esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments
### Ablation Study  
To assess the impact of the DWA scheme, we conducted a comparative analysis against the following methods which determined the weight of the muti-loss function: (a) Equal weighting (EW), and (b) Grid search (GS). It is worth noting that we selected the best relative performance among all parameter combinations as the final result of the grid search. As shown in Table 4, compared to EW, GS's performance has an improvement in mAUC and BCA and a decrease in adasMAE. However, DWA improved in all metrics and performed best among all methods. DWA improved over EW by 0.6%, 0.8%, and 3.3% in each metric, respectively. This indicates that despite utilizing grid search, achieving a balance between different tasks with fixed weights still remains challenging. Consequently, for multi-task learning, utilizing a dynamic weight scheme may be a more favorable approach which can also be employed in other health care scenarios.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **mAUC (\(\uparrow\))** & **BCA (\(\uparrow\))** & **adasMAE (\(\downarrow\))** \\ \hline EW & 0.936 \(\pm\) 0.0139 & 0.872 \(\pm\) 0.0114 & 4.421 \(\pm\) 0.32 \\ GS & 0.937 \(\pm\) 0.0140 & 0.879 \(\pm\) 0.0128 & 4.449 \(\pm\) 0.42 \\ DWA & **0.942 \(\pm\) 0.0091** & **0.879 \(\pm\) 0.0098** & **4.280 \(\pm\) 0.40** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Model performance of different weighting methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **mAUC (\(\uparrow\))** & **BCA (\(\uparrow\))** & **adasMAE (\(\downarrow\))** \\ \hline LSTM-F & 0.922 \(\pm\) 0.0124 & 0.869 \(\pm\) 0.0117 & 4.299 \(\pm\) 0.25 \\ LSTM-M & 0.916 \(\pm\) 0.0134 & 0.864 \(\pm\) 0.0136 & 4.284 \(\pm\) 0.37 \\ MinRNN & **0.944 \(\pm\) 0.0080** & **0.882 \(\pm\) 0.0143** & **4.160 \(\pm\) 0.39** \\ GRU-MF & 0.927 \(\pm\) 0.0140 & 0.874 \(\pm\) 0.0145 & 4.536 \(\pm\) 0.72 \\ Ours & 0.942 \(\pm\) 0.0091 & 0.879 \(\pm\) 0.0098 & 4.280 \(\pm\) 0.40 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model performance comparison with other methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **mAUC (\(\uparrow\))** & **BCA (\(\uparrow\))** & **adasMAE (\(\downarrow\))** \\ \hline LSTM-EDL & 0.914 \(\pm\) 0.0129 & 0.842 \(\pm\) 0.0110 & **4.432 \(\pm\) 0.47** \\ GRU-EDL & **0.937 \(\pm\) 0.0068** & 0.846 \(\pm\) 0.0662 & 4.720 \(\pm\) 0.83 \\ MinRNN-EDL & **0.937 \(\pm\) 0.0140** & **0.879 \(\pm\) 0.0128** & 4.449 \(\pm\) 0.42 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model performance of different backbones.
