methodology to facilitate scripts' integration in the everyday reality of an actual classroom leading up to their enactment. A comfortable and concrete solution to the above, will enable more educational practitioners to be involved in the script authoring and deployment process, also taking into account the important constituent of assessment. We claim that, initially, COSTLyP's usage requires no specialized knowledge of IT and, as preliminary evaluation tests show, scripts' authors and teachers may use it after a small introductory phase. It deals with the first three phases of a CSCL script's life cycle (see Figure 1). The rest of this paper is structured as follows: the following section briefly describes the COSTLyP platform. In Section 3, the implementation of assessment-related aspects of COSTLyP are discussed as means for authoring adaptable CSCL scripts aiming to, among others, cultivate or promote member's regulation skills. In the last section, conclusions and plans for future work are presented.
## 3 Scalable On-line Anomaly Detection System (SOADS)
### Architecture of SOADS
#### 3.1.4 Frequent Subsequence Miner  
The Frequent Subsequence Miner derives high-frequency subsequence patterns using the sequential pattern algorithm. The sequential pattern is also referred to as a sequential association rule, and it identifies sequential patterns that commonly appear in the data by adding the concept of time to the association rules [51]. The sequential patterns can be largely separated into Apriori series and Pattern-Growth series. The representative algorithm of the Apriori series is the GSP (Generalized Sequential Pattern) developed by IBM Research [52]. For the Pattern-Growth series, PrefixSpan [53] developed by Simon Fraser University is a typical example. This study uses the PrefixSpan algorithm, which improves the speed of performance by omitting the process of generating candidate patterns.  
Figure 5 shows examples of the input and output data of the Frequent Subsequence Miner.
# Overview of Tool Wear Monitoring Methods Based on Convolutional Neural Network
## 4 Information Data of Indirect Online Measurement on Tool Wear
### Temperature  
Cutting heat is an essential physical phenomenon in the metal cutting process. Tool wear and damage cause a sudden increase in cutting temperature. There are three main ways to measure cutting temperature. (1) The natural thermocouple composed of the tool and the workpiece measures the average temperature of the cutting zone. The different materials of the tool and the workpiece need to be calibrated. (2) A thermocouple, composed of two metal wires fixed at a point in the tool body, measures the temperature at a certain distance from the tool edge. The method has the problems of time-consuming preparation and a slow response to temperature changes. An infrared thermal imaging camera measures the temperature distribution in the cutting zone, which has the characteristics of a high sensitivity and short response time. However, the instrument is complicated to operate, expensive, and difficult to focus on. Additionally, it is difficult to measure the temperature in the cutting coverage area. Researchers conducted many studies on the relationship between cutting edge temperature and tool wear [79; 80; 81; 82; 83]. [PERSON] et al. [84] developed a method for predicting the temperature of the tool-chip contact area based on a regression analysis and the BP neural network, in order to estimate the tool wear condition accurately. [PERSON] et al. [85] proposed a work-tool thermocouple method to determine the electromagnetic/thermal field feature generated by the interaction between the coating and the workpiece. It suggested that the high temperature at the cutting tool edge has a controlling effect on the wear rate of the cutting tool. [PERSON] et al. [86] established a mathematical model of milling cutter flank wear by obtaining the cutting force and cutting temperature in real time based on determining the wear mechanism.  
The cutting temperature depends on the material thermal properties of the workpiece and the tool, and the coolant weakens the correlation between the cutting temperature and the tool wear condition. Therefore, some scholars questioned the feasibility of monitoring methods based on temperature features. Generally, the sensitivity of the temperature sensor is relatively low, and can be easily disturbed by the signal of the environment and affected by the temperature drift of the preamplifier. It is not suitable for measuring small temperature changes.  
The installation diagram of a temperature sensor is shown in Figure 3.  
Figure 3: An installation diagram for temperature sensor (drawn from the literature [83]).
