esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Initial Settings
As illustrated in Figure 7, when \(\text{coef}_{\text{HE}}\) increases, the temperature attenuates faster with distance and the temperature distribution becomes more concentrated in space. Conversely, lower \(\text{coef}_{\text{HE}}\) values yield flatter temperature profiles with broader thermal source coverage. Furthermore, \(\text{coef}_{\text{HE}}\) modulates the diffusion and superposition effects of each source. Higher \(\text{coef}_{\text{HE}}\) values results in more pronounced localized high-temperature zones, with strong heat source independence, weaker superposition effects, and high-temperature zones confined to the center of each source. Conversely, lower \(\text{coef}_{\text{HE}}\) values lead to a more uniform temperature distribution and more pronounced interactions between the sources. As shown in Table 1, there is a close linear relationship between the \(\text{C}_{\text{HE}}\) of the temperature phantoms and \(\text{coef}_{\text{HE}}\). The \(\text{C}_{\text{HE}}\) does not exceed 6.51 for \(\text{coef}_{\text{HE}}\) values of 0.1 and 0.2. However, when \(\text{coef}_{\text{HE}}\) is set at 0.5 and 1, the \(\text{C}_{\text{HE}}\) increases significantly to high levels of 15 and 30, respectively. When the reconstruction for three types of temperature phantoms is completed, qualitative and quantitative analyses are performed to evaluate the reconstruction performance. Multicriteria error analysis is used to evaluate the reconstruction accuracy. The normalized root mean square error (NRMSE) is utilized as the comprehensive description of the reconstruction fields, as described in Equation (13). \[\text{NRMSE}=\sqrt{\sum_{1}^{N}\left(T_{\text{r}}-T_{\text{a}}\right)^{2}}/ \sqrt{\sum_{1}^{N}T_{\text{a}}^{2}} \tag{13}\]  
where \(N\) is the number of grids. \(T_{\text{r}}\) and \(T_{\text{a}}\) are the reconstructed and actual grid average temperature, respectively. The NRMSE normalizes the error metric to the scale of the observed data, which allows direct comparison of model performance and immediate understanding of error magnitude relative to data scale. Therefore, the NRMSE is used as a key performance metric to evaluate the overall reconstruction accuracy of temperature fields. For hotspot tracking, the hotspot temperature error \(E_{\text{h}}\) and the hotspot position deviation \(D_{\text{h}}\) are given as:  
\[E_{\text{h}}=\left|\frac{T_{\text{Rh}}-T_{\text{Mb}}}{T_{\text{Mb}}}\right| \text{, }D_{\text{h}}=\sqrt{\frac{\left(x_{\text{Rh}}-x_{\text{Mb}}\right)^{2}+ \left(y_{\text{Rh}}-y_{\text{Mb}}\right)^{2}}{S_{\text{ROI}}}} \tag{14}\]  
where \(T_{\text{Mh}}\) and \(T_{\text{Rh}}\) are the temperature values of hotspots in the model phantom and the reconstructed temperature field, respectively. (\(x_{\text{Mh}}\), \(y_{\text{Mh}}\)) and (\(x_{\text{Rh}}\), \(y_{\text{Rh}}\)) are the 2D coordinates of hotspots in the model phantom and the reconstructed temperature field, respectively. \(S_{\text{ROI}}\) is the area of the region of interest.
