esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Water Flow and Heat Transport Theory in Porous Media  
Water transport in porous media usually is represented using the two-dimensional version of the Richards' [28] equation:  
\[\frac{\partial\theta}{\partial t}=\frac{\partial}{\partial x}\bigg{[}K(\theta) \frac{\partial h}{\partial x}\bigg{]}+\frac{\partial}{\partial z}\bigg{[}K( \theta)\bigg{(}\frac{\partial h}{\partial z}\bigg{)}+1\bigg{]}, \tag{1}\]  
where \(\theta\) is the volumetric water content; \(K(\theta)\) is the hydraulic conductivity curve; \(h\) is the pressure head; \(x\) and \(z\) are the horizontal and vertical directions, respectively; and \(t\) is the time. The water retention curve, \(h(\theta)\), and the hydraulic conductivity curve, \(K(\theta)\), are two important properties that control water flow in a partially saturated media [15], and are needed to solve Equation (1). There are many expressions that can be used to represent the \(h(\theta)\)[29; 30; 31]. The most used equation to represent the \(h(\theta)\) is that proposed by [PERSON] [30]:  
\[S_{e}=\frac{\theta-\theta_{r}}{\theta_{s}-\theta_{r}}=\frac{1}{\left(1+\left| \alpha\;h(\theta)\right|^{m}\right)^{m}}, \tag{2}\]  
where \(S_{e}\) is the effective saturation; \(\theta_{r}\), and \(\theta_{s}\) are the residual and saturated volumetric water contents, respectively; \(\alpha\) is the inverse of the air-entry pressure; and \(n\) and \(m\) are empirical parameters. [PERSON] et al. [31] also proposed a water retention model that has been used to investigate the impact of compaction on both \(h(\theta)\) and \(K(\theta)\); therefore, it is a relevant model for this investigation. The \(h(\theta)\) model proposed by [PERSON] et al. [31] is given in Equation (3):  
\[S_{e}(h)=\left\{1-\exp\Bigl{[}-\xi\Bigl{(}\left|h_{-1}-\left|h_{L}\right|^{-1 }\right)^{\mu}\Bigr{]}\right\}\;\;0\leq h\leq h_{L}, \tag{3}\]  
where \(\xi\) and \(\mu\) are two fitting parameters, and \(h_{L}=15\) bar is the pressure head corresponding to a very low water content. It is also interesting to note that the parameters of the \(h(\theta)\) models of [PERSON] et al. [31] and [PERSON] [30] can be related using the following expressions:  
\[\mu =0.51\ln^{1.1}, \tag{4}\] \[\xi =1.21\alpha^{-1}. \tag{5}\]  
There are also many expressions to represent the \(K(\theta)\). The most used expression is the van Genuchten-Mualem model with \(m=1-1/n\)[32]:  
\[K(\theta)=K_{S}S_{e}^{l}\Bigl{[}1-\left(1-S_{e}^{1/m}\right)^{m}\Bigr{]}^{2}, \tag{6}\]  
where \(K_{S}\) is the saturated hydraulic conductivity, \(l\) is the pore-connectivity parameter (=0.5 [32]), and the \(S_{e}\) is determined using the van Genuchten [30] model. [PERSON] and [PERSON] [33], on the other hand, describes the \(K(\theta)\) using the following expression:  
\[K(\theta)=K_{S}\sqrt{S_{e}}\Biggl{[}\frac{\xi^{-1/\mu}\mu^{-1}\gamma\left(\mu ^{-1},\xi a_{h}\right)-h^{-1}\exp\left(-\xi a_{h}\right)+h_{L}^{-1}}{\xi^{-1/ \mu}\mu^{-1}\Gamma(\mu^{-1})+h_{L}^{-1}}\Biggr{]}^{2}, \tag{7}\]  
where \(\gamma\) and \(\Gamma\) are the incomplete and complete Gamma functions, respectively, and \(a_{h}=\left[h^{-1}-h_{L}^{-1}\right]^{\mu}\).
# I3 Mote: An Open Development Platform for the Intelligent Industrial Internet
## 2 Related Work
### Sensor-Oriented Platforms  
The emergence of microelectromechanical systems (MEMS) sensors in the last decade led to the appearance of their respective development kits. The kits soon incorporated wireless technologies, added different sensors and became advanced development platforms.  