# Semi-Supervised Ridge Regression with Adaptive Graph-Based Label Propagation
## 2 Related Works
### Label Propagation  
Suppose there is a sample set \(X=[x_{1},x_{2},...,x_{l},x_{l+1},...,x_{u}]\in R^{d\times n}\) from \(c\) class, in which the first \(l(l<n)\) samples \(X_{l}=[x_{1},x_{2},...,x_{l}]\in R^{l\times n}\) are labeled and the rest \(n-l\) samples \(X_{u}=[x_{l+1},x_{l+2},...,x_{n}]\in R^{(n-l)\times n}\) are unlabeled. Let \(Y=[y_{1},y_{2},...,y_{n}]\in\{0,1\}^{c\times n}\) be a label matrix. Specfically, we set \(y_{ij}=1\), if \(x_{i}\) is a labeled sample belonging to the \(j\)-th class, otherwise \(y_{ik}=0\,(k\
eq j)\). The core purpose of the label propagation is to estimate the labels of the unlabeled data. We define \(G\) as a weighted undirected graph, in which each node corresponds to a data sample in \(X\). The weight of edge between \(x_{i}\) and \(x_{j}\) is defined as  
\[W_{ij}=\left\{\begin{array}{ll}\exp(-\frac{||x_{i}-x_{j}||_{2}^{2}}{2\sigma^ {2}}),\text{ if }x_{j}\in N_{k}(x_{i})\text{ or }x_{i}\in N_{k}(x_{j})\\ 0,\text{otherwise}\end{array}\right. \tag{7}\]  
where \(N_{k}(x_{i})\) is the set of \(k\)-nearest neighbors of \(x_{i}\) and \(\sigma\) is a parameter which determines the decay rate of heat kernel function. Let \(F=[f_{1},f_{2},...,f_{n}]\in R^{c\times n}\) be a predicated label matrix, in which \(f_{i}\in R^{c}\) is the corresponding predicated label vector of \(x_{i}\). In terms of the LP theory [43; 44], the predicated labels of the nodes in graph \(G\) is estimated as  
\[\min\sum_{i,j=1}^{n}||f_{i}-f_{j}||_{2}^{2}W_{ij}+\mu\sum_{i=1}^{n}||f_{i}-y_{ i}||_{2}^{2} \tag{8}\]  
where \(\mu\) is a balance parameter which controls the discrepancy between the predicated label vector and true label vector for the labeled samples. From Equation (7), we can find that the weighted undirected graph in the LP is constructed in advance and remains unchanged during the label propagation procedure. Furthermore, seen from the objective function in Equation (8), the LP can propagate the label information of the labeled samples to the unlabeled ones in the training dataset. However, since the LP fails to provide an explicit classifier, it suffers from the \"out-of-samples\" problem.
## 3 Materials and Methods
### Feature Selection  
Feature selection is the process of removing irrelevant features and improving the model's performance. We applied sequential forward floating selection (SFFS) to select the best combination of features. It is one kind of greedy algorithm to reduce the initial \(d\)-dimensional feature space into a k-dimensional feature subspace (\(k<d\)) [30]. The pseudo-code is shown below:  
**Input:**\(Y=\{y_{1},y_{2},\ldots,y_{d}\}\)  
**Output:**\(X_{k}=\{x_{j}|j=1,2,\ldots,k;x_{j}\in Y\}\), where \(k\in(0,1,2,\ldots,d)\)  
**Initialize:**\(X_{0}=\emptyset,k=0\)  
**Step 1:**: \(x^{+}=\text{argmax }J(X_{k}+x)\), where \(x\in Y-X_{k}\), \(J\) is an evaluation index and \(x^{+}\) is the feature with the highest evaluation when it is chosen.
**Step 2:**: \(x^{-}=\text{argmax }J(X_{k}-x)\), where \(x\in X_{k}\) and \(x^{-}\)  
is the feature with the best performance when the feature is deleted.  
If \(J(X_{k}-x)>J(X_{k})\) :  
\(X_{k-1}=X_{k}-x^{-}\)  
\(k=k-1\)  
Go to **Step 1**.  
In **Step 1**, include the feature of the feature space that leads to the best performance increase from the feature subset. Then, go to **Step 2**. In **Step 2**, remove only a feature in the case of increasing the performance of the result subset. If k = 2 or the improvement cannot be made, go back to **Step 1**; else, repeat **Step 2**. When k = k, terminate this algorithm. In this study, we implemented SFFS using the SequentialFeatureSelctor of the \"mlxtend\" library [31]. We selected the combination of the best features with the highest identification accuracy.
## 2 Result and Discussion
### Working Principle and Performance of SAVWS
Due to the protection of the Zener diode (D\({}_{2}\)), the voltage of C\({}_{2}\) would stop increasing when the voltage was about to 6 V, and then the acceleration was set to 2.5 m/s\({}^{2}\) to simulate the abnormal acceleration of the machine. Under this circumstance, the S-TENG could detect that acceleration had exceeded the threshold and controlled the MEMS switch to wake up the transmitter module. The energy in the C\({}_{2}\) was supplied to the transmitter module to continuously send alarm signals. Due to the voltage monitoring circuit, the transmitter module stopped working as the voltage of C\({}_{2}\) was lower than 2 V until it increased to 2.9 V. In this process, the remote monitoring could receive alarm signals periodically. The acceleration was then reduced to 2 m/s\({}^{2}\), and the MEMS switch turned off the transmitter module. Meanwhile, the voltage of C\({}_{2}\) continued to increase until the next abnormal acceleration came. After a while without receiving alarm signals, the remote monitoring would clean the warning. When the abnormal acceleration came again, the system would send alarm signals once more. In the experimental environment, the maximum transmission distance of the transmitter module was verified to be about 50 m. At last, due to the extremely low leakage current of the MEMS switch, the voltage of C\({}_{2}\) hardly changed after the vibration stopped. Figure S8 demonstrates the ultra-low leakage current of the MEMS switch at different gate-to-source voltages. Video S1 demonstrates the demo of the system. By continuously harvesting vibrational energy in the environment, the SAVWS could enable fully self-powered and continuous acceleration monitoring and alarm, which has wide applications in environmental monitoring of unattended factories.