## 3 Allan Variance
\begin{split}\sigma^{2}(\tau)&=\sigma_{Q}^{2}(\tau )+\sigma_{N}^{2}(\tau)+\sigma_{B}^{2}(\tau)+\sigma_{K}^{2}(\tau)+\sigma_{R}^{2} (\tau)\\ &=\frac{3Q^{2}}{\tau^{2}}+\frac{N^{2}}{\tau}+\frac{2B^{2}}{\pi} \text{ln}2+\frac{K^{2}\tau}{3}+\frac{R^{2}\tau^{2}}{2}\end{split} , \tag{8}\]  
where \(\sigma_{Q}^{2}(\tau),\sigma_{N}^{2}(\tau),\sigma_{B}^{2}(\tau),\sigma_{K}^{2} (\tau),\sigma_{R}^{2}(\tau)\) represent Allan variance due to the individual random processes. Then the noise coefficient of the five noise terms can be obtained by a data fitting algorithm. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Noise Terms** & \
\begin{tabular}{c} **Noise** \\ **Coefficient** \\ \end{tabular} & \(\mathbf{S_{\Omega}(v)}\) **PSD of the Random Process** & \(\mathbf{\sigma^{2}(\tau)}\) &
\begin{tabular}{c} **Slope of** \\ \(\text{log}\sigma(\tau)-\text{log}\tau\) \\ \end{tabular} \\ \hline \multirow{2}{*}{**the quantization noise**} & \multirow{2}{*}{\(Q\)} & \multirow{2}{*}{\(\mathsf{S_{\Omega}(f)=\begin{cases}\frac{4Q^{2}}{\tau}\text{sin}^{2}(\pi f \tau)&f\geq\frac{1}{2\tau}\\ (2\pi f)^{2}\tau Q^{2}&f<\frac{1}{2\tau}\end{cases}\)}} & \multirow{2}{*}{\(\frac{3Q^{2}}{\tau^{2}}\)} & \multirow{2}{*}{\(-1\)} \\ \hline
**angular random walk** & \multirow{2}{*}{\(N\)} & \multirow{2}{*}{\(\mathsf{S_{\Omega}(f)=N^{2}}\)} & \multirow{2}{*}{\(\frac{N^{2}}{\tau}\)} & \multirow{2}{*}{\(-1/2\)} \\ \hline
**bias instability** & & & \\ \hline
**rate random walk** & \multirow{2}{*}{\(K\)} & \multirow{2}{*}{\(\mathsf{S_{\Omega}(f)=(\frac{K^{2}}{2\pi})\frac{1}{f^{2}}}\)} & \multirow{2}{*}{\(\frac{K^{2}\tau}{3}\)} & \multirow{2}{*}{\(1/2\)} \\ \hline
**the rate slope** & \multirow{2}{*}{\(R\)} & \multirow{2}{*}{\(\mathsf{S_{\Omega}(f)=\frac{R^{2}}{(2\pi f)^{3}}}\)} & \multirow{2}{*}{\(\frac{R^{2}\tau^{2}}{2}\)} & \multirow{2}{*}{\(1\)} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Representation of noise terms using Allan variance. Figure 3: Sample plot of Allan variance analysis results. When the dimension of FOG output data is in degrees per hour (\({}^{\circ}\)/h), the different noise terms are as follows:  
\[ \begin{split}& N=\frac{\sqrt{C_{-1}}}{60}({}^{\circ}/h^{\frac{1}{2}}) \\ & K=60\sqrt{3C_{1}}({}^{\circ}/h^{\frac{3}{2}})\\ & B=\frac{\sqrt{C_{0}}}{0.664}({}^{\circ}/h)\\ & Q=\frac{10^{6}\pi\sqrt{C_{-2}}}{180\times 3600\times\sqrt{3}}( \arcsec)\\ & R=3600\sqrt{2C_{2}}({}^{\circ}/h^{2})\end{split} . \tag{9}\]
# An Attention-Based Full-Scale Fusion Network for Segmenting Roof Mask from Satellite Images
## 6 Limitations of the Model and Future Work  
The method proposed in this article has low detection accuracy for some roof edges, especially for roof edges with variable shapes in small areas. The model can be improved in the future to enhance its detection performance on roof edges and, in addition, to consider that there may be some pipelines and other facilities on the roof. Generally speaking, the area where photovoltaics can be installed on the roof is equal to the total roof area minus the area occupied by pipelines and other facilities. The current method is to directly multiply the empirical coefficient by the roof area to obtain the area where photovoltaics can be installed, which has a certain degree of error. Therefore, if the model can accurately identify the structures inside the roof after identifying the roof area, it can further accurately calculate the area where photovoltaic panels can be installed on the roof, which is also a point that can be further studied.
# Distributed Routing and Channel Selection for Multi-Channel Wireless Sensor Networks +
## 5 Towards a Completely Distributed and Dynamic Approach
### Key Characteristics of DRCS  
The proposed routing and channel selection scheme has several desirable characteristics that are summarized below:  
Adaptation to the battery stateThe battery state of a node is taken into account by the term \(B\). If the battery condition of any node deteriorates, the value of its health-metric will drop. This will result in a lower probability of selection of that node's channel by its neighboring nodes for DATA transmission, resulting in reducing its current consumption. Variability in the battery states of the sensor nodes is an inherent property of both battery-powered and rechargeable sensor networks. However, in rechargeable WSNs the battery states of the nodes vary relatively frequently over time. DRCS has the ability to dynamically adapt the routes and transmit channels to cope up with this energy variations.  