One of the most representative platforms of this category is the Texas Instruments SensorTag. The SensorTag is based on the CC2650/CC1350 SoC, which embeds a Cortex-M3 and a 2.4 GHz (CC2650) or Sub-GHz (CC1350) radio transceiver. On the sensors side, the SensorTag include multiple sensors (Infra-red thermopile, 9-axis motion, humidity, pressure, digital micro-phone, magnetic fields and ambient light). On the communications side, the SensorTag supports IEEE 802.15.4 Low-Rate Wireless Personal Area Networks (LR-WPAN) and IEEE 802.15.1 Bluetooth Low Energy (BLE). On the software side a complete solution is provided by Texas Instruments, including RTOS, drivers and multiple application examples.  
Recently, Bosch has released the XDK Cross-Domain Development Kit, a platform following a very similar approach. The XDK is also built around a Cortex-M3 microcontroller. As in the SensorTag, the XDK includes multiple sensors (temperature, humidity and pressure, light, sound, acceleration, angular position and magnetic field). Regarding connectivity, the XDK supports IEEE 802.11 (Wi-Fi) and IEEE 802.15.1 (BLE). WiFi support is based on the CC3100 chipset and the TI SimpleLink stack. On the software side, the Bosch XDK platform is based on FreeRTOS.  
Both platforms have similar shortcomings. First, there is no interface to any industrial bus, so it is not easy to connect to existing equipment. Second, they focus on sensors on board, but have not been thought to read external sensors that have been already deployed in the industry on a massive scale.
# Fine-Grained Detection of Pavement Distress Based on Integrated Data Using Digital Twin
## 4 Experiment
### Evaluation Metrics  
The mean of the average precision (AP) of all classes of detected objects represents the detection performance of the deep-object detection network. The key to calculating MAP is to calculate Intersection over Union (IoU), which is defined as the degree of overlap between the predicted region and ground truth, and the mathematical expression is shown in Equation (3) and Figure 8.  
\[IoU=\frac{Predicted\ region\ \cap\ Ground\ truth}{Predicted\ region\ \cup\ Ground\ truth} \tag{3}\]  
The IoU threshold is usually predefined (set to 0.5 in this paper) and when the IoU between the predicted bounding box and the ground truth bounding box is greater than this threshold, the object within the predicted bounding box is defined as a positive sample. Otherwise it is a negative sample. In addition, the confidence threshold of the prediction-bounding box was also used to determine positive or negative prediction results. True positive (TP) is indicated when the predicted IoU is greater than 0.5 and the prediction result is correct. False positive (FP) is indicated when the predicted IoU is less than 0.5 or the prediction result is incorrect, and false negative (FN) is indicated when there is no IoU with an annotated bounding box, indicating that the model is unable to detect any object labels from the manual annotation.  
Precision and Recall can be further determined based on the above metrics. Precision is defined as the proportion of correctly detected objects to the total number of detections, and Recall is defined as the proportion of correctly detected objects to the total number of actual objects. The mathematical expressions are shown in Equations (4) and (5). The P-R curve can be obtained by calculating the accuracy and recall of the YOLOv5 network on the test set at different confidence thresholds, and the value of AP is obtained by integrating the P-R curve, which represents the area enclosed by the P-R curve and the coordinate axis. The MAP is obtained by calculating the average value of AP for each category of test object, and the mathematical expression is shown in Equation (6).  
\[Precision=\frac{TP}{TP+FP} \tag{4}\]  
\[Recall=\frac{TP}{TP+FN} \tag{5}\]  
\[MAP=\frac{AP}{n} \tag{6}\]  
where \(n\) is the category of the test object.  
Figure 8: Schematic diagram of the predicted and labeled regions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Network Structure** & **Initial Learning Rate** & **Exponential Decay Rate** & **Parameter Update Algorithm** & **Batch Size** & **Epoch** \\ \hline YOLOv5 & 0.00125 & 0.9 & Adam, \(\beta\) = 0.9 & 8 & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental hyperparameter setting.