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref.** & [8] & [11] & [15] & [17] & [19] & [21] & **This Work** \\ \hline Technology & Stepped ridge & Antipolal slot line & Square probe & E-plane probe & Quasi-Yagi antenna & Common probe & Probe with patch \\ \hline Working freq. (GHz) & 50–72 & 70–110 & 80–110 & 75–110 & 234–314 & 66–90 & 70–100 \\ \hline Imp. BW (\%) & 36 & 44 & 31.5 & 40 & 29 & 32.5 & 44 \\  & (\(\rm{[S_{11}<-10\,dB]}\)) & (\(\rm{[S_{11}<-11\,dB]}\)) & (\(\rm{[S_{11}<-10\,dB]}\)) & (\(\rm{[S_{11}<-10\,dB]}\)) & (\(\rm{[S_{11}<-10\,dB]}\)) & (\(\rm{[S_{11}<-10\,dB]}\)) & (\(\rm{[S_{11}<-15\,dB]}\)) \\ \hline \(\alpha_{r}/\tan\delta\) & 9.4/0.001 & 32./0.0045 & 9.9/N.A & 12.9/N.A & 3.78/N.A & 2.2/0.0009 & 2.2/0.0009 \\ \hline Insertion Loss (dB) & \(<\)0.7 & \(<\)1.8 & \(<\)0.7 & \(<\)0.5 & 0.8 \({}^{*}\) & \(<\)1 & 1.2 \({}^{*}\) \\ \hline Size & large & large & compact & compact & compact & compact & compact \\ \hline Fabrication & high & low & high & high & high & low & low \\ complexity & & & & & & & \\ \hline \multicolumn{7}{l}{\({}^{*}\) indicates average insertion loss.} \\ \end{tabular}
\end{table} 
Table 2: Comparison among the transitions from the transmission line to rectangular waveguide.
## 4 Results  
The results are evaluated for images with different contrast-to-noise ratios (CNR). The CNR of an image is calculated as the average CNR value over all image columns, while the CNR of one column is calculated with the following equation:  
\[CNR=\frac{|\overline{x_{\text{lam}}}-\overline{x_{\text{tur}}}|}{\sqrt{\sigma _{\text{lam}}^{2}-\sigma_{\text{tur}}^{2}}}, \tag{16}\]  
where \(\overline{x_{\text{lam}}},~{}\overline{x_{\text{tur}}}\) are the mean temperature values and \(\sigma_{\text{lam}}^{2}\), \(\sigma_{\text{tur}}^{2}\) are the standard deviations of the temperatures in the laminar and turbulent region of the rotor blade, respectively.  
For verification and validation purposes, the Bayesian approach is tested on four kinds of data. First, a thermographic image is simulated, which means that the laminar-turbulent transition position is set manually, and the temperature values on the rotor blade exactly follow the error function introduced in Equation (2). This simulation process is carried out for every image column individually, which represents the chord axis. The simulated image is similar to the measurement (since it is designed on the basis of a measurement thermogram) but with an extraordinarily high CNR. As a result, the detected transition positions for the different image columns will always yield the given true value of the transition position for the simulated image. Second, a measured thermogram is used. Third and fourth, in order to evaluate the method on images of varying quality, which means thermograms with a different CNR, artificial Gaussian noise with the expectation value \(\mu_{w}=0\) is added and the effect of different noise standard deviations \(\sigma_{w}\) is studied. As an example, Figure 2 shows, on the left, a simulated image (a) and a preprocessed thermogram with no added noise (c) and, thus, a high CNR value. At the right, the same images are shown with added noise, with a reduced similar CNR of 6.7 (b) and 6.3 (d), respectively. In Section 4.1, the simulation-based images are used to verify the Bayesian method for including previous knowledge and to compare its performance with the classical non-Bayesian estimation as a reference. Subsequently, in Section 4.2, the findings are validated for real, measured thermograms. Application scenarios to make use of the Bayesian method are presented in Section 4.3, which includes a discussion of suitable priors.
## 2 Data and Methods
### Data Processing Method
#### 2.2.1 NDVI Anomaly as the Indicator of Droughts  
Drought was defined as relative to the statistical average for one or several seasons or years. The relative _NDVI_ anomaly renders one of the simplest _NDVI_-based approaches for monitoring drought [42]. We used Terra MODIS _NDVI_ to monitor the drought variation in Mongolia during the vegetation growing season (May to September) from 2003 to 2018. To derive _NDVI_ anomaly, the individually mean _NDVI_ (\(NDVI_{\text{mean}i}\)) was determined as the annual _NDVI_ using Equation (1) following [PERSON] et al. [14]:  
\[NDVI_{\text{mean}i}=(NDVI_{1}+NDVI_{2}+\ldots\ldots,NDVI_{n})/n \tag{1}\]  
where \(NDVI_{\text{mean}i}\) is mean _NDVI_ value of the growing season of the \(i\)th year, and \(NDVI_{1}\) is the first 16-day _NDVI_ composite to \(NDVI_{n}\), which is the last 16-days _NDVI_ composite during the growing season of the \(i\)th year.  