Load balancing between nodesIf a node's load increases, its \(\mathcal{I}\) will increase, causing its health-metric to decrease. This will cause that node's channel to be chosen with lower probability in the next RUI. Also after choosing the transmit channel, a parent is chosen based on the lowest ETX. Thus, if a parent is overloaded, its ETX will increase, resulting in other nodes to avoid selecting that node.  
Load balancing between channelsIf a channel is overused, the forwarding and overhearing traffic on that channel will increase. This will decrease the health-metric of the nodes in that channel. Thus, that channel is avoided in the next RUIs with higher probability.  
Route qualityThe ETX value quantifies the quality of a route. The route quality is important as bad routes result in higher retransmissions, which reduce the network lifetime.  
Channel qualityDRCS favors selection of channels with better quality, i.e., lower interference, as follows. A high level of channel interference will result in higher number of retransmissions and overhearing on that channel, causing the health-metrics of the nodes on that channel to reduce. Consequently, the corresponding channel will be chosen with lower probability in the next RUIs.  
The proposed scheme does not incur any additional control overhead other than periodic beacon updates. Also, _idle listening_ is avoided by using low-power listening. Problems such as routing loop detection and repairing are tackled similar to CTP. One possible drawback of the DRCS is energy wastage and delay associated with channel switching, which occurs when the receiver and transmit channels of a node are different. However, we show that data collection application with low data rates, this does not impact the performance. For high data rate applications, frequent channel switching may result in some data loss. However, in [45] the authors have shown that for CC2420 radios the channel switching time is \(\sim\)0.34 milliseconds, which results in _the additional energy consumption of less than \(\sim\)_2%. This makes the proposed dynamic channel selection scheme particularly appealing in practical settings.
## 3 Full-Scale Testing
### Fabrication and Precision  
Geometric model files created using the nominal dimensions shown in Table 7 are used as input for PrusaSlicer 2.4.0 [34] to prepare Goode directly for the printer. Referencing the global axes defined in Figure 2, the toolpath for the elliptical tubes consists of layers of material printed circumferentially in the \(xz\)-plane and then layered upwards along the longitudinal \(y\)-axis of the elliptical tube. Printing parameters and conditions are identical to those described in Section 2.  
The outer diameters \(2a\) and \(2b\), and the length \(L\) of all the specimens were found to be within 0.5% of the nominal values. Measurements of the wall thickness \(t\) were taken at four points around the cross-section at either end of the specimens; average values are shown in Table 7 along with the standard deviations from the nominal dimensions, and the resulting coefficient of variation of the tube wall thickness COV(\(t\)). As can be seen, COV(\(t\)) is between 1% and 6% for all specimens other than specimens EHS08 and EHS21 where the COV(\(t\)) values are 10.9% and 15.4%, respectively, indicating a lower level of precision during fabrication of these specimens.
# PLC-Controlled Intelligent Conveyor System with AI-Enhanced Vision for Efficient Waste Sorting
## 3 Deep Learning Model and Programming Framework
### System Deployment  
The physical system was developed to validate the functionality of the classification system, which consists of a conveyor belt powered by an Oriental Motor BMUD60-A2  
Figure 11: Ladder diagram of the subroutine handling conveyor timing and sorting actions specific to identified parts, including paper, cans, and plastic.  
Figure 12: (**a**) Main HMI interface displaying start/stop controls and material counters for paper, plastic, and cans. (**b**) Configuration panel for system settings, including start roll and actuator timing adjustments.  
brushless motor driver manufactured by Oriental Motor Co., Ltd., located in Tokyo, Japan, which facilitates the movement of recycling materials along the conveyor. At the end of the conveyor, a stepper motor is integrated with a flipper mechanism designed to sort bottles and cans. As illustrated in Figure 13, the system operates by advancing items along the conveyor until they are detected by the vision system using a camera. If the system identifies paper, the item proceeds to the end of the conveyor without intervention. In contrast, if a can is detected, the flipper mechanism directs it to the left bin, and if a plastic bottle is identified, it is sorted into the right bin. The flipper mechanism is controlled by a stepper motor managed by an Arduino Uno, which synchronizes the sorting process based on input signals received from the PLC.  