# Thermal Effects on Photovoltaic Array Performance: Experimentation, Modeling, and Simulation
## 6 Conclusions  
The relationships between ambient temperature, wind speed, and wind direction on PV electrical output were analyzed in this work. Ambient and array temperatures, solar irradiance, wind speed, wind direction, and electrical output from a PV array mounted on top of an industrial building in Varennes, Quebec, were recorded from 2018 to 2019. The experimental results show that the PV array produced power at higher efficiency with lower surface and ambient temperatures. The findings also reveal that efficiency seems to increase linearly with increasing wind velocity; therefore, we conclude that wind velocity is an important parameter in estimating PV performance. Wind direction, however, appears to have little to no effect on array temperature in this case. CFD simulations were performed and showed that temperature along the array surface can vary significantly at lower wind speeds and that this variability decreases at higher wind speeds. Furthermore, the heat transfer contribution from convection becomes more important than radiation at wind speeds greater than 3 m/s, and radiation effects cannot be ignored. Finally, an empirical correlation of heat transfer coefficient/thermal loss factor has been developed and is valid between 1 to 9 m/s for all wind directions, at high solar irradiance of 700 to 1100 W/m\({}^{2}\).  
Conceptualization, J.K. and C.B.-J.; Methodology, J.K. and L.G.; software, L.G. and K.P.; validation, J.K., L.G., A.F. and K.P.; formal analysis, L.G., A.F. and K.P.; investigation, C.B.-J., J.K., L.G., A.F. and K.P.; resources, C.B.-J.; data curation, L.G., A.F. and K.P.; writing--original draft preparation, C.B.-J., J.K., L.G., A.F. and K.P. writing--review and editing, C.B.-J., J.K., L.G., A.F. and K.P.; visualization, L.G., A.F. and K.P.; supervision, J.K., C.B.-J.; project administration, J.K.; funding acquisition, J.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Center of Energy and Sustainability at California State University, Los Angeles, and the National Science Foundation under Award No. HRD-1547723.  
Not applicable  
The data presented in this study are available on the request from the corresponding author.  
The authors declare no conflict of interest.  
Figure 14: Temperature along the length of PV array at different wind speeds.
## 2 Assessment of Satellite-Based Remote Sensing for Cloud Property Retrievals  
### Background  
Optical depth retrievals from satellite radiances are particularly sensitive to assumptions regarding particle phase and single scattering properties as well as instrument calibration [19]. For thick cloud cases, uncertainties in satellite optical depth retrievals are further magnified because of the asymptotic relationship between reflectance and optical depth [12] whereas small differences in reflectance equate to very large differences in optical depth as they tend to become large. This uncertainty likely contributes to much of the scatter in our comparisons. Due to horizontal transport of photons, bias in visible optical depth retrievals from satellite radiances are known to occur when the scale of the satellite retrieval is less than a radiative smoothing scale that depends on cloud geometry [20]. The cloud retrievals from VISST algorithm (GOES) have been compared with active and passive radiometric measurements at surface sites, primarily ARM SGP central facility in Oklahoma [21,22]. Another possible source of bias in optical depth is caused by sub-pixel variability of optical depth. A satellite radiometer measures pixel mean radiance and from this quantity derives an optical depth that equates to an approximation of the logarithmic mean of the optical depth within the pixel. The exact relationship in any given instance between pixel-mean radiance and the desired pixel-mean optical depth depends on the variability of the cloud field within the pixel but would result in the satellite measured COD being biased low (discussed in Section 2.3) [23].