In addition to that, the growing season long-term mean _NDVI_ (2003-2018) was calculated using Equation (2) following [PERSON] et al. [43]:  
\[\overline{NDVI}=\sum_{i=1}^{n}\frac{NDVI_{\text{mean}i}}{n} \tag{2}\]  
where \(n\) is the number of years, which is equal to 16 years.  
We derived the spatial patterns of mean growing season _NDVI_ anomaly for each grid cell using Equation (3) in the study area [44]:  
\[NDVI_{anomaly\ i}=\frac{NDVI_{\text{mean}i}-\overline{NDVI}}{\overline{NDVI}} \times 100 \tag{3}\]  
Figure 2: Methodological flow chart for NPP (net primary productivity) response to drought events. NDVI, normalized difference vegetation index; BEPS, Boreal Ecosystem Productivity Simulator; GPP, gross primary productivity; AWC, available soil water-holding capacity; DEM, digital elevation model.  
where \(NDVI_{anomaly}\) is the percentage of the growing season at a grid cell during the \(i\)th year, \(NDVI_{\text{meani}}\) is the individual mean growing season, and \(\overline{NDVI}\) is the mean growing season from 2003 to 2018.  
Here, we defined drought conditions as a negative variation of the \(NDVI\), while positive values indicated normal or wet conditions [45]. Based on the \(NDVI\) anomaly classification scheme, the drought severity was classified into mild drought, moderate drought, severe drought, and extreme drought. Details about the categorization of dryness/wetness grade of \(NDVI\) anomaly are described in Table 1.
# Manipulator Smooth Control Method Based on LSTM-XGboost and Its Optimization Model Construction
## 5 Discussion  
In the field of manipulator control, achieving efficient, safe, and stable motion control remains a continuous challenge, especially when addressing complex tasks and environments that require accurate trajectory prediction and control. A novel solution is presented in the form of a hybrid machine learning model based on LSTM and XGBoost, which aims to predict the motion stability of the manipulator. The inclusion of LSTM enables effective modeling of time-dependent characteristics in the data, while XGBoost's incorporation allows for capturing complex non-linear relationships. The combination of these methods enhances prediction performance.  
The model is trained on actual motion data from the manipulator, including parameters such as speed, acceleration, and displacement. This data-driven training approach bolsters the model's applicability in real-world scenarios and its generalization capability. Training results reveal that the prediction error for a single-action index is minimal, demonstrating impressive prediction prowess.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Type** & **MAE** \\ \hline \(|a|_{\text{are}}\) & 0.0032 \\ \(\delta_{a}\) & 0.0024 \\ \(\theta_{\text{$p$}}\) & 0.0027 \\ \(\delta_{\text{$v$}}\) & 0.4721 \\ jerk & 0.0051 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation of Model Prediction Effect.  
Figure 6: Stability prediction score.  
The model's performance was evaluated not only on individual action indices, but also on complete motion sequences. Given a series of command sequences, the model can provide the motion index for a sample, showing consistency. Furthermore, the low average absolute error of the five indicators suggests high prediction accuracy.  
However, despite its overall effective prediction capability, some discrepancies have been observed between the model's score predictions and the actual scores. These discrepancies likely arise from inherent model errors, suggesting potential avenues for further optimization to enhance prediction accuracy.  
In conclusion, the manipulator stability prediction model based on LSTM-XGBoost provides a new and effective method for predicting and controlling the motion state of robot arms. Future work aimed at refining this model could hold significant promise for the broader applicability and optimization of robotic arm control.