# The Effectiveness of Unimodal and Multimodal Warnings on Drivers' Response Time: A Meta-Analysis
## 4 Discussion
### Limitations and Future Studies  
The current study has its limitations. Firstly, while this study included 60 articles with 308 studies that was sufficient for conducting a meta-analysis, the number was still too limited for conducting a moderator analysis. It is therefore highly recommended that future research conducts a moderator analysis when the number of accumulated publications in this area becomes abundant, which would enable us to further understand the effect of unimodal warnings. For instance, the location for presenting visual warnings, the volume-level of auditory warnings, and the vibration intensity of tactile warnings are some of the potential influencing factors of the effectiveness of driving warning systems. Secondly, the five basic senses include visual, auditory, tactile, gustatory, and olfactory. The current study fails to consider the latter two sensory modalities as they are not commonly utilized in the design of driving warning systems [73]. Lastly, due to the possible occurrence of sensory overload in the real world, future studies are also advised to explore whether the effects of each of the multimodal warning systems differs when being utilized in the real world in comparison to the simulated driving environment.  
Figure 10: Prediction of response time with multimodality warnings based on the Multiple Resources Theory and Attentional Co-Racing Theory.
## 6 Result and Analysis
### Mean-Time Resolution  
For the meantime-to-resolve, we used the MIR outage records because they contained the escalated incident records. To calculate meantime-to-resolve, two DateTime columns were used: Incident_Opened_at and MIR_Opened_at. We defined the meantime-to-resolve  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**GRU**} & \multicolumn{4}{c}{**Bi-LSTM**} & \multicolumn{4}{c}{**CNN**} \\ \hline \(\mathbf{k}\)**-fold** & **TP** & **TN** & **FN** & **FP** & **TP** & **TN** & **FN** & **FP** & **TP** & **TN** & **FN** & **FP** \\ \hline
1 & 1334  
as the time recorded, starting when the incidents had been reported and then resolved by the outage team. The meantime-to-resolve ranged from minutes to hours. The evaluation of meantime-to-resolve was conducted using the prediction horizon, as follows:  
\[Prediction\_Horizon=\sum T_{2n}-\sum T_{1n},\]  
where \(\sum T_{2n}\) denotes MIR opened at (\(T_{2},...T_{2n}\)) and \(\sum T_{1n}\) denotes incident opened at (IOA) incidents (\(T_{1},...T_{1n}\)).  
Our prediction horizon (meantime-to-resolve ) for MIR records from January 2020 to March 2021 is depicted in Figure 12. The blue line indicates the IOA records while the orange shows the MOA records. The IOA points (in orange) indicated the highest DateTime record where an incident log is generated. MOA denoted the point where we declared an incident as MIR. For example, ticket #20 had been reported to IOA on 30 April 2020 and labeled as MIR on 11 May 2020 by the outage team, so it took around 11 days to resolve this incident. Another ticket, #48, had been reported as IOA on 17 August 2020 and labeled as MIR on 27 August 2020, taking around ten days. In Figure 12, we noted that the orange line overlapped with the blue line, indicating the closing of the incident at the same instance. We predicted the average prediction horizon time of MIR as 4 days, 3 h, and 26 min, which could provide a significant advantage in addressing incidents and could prevent major incidents or outages.  
To further investigate our evaluation, we refined the prediction horizon by selecting only escalated incident records with time durations of 6 h or less. The reason was to scrutinize results in the context of hours (removing outliers that took more than days to resolve) to better quantify the improved meantime-to-resolve for most incidents, if predicted correctly. The leftover incidents numbered 44 (see Figure 13). The orange line shows the MOA values, indicating the incident escalated time, whereas the blue line shows the IOA records. Incident #1 took a maximum of 5 h and 35 min to escalate; in contrast, incident #7 took 5 min to mark as a significant incident. The analyzed results based on a refined search showed us the average time to resolve an incident within 1 day was 2 h and 30 min.  
Figure 12: Prediction horizon comparison (MIR opened at vs. incident opened at. The blue line indicates IOA records while the orange shows the MOA records).
## 4 Experimental Results and Analysis
### Discussion  
#### 4.5.1 Performance of Parameterized Linear Elements  
PReLU can effectively diagnose bearing faults by solving the equal displacement and negative interval death problems of neurons. To evaluate the influence of PReLU on the model performance in IGVMD-1 DCNN, an IGVMD-1 DCNN model with the same parameters was tested with regard to the size and number of convolution kernels, the learning rate, etc., except for the difference of a PReLU activation unit and ReLU activation unit. The same signals were input, and the validation accuracy and loss of the two models were recorded. Figures 15 and 16 show the comparison results of accuracy and loss changes, respectively, in the training process of the two algorithms. These figures show that the model using the PReLU activation function had higher accuracy, faster loss convergence, and a more stable training process. It was proven that PReLU makes the model have better learning ability, and it enhances the fitting of the 1 DCNN network for fault diagnosis without increasing the amount of computation.