## 3 Results and Analysis
It is evident from Table 3 that the detection of the clamp was higher when features from both channels were used simultaneously to represent a single clamp. In railway applications, it is essential to have both high precision and recall. A higher precision of the algorithm will minimise the false positive rates. A higher precision will  
Figure 8: Learning curve with respect to number of training samples and F1-score for all algorithms: (**a**) Gaussian naive Bayes, (**b**) SVM, (**c**) K-NN, (**d**) RF, (**e**) AB, (**f**) GBDT. The best hyperparameter combinations/values after cross validation are marked above for each algorithm. thus contribute to better detection of the fastener state, thus ensuring safe and reliable operation of the railway. On the other hand, a higher recall of the algorithm will ensure the false negative rates are minimised. A higher recall will thus minimise cost incurred due to unwanted inspection. It is necessary to have a balance between reducing cost of inspection as well upholding the safety and reliability of the railway asset. Hence, the best algorithm was not selected based on the F1-score alone, but also on precision and recall score and the balance between the two, during the test stage. Table 4 represents the results obtained during the testing phase, for all the six algorithms. For the data set obtained from the 18 kHz channel, RF registered the highest precision (93.43%); however, the recall for the same was below 90%. Recall was seen to be highest for the k-NN algorithm with 94.29%, while the precision for the same was around 90%. No algorithm exhibited a good balance between the precision and recall during the testing phase for the 18 kHz data set. For the data from 27 kHz channel, the highest precision (94.16%) was achieved using random forest, with a recall of 90.46%. The highest recall (96.45%) for the 27 kHz data set was achieved with AB, with a precision of 93.92%. The algorithms with higher performance on this data set, however, did not exhibit a good balance between precision and recall. Precision and recall value were well balanced for all the algorithms when the data set included features, from both channels were used simultaneously to represent the clamp. Highest precision and recall were achieved by the AdaBoost algorithm, at 96.64% and 95.52% respectively, with the highest F1-score of 96.02 during testing.
## 4 Results and Discussion
For example, ANN outperforms other single imputation methods on average, but it is worse than pchip when imputing the sensor \"vv-1-b-1/air_flow/flow_input\". Moreover, the best single imputation method selected is different from sensor to sensor. * The difficulty of sensor prediction is different from sensor to sensor. The fan sensor \"ct_control/ct_fan_status/trend_log\" has a relatively low testing accuracy with all the imputation methods in the pool, while sensors such as \"chws_control/chws_temp\" can be easily imputed even with the simplest imputation methods. * To further analyze the single imputation method performance in terms of sensor types, the machine-learning-based imputation method is most suitable for the energy meter, and piecewise-spline imputation methods are suitable for some temperature sensors and some pressure sensors. Although the above conclusion may be biased to the specific building and sensors, the result indicates the importance of ensemble method to automatically customize the selection of imputation method based on different sensor types and missing data characteristics. * To further analyze the result in terms of the five imputation types, machine-learning-based imputation generally has the best overall performance. Besides, machine-learning methods have even better performance improvement, especially when there are large holes in the missing data, such as the sensor \"chws_control/cw_vlv_fdbk\". However, the ensemble method can still always select the best performance among all candidate single imputation methods. To sum up, the results show that different single imputation methods are suitable for different sensor types and missing data characteristics. The results also show that the proposed framework can optimize the accuracy of imputation under different mechanisms and the randomness of data missing for different sensors. The ensemble imputation method outperforms the best single imputation method by 18.2% in the case study. The result also indicates the importance of the ensemble method to automatically customize the selection of the imputation method based on different sensor types and missing data characteristics.