## 2 Experiments
### Dust Emission in CHIMERE  
The mineral dust emissions are calculated using the last version of the emission module described in previous studies [46; 48; 64]. In this model version, the [PERSON]'s [65; 66] mineral dust emissions parameterization is proposed, in addition to the schemes described in [67; 68]. The vertical dust flux \(F_{d}\) is calculated as described in Equation (1):  
\[\begin{split} F_{d}=C_{d}f_{bare}f_{clay}\frac{\rho_{a}(u_{s}^{2}- u_{st}^{2})}{u_{st}}\bigg{(}\frac{u_{s}}{u_{st}}\bigg{)}^{C_{a}\frac{u_{st}-u_{ st}\where \(f_{ture}\) and \(f_{clay}\) represent the relative fraction of bare soil and clay soil content, respectively; \(\rho_{a}\) is the air density; and \(C_{d}\) the coefficient of erodibility. The flux is calculated only if \(u_{*}>u_{*t}\), the threshold friction velocity, \(u_{*t}\), is calculated using two possible schemes up to the user [69; 70]. The corresponding \(u_{*st}\) is this friction velocity but for a standard atmospheric density. The vertical dust flux is integrated over the whole size distribution of particles. The Dust Production Model and its parameters are fully described in [46; 71]. To have global and highly spatially resolved surface databases, the module uses the NCAR USGS landuse dataset [72] and the STATSGO-FAO soil dataset [73]. The roughness length is estimated using the global 6 km horizontal resolution \"Global Aeolian Roughness Lengths from ASCAT and PARASOL\" dataset [74]. The soil erodibility is refined over desert areas using the global erodibility dataset derived from MODIS (Moderate Resolution Imaging Spectroradiometer) data [75], Figure 1 represents the erodibility of soils for the CHIMERE domain, deserts can be clearly identified but large agricultural zones over the US, Europe and Asia are also highlighted in light blue. Some soil and surface parameters are added to limit the emissions fluxes, depending on the period to model and its meteorological conditions. To consider the monthly vegetation variability, the vegetation fraction is diagnosed from the USGS 30s resolution database (from U.S. Geological Survey, accessible at [[https://landcover.usgs.gov/](https://landcover.usgs.gov/)]([https://landcover.usgs.gov/](https://landcover.usgs.gov/))) and is used as limiter to the erodibility factor. The rain may also inhibit the emission flux and a function is added to stop the fluxes after a precipitation event [46]. The flux may also be limited by the soil humidity and the threshold friction velocity is limited using the [PERSON]'s scheme [76]. Higher in latitude over Alaska, Canada, Siberia and Greenland, dust emissions can potentially occur [77] due to additional processes specific to or enhanced in cold regions: low temperatures, humidity, strong winds, permafrost and niveo-aeolian processes. Most of these processes are not included in CHIMERE, however for an early spring period such as in this study, the emissions would be turned off due to the snow coverage in these regions.
## 4 Full-Duplex Single-Antenna Energy Harvesting
### Optimal Power Allocation Scheme  
As above, when we find the optimal power allocation scheme, we only need to discuss the range of \(\alpha\in 0,\alpha_{1}\). The signal to interference and noise ratio at this time is represented by \(\text{SINR}\ =\frac{\eta P_{s}|h_{1}|^{2}|g|^{2}\alpha}{N_{0}(1-\alpha)}\), so the objective function can be expressed as follows  
\[\underset{P_{s}}{max}\xi(P_{s})=\frac{(1-\alpha)log_{2}\bigg{(}1+\frac{\eta P_ {s}|h_{1}|^{2}|g|^{2}\alpha}{N_{0}(1-\alpha)}\bigg{)}}{\beta P_{s}+P_{c}}, \mathbf{s.t.}0<P_{s}<P_{max}, \tag{41}\]  
\[\frac{\partial\xi(P_{s})}{\partial P_{s}}=\frac{\eta|h_{1}|^{2}|g|^{2}\alpha( \beta P_{s}+P_{c})-\beta\bigg{(}(1-\alpha)N_{0}+\eta P_{s}|h_{1}|^{2}\big{|}g \big{|}^{2}\alpha\bigg{)}\eta\delta_{2}\bigg{(}1+\frac{\eta P_{s}|h_{1}|^{2}|g| ^{2}\alpha}{N_{0}(1-\alpha)}\bigg{)}ln2}{\bigg{(}1+\frac{\eta P_{s}|h_{1}|^{2} |g|^{2}\alpha}{N_{0}(1-\alpha)}\bigg{)}(\beta P_{s}+P_{c})^{2}N_{0}ln2}. \tag{42}\]  
In a similar way, if \(\frac{\partial\xi(P_{s})}{\partial P_{s}}\bigg{|}P_{s}=P_{s2}=0\), \(\xi(P_{S})\) is a monotonically increasing function of \(P_{S}\) in the region of \(0,P_{S2}\), \(\xi(P_{S})\) will be a monotonically decreasing function of \(P_{S}\) in the region of \((P_{S2},P_{max})\), where \(P_{s2}\) is the optimal \(P_{S}\)'. Otherwise \(P_{max}\) will be the optimal \(P_{s}\)'.  