## 3 Results
### Description of Interventions
#### 3.3.4 Photobiomodulation  
In this section, we distinguish five trials with low-level laser therapy (LLLT) and one trial with low-level laser with light-emitting diode (LED). Trials with LLLT employ a continuous-wave aluminum-gallium-arsenide (AlGaAs) semiconductor laser device, with a wavelength of 658 nm [29], 830 nm [19] and 940 nm [24; 31; 33] and energy of 2.29 J/cm\({}^{2}\)/point [29], 2.25 J/cm\({}^{2}\)/point [19], 2.5 J/cm\({}^{2}\)[24], 7.5 J/cm\({}^{2}\)/point [33] and 8 J/cm\({}^{2}\)/point [31]. The laser was applied at various points around the teeth that were intended to be moved for a period of 3 to 10 s for each point until the canine alignment or retraction phase was completed, depending on the trial in question. The trial with an LED laser used the Biolux OrthoPulse\({}^{\
Future Internet_**2013**, \(5\), 67-94; doi:10.3390/fi5010067  
_future internet_  
**ISSN 1999-5903**  
www.mdpi.com/journal/futureinternet  
_Article_  
**Global Locator, Local Locator, and Identifier Split (GLI-Split)**  
**[PERSON] \({}^{1,\ast}\), [PERSON] \({}^{2}\) and [PERSON] \({}^{2}\)**  
\({}^{1}\) Department of Computer Science, University of Tuebingen, Sand 13, 72076 Tuebingen, Germany  
\({}^{2}\) Chair of Communication Networks, Institute of Computer Science, University of Wuerzburg, Am Hubland, 97074 Wuerzburg, Germany; E-Mails: [EMAIL_ADDRESS] (M.H.); [EMAIL_ADDRESS] (D.K.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +49-7071-29-70505; Fax: +49-7071-29-5220.  
_Received: 15 January 2013 / Accepted: 4 March 2013 / Published: 11 March 2013_  
**Abstract:** The locator/identifier split is an approach for a new addressing and routing architecture to make routing in the core of the Internet more scalable. Based on this principle, we developed the GLI-Split framework, which separates the functionality of current IP addresses into a stable identifier and two independent locators, one for routing in the Internet core and one for edge networks. This makes routing in the Internet more stable and provides more flexibility for edge networks. GLI-Split can be incrementally deployed and it is backward-compatible with the IPv6 Internet. We describe its architecture, compare it to other approaches, present its benefits, and finally present a proof-of-concept implementation of GLI-Split.  
**Keywords:** locator/identifier split; scalable core routing; multihoming
## 4 Materials and Methods
### Classification Model
Many examples exist where no augmentation was used [17; 19] and many more where only a few augmentation techniques were applied [8; 16; 27]. However, in deep learning libraries such as PyTorch, multiple more functions exist that allow for augmentations in a single line of code [28]. For our training, we utilized random cropping, random resizing, horizontal flipping, and vertical flipping techniques. The algorithm begins by selecting a random area between 8 and 100% of the total image and crops it to keep only the selected area. This is a technique commonly applied in computer vision [20]. It then resizes the cropped area to \(224\times 224\) pixels and chooses randomly whether to flip the image along the horizontal axis. Therefore, if a model is trained over 100 epochs, each sample is likely augmented in 100 slightly different ways during training, and the model is taught to recognize the ground truth for that sample from 100 different vantage points. This provides a robustness to the model that is missing from other satellite image classification models in the literature. It is highly beneficial toward preventing overfitting of a model as augmentations make the model learn more generalized features of the images on which it is training. The last key element to highlight that is not in the literature for deep learning-based computer vision classification models for inference on satellite images is decaying learning rates [29]. Decaying learning rates is common practice in neural network algorithms, particularly in cases where stochastic gradient descent and standard loss optimization are utilized, yet this is not addressed in the literature. This is an important step in the backpropagation of many computer vision-based deep learning algorithms, especially for models trained over many epochs, as it helps adjust the step size accordingly during backpropagation while training a model. Without this technique, a model is likely to plateau at a local minimum instead of converging to a global minimum, hence producing suboptimal model performance.