## 3 Materials and Methods
### Tools  
For the analysis of the example traces in this paper we used tcpdump [20], Wireshark [19] and its associated tools, Bro Network Security Monitor (Bro) [21, 22] version 2.1. During the initial investigations Netmate and NetAI as bundled in [23] were used as well.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**ARP** & **NBSS NetBIOS session service** \\ \hline CDP Cisco Discovery Protocol & NDPS Novell Distributed Print System \\ DCERPC & NetBIOS Name Service \\ DCOM OXID Resolver & NTP Network Time Protocol \\ DHCP & PARLAY General Inter-ORB \\ GIOP General Inter-ORB Protocol & RMI Java \\ HP Switch Protocol & RELOAD \\ HTTP & SMB \\ ICAP Internet Content Adaptation Protocol & SMB\_NETLOGON \\ ICMP & SNMP \\ IGMP & SSDP \\ Intel ANS Probe & STUN Session Traversal Utilities for NAT \\ ISystemActivator & STP \\ LANMAN Remote Api & TCP \\ LLDP Link Layer Discovery Protocol & TPNCP TrunkPack Network Control Protocol \\ LLMNR Link-Local Multicast Name Resolution & UDP \\ Microsoft Windows Browser Protocol & VRRP Virtual Router Redundancy Protocol \\ MySQL & X11 \\ NBNS & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Protocols seen in the traffic captures.  
Netmate and NetAI were used to calculate flow statistics from the traffic captures to aid in the initial analysis of the packet capture data. At a later stage Bro was mostly used for generating the connection information and the usage of Netmate and NetAI was dropped.  
Tcpdump was used to capture and manipulate the traffic from the test network depicted in Figure 1. As the monitoring setup produced various packet capture files, we merged them in a bi-directional traffic file.  
Wireshark, tcpdump and the included tools were used for calculating the statistics depicted in Table 2, Table 1 and Figure 2 and deriving the protocol list in Table 3. Wireshark and Tcpdump were also both used to investigate trace contents manually. This revealed additional information to the automatic analysis e.g. transmission of control information in plain text format.  
Bro was used mainly to produce connection, domain name system and weird traffic logs in its network traffic analysis framework capability. Both the connection and domain name system logs proved very informative and would possibly be useful even without the added configuration of site-specific security policies written in the provided scripting language. The number of connections in Table 1 reflect Bro interpreting UDP and ICMP flows as pseudo-connections, based on its own internal logic [22].  
The weird traffic log produced by Bro informed that the communication was triggering weird behavior events and would benefit from a closer look. The triggered weird events were of the following types listed below. Their cause and effect warrant further investigation into the network and its functionality, but is not in the scope of this paper. The events were:  
non_IPv4_packet truncated_IP  
above_hole_data_without_any_acks connection_originator_SYN_ack  
possible_split_routing data_before_established  
DNS_label_len_gt_pkt DNS_truncated_ans_too_short
## 1 Introduction
### Related Work
#### 1.1.2 AR Content Evaluation  
Research on AR content not only involves development but also continues to explore the validation of AR content effectiveness. Studies on usability evaluation, such as user experience assessments, involve conducting surveys to evaluate how users interact with and perceive AR prototypes, providing recommendations for improvement based on user feedback [15]. [PERSON] et al. [16] evaluated the usability and learning potential of an AR system developed for grading Euclidean vectors in physics and mathematics through a system usability scale (SUS) assessment. [PERSON] et al. [17] evaluated the usability scale and NASA-Task Load Index of an AR-based instruction system developed for maintenance tasks, presenting the ARBI methodology with the shortest task completion time and the fewest errors. [PERSON] et al. [18] investigated the usability of HoloLens 2 using standardized tutorials on AR's multimodal interaction among 47 elementary school students (grades 2-6). While the overall system usability resulted in a \"good\" outcome, various behavioral metrics revealed differences in the efficiency of specific interaction modes. [PERSON] et al. [19] developed an MAR application, utilizing keypad- and marker-based interactions, to teach Kamaugh mapping in digital electronics courses. Evaluation with 90 engineering students using the SUS and HARUS revealed that the keypad-based MAR application yielded superior evaluation results, indicating better user interaction. [PERSON] et al. (2020) developed augmented-reality-based robot control content and conducted an objective usability evaluation using the Usability Metric for User Experience (UMUX) survey to assess remote presence. [PERSON] et al. (2021) developed an augmented-reality-based software architecture for military scenarios, which effectively responded to situational awareness, and obtained usability evaluation scores through UMUX. [PERSON] et al. (2022) created augmented reality content for engineering education via mobile applications to facilitate new learning experiences and conducted an objective usability evaluation using the Computer System Usability Questionnaire (CSUQ) survey. [PERSON] et al. (2023) developed augmented-reality-based industrial operation content for performance and precision machining industries and performed an objective usability evaluation of the developed content through the CSUQ usability assessment.