If Equation (42) is equal to zero, we get  
\[\begin{split}&\eta|h_{1}|^{2}\big{|}g\big{|}^{2}\alpha\beta P_{s}- \beta\bigg{(}(1-\alpha)N_{0}+\eta P_{s}|h_{1}|^{2}\big{|}g\big{|}^{2}\alpha \bigg{)}\cdot\\ & ln\bigg{(}N_{0}(1-\alpha)+\eta P_{s}|h_{1}|^{2}\big{|}g\big{|}^ {2}\alpha\bigg{)}+\beta\bigg{(}(1-\alpha)N_{0}+\eta P_{s}|h_{1}|^{2}\big{|}g \big{|}^{2}\alpha\bigg{)}ln(N_{0}(1-\alpha))=-\eta|h_{1}|^{2}\big{|}g\big{|}^{ 2}\alpha P_{c}.\end{split} \tag{43}\]  
Equation (43) can be simplified to  
\[ln\frac{y_{4}}{e(1-\alpha)N_{0}}=W\Bigg{(}\frac{\beta(\alpha-1)N_{0}+\eta|h_{ 1}|^{2}\big{|}g\big{|}^{2}\alpha P_{c}}{\beta e(1-\alpha)N_{0}}\Bigg{)}. \tag{44}\]  
where \(W\) is the Lambert function and \(y_{4}=N_{0}(1-\alpha)+\eta P_{s}|h_{1}|^{2}\big{|}g\big{|}^{2}\alpha\).  
Therefore, we can get the optimal power allocation \(P_{s}\)', which is expressed as  
\[P_{s} \tag{45}\]
## 3 Towards Autonomous SC with Tenderfone
### An Agent-Oriented Language for SC
#### 3.1.3. Reactiveness to Time  
Tenderfone assumes that the underlying BCT provides a global notion of (logical) time, \(T_{g}\), maintained through consensus, hence consistently observable every time a new block is created--a hypothesis which is likely to hold given that virtually all practical consensus algorithms leverage on some shared notion of time-round to circumvent the FLP impossibility theorem [38]. The following built-in predicates are then available to Tenderfone developers, to let smart contracts observe time (time-awareness) and act depending on it (reactiveness to time).  
* now(-Timestamp) is a built-in predicate aimed at letting SC inspect the current time. It works by retrieving the last value of \(T_{g}\) shared through consensus, that is, the value of \(T_{g}\) stored in the block whose commit has triggered execution of the current smart contract. Hence, multiple invocations of this predicate occurring in the same callback will always get the same value for Timestamp.
* when(@Instant, +Goal) is a built-in predicate aimed at letting SC postpone a computation for a future moment in time. It works by scheduling the execution of the receive(Goal, Me) callback, assuming Me is the executing smart contract ID, as soon as \(T_{g}\geq\texttt{Instant}\), as a way to delay a computation to an absolute point in time.
* delay(@Interval, +Goal) is a built-in predicate aimed at letting SC delay a computation for a relative amount of time. It works by scheduling the execution of the receive(Goal, Me) callback as soon as \(T_{g}\geq T^{\prime}_{g}+\texttt{Interval}\), where \(T^{\prime}_{g}\) is the value of the global time when delay/2 was invoked.
* periodically(@Period, +Goal) is a built-in predicate aimed at letting SC execute a computation periodically. It works by _(i)_ triggering the receive(Goal, Me) callback other than _(ii)_ scheduling the future execution of the same callback for the first possible instant \(T_{g}\geq t_{0}+\texttt{Period}\), where \(t_{0}\) is the value of the global time when receive/2 was first invoked. Such process is repeated again as soon as the receive/2 callback is executed twice. The repetition of this process over and over again produces a periodic series of executions of the receive/2 callback, which is unlimited in length. The temporal distance among any two consecutive executions of the receive(Goal, Me) is constant and is equal to Period units of time. The series is interrupted as soon as some execution of receive(Goal, Me) fails.  
Notice that in the description above, we assumed the variables Timestamp and Instant represent an instant in time, such as datetime(Y, M, D, HH, MM, SS), whereas variables Interval, Delay, and Period represent an interval of time, such as seconds(SS),..., years(Y) or any combination thereof.