## 3 Results and Discussion
Absorbers exhibit insensitivity to polarization and can effectively absorb across a wide range of angles. The study delved into how the absorber performs at various polarization orientations and incident angles of incoming THz waves. By adjusting the THz wave's polarization angle from 0\({}^{\circ}\) to 90\({}^{\circ}\), we obtained the absorption spectrum of the absorber corresponding to various polarization angles. The absorption spectrum of the absorber under varying polarization angles is shown in Figure 6a. This implies that the absorber is insensitive [31] to polarization angle, a trait mainly attributed to the symmetry of its structure. This characteristic allows the absorber to remain unaffected by the polarization phase of the incident light. A separate investigation of the absorption spectrum was conducted under different polarization modes, namely TE and TM polarization. The results are shown in Figure 6b,c. With an increase in the angle of incidence, the rate of absorption exhibits a decline. Nevertheless, across a broad range of incident angles up to 60\({}^{\circ}\), the absorber demonstrated consistent absorption performance for both TE and TM waves. Over a spectrum of incident angles below 60\({}^{\circ}\), the frequency range of 3-11.4 THz still exhibited a wide bandwidth for the TE polarization mode, in which the absorption exceeded 90%. In the TM polarization mode, the peak absorption of the broadband absorber could still be maintained above 90% when the incident angle was below 69\({}^{\circ}\). Compared to the work of others, the proposed THz absorber has certain advantages in terms of bandwidth, absorption rate, and angular stability, as shown in Table 1[28; 32; 33; 34]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Material** & **Layers** & **Abs BW (THz)** & **Angular Stability** \\ \hline
[32] & VO\({}_{2}\) & 3 & 3.3 (2.34–5.64) & 55 \\ \hline
[33] & Graphene and VO\({}_{2}\) & 3 & 1.03 (1–2.03) & 50 \\ \hline
[28] & VO\({}_{2}\) & 5 & 9.31 (7.36–16.67) & 40 \\ \hline
[34] & Graphene and VO\({}_{2}\) & 4 & 6.35 (2.30–8.65) & 50 \\ \hline This work & VO\({}_{2}\) & 5 & 8.6 (2.4–11) & 60 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The performance of the absorber compared with the performance of absorbers in the literature. Figure 5: (**a**) Adjusting the size of the parameter r\({}_{1}\) and (**b**) modifying the size of the parameter r\({}_{2}\).
## 3 Experiment Description
### Monitoring Scheme Design  
The WGC under construction underwent dynamic deformation because of solar radiation, construction vibration, wind load, temperature, and other factors. Traditional monitoring methods (e.g., GNSS, total station, and sensors) need to be in contact with the building, and the monitoring points are difficult to arrange when monitoring the dynamic characteristics of the building. The large tower crane on top of the building during construction caused serious effects (such as signal blocking and multipath effect) on GNSS measurement, making it difficult to effectively obtain the dynamic  
Figure 1: Wuhan Greenland Center (WGC): (**a**) architectural rendering; (**b**) architectural structure drawing.  
characteristics of the building using GNSS. Satellite-based SAR cannot obtain the high-accuracy horizontal displacement dynamic information of the building because of the influences of the revisit period, imaging angle, and monitoring distance. GB-RAR can set reasonable monitoring base points based on the monitoring requirements, adjust the most favorable radar imaging angle to achieve the noncontact high-accuracy continuous deformation monitoring of the building, and can simultaneously monitor multiple target points in the radar field of view. Therefore, the IBIS-S system was adopted to conduct high-accuracy continuous monitoring of the WGC under construction, and the dynamic characteristics of the building were extracted and analyzed based on the acquired radar signal data.  
In this study, two IBIS-S systems were simultaneously used to conduct high-accuracy continuous deformation monitoring of the WGC. Working base points S1 and S2 were established in the north-south and east-west directions of the building, and IBIS-S systems were installed in S1 and S2, as shown in Figure 2. The IBIS-S systems were used to conduct radar scanning of the WGC along the north-south and east-west directions for acquiring high spatial-temporal resolution radar images of the building and continuously obtain multiscene radar images. The displacement changes of the building along the monitoring direction can be obtained through interferometry, and the precise oscillation amplitude of the building can be calculated based on the GB-RAR-derived displacement. Radar monitoring adopted the high-frequency data acquisition mode (sampling frequency was 20 Hz). The horizontal displacement variation of the building and the displacement trajectory of the roof were computed through straightforward geometric projections based on the radar incidence angle, because the IBIS-S systems can only obtain the deformation of the radar LOS direction.