This setup provides a simplified yet effective platform for validating the performance of the vision-based classification system, ensuring reliable operation without introducing unnecessary complexity into the physical design. The system is currently configured to classify three types of waste, i.e., paper, cans, and bottles, while offering scalability to include additional recycling categories by incorporating more conveyors and actuators as needed.
## 5 Numerical Tests

Table 8: The absolute errors of the function values of \(y_{2}\) at the knots and the numerical convergence orders. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(x_{i}\) & \(E_{1}(x_{i},10)\) & \(E_{1}(x_{i},20)\) & \(E_{1}(x_{i},40)\) & \(O_{1}(x_{i},10,20)\) & \(O_{1}(x_{i},10,40)\) \\ \hline \(-0.5\) & \(6.342\times 10^{-7}\) & \(9.518\times 10^{-9}\) & \(1.470\times 10^{-10}\) & 6.0 & 6.0 \\ \(-0.4\) & \(5.069\times 10^{-8}\) & \(1.374\times 10^{-10}\) & \(7.450\times 10^{-14}\) & 8.5 & 9.6 \\ \(-0.3\) & \(9.230\times 10^{-9}\) & \(4.217\times 10^{-12}\) & \(2.941\times 10^{-13}\) & 11.0 & 7.4 \\ \(-0.2\) & \(2.200\times 10^{-9}\) & \(2.199\times 10^{-12}\) & \(7.871\times 10^{-14}\) & 9.9 & 7.3 \\ \(-0.1\) & \(3.042\times 10^{-10}\) & \(2.467\times 10^{-12}\) & \(1.760\times 10^{-13}\) & 6.9 & 5.3 \\
0.0 & \(3.641\times 10^{-10}\) & \(2.618\times 10^{-12}\) & \(1.416\times 10^{-13}\) & 7.1 & 5.6 \\
0.1 & \(3.119\times 10^{-10}\) & \(2.557\times 10^{-12}\) & \(5.085\times 10^{-14}\) & 6.9 & 6.2 \\
0.2 & \(2.242\times 10^{-9}\) & \(2.245\times 10^{-12}\) & \(6.439\times 10^{-15}\) & 9.9 & 9.2 \\
0.3 & \(9.406\times 10^{-9}\) & \(4.439\times 10^{-12}\) & \(1.124\times 10^{-13}\) & 11.0 & 8.1 \\
0.4 & \(5.172\times 10^{-8}\) & \(1.459\times 10^{-10}\) & \(4.774\times 10^{-14}\) & 8.4 & 10.0 \\
0.5 & \(6.482\times 10^{-7}\) & \(1.013\times 10^{-8}\) & \(1.584\times 10^{-10}\) & 6.0 & 5.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The absolute errors of the first order derivatives of \(y_{2}\) at the knots and the numerical convergence orders. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(x_{i}\) & \(E_{2}(x_{i},10)\) & \(E_{2}(x_{i},20)\) & \(E_{2}(x_{i},40)\) & \(O_{2}(x_{i},10,20)\) & \(O_{2}(x_{i},10,40)\) \\ \hline \(-0.5\) & \(1.909\times 10^{-5}\) & \(5.731\times 10^{-7}\) & \(1.765\times 10^{-8}\) & 5.0 & 5.0 \\ \(-0.4\) & \(1.899\times 10^{-7}\) & \(1.429\times 10^{-8}\) & \(4.735\times 10^{-10}\) & 3.7 & 4.3 \\ \(-0.3\) & \(3.931\times 10^{-7}\) & \(1.135\times 10^{-8}\) & \(3.656\times 10^{-10}\) & 5.1 & 5.0 \\ \(-0.2\) & \(2.668\times 10^{-7}\) & \(1.039\times 10^{-8}\) & \(2.035\times 10^{-10}\) & 4.6 & 5.1 \\ \(-0.1\) & \(2.939\times 10^{-7}\) & \(9.536\times 10^{-9}\) & \(1.882\times 10^{-10}\) & 4.9 & 5.3 \\ \(0.0\) & \(2.761\times 10^{-7}\) & \(8.669\times 10^{-9}\) & \(1.213\times 10^{-10}\) & 4.9 & 5.5 \\ \(0.1\) & \(2.583\times 10^{-7}\) & \(7.801\times 10^{-9}\) & \(5.797\times 10^{-11}\) & 5.0 & 6.0 \\ \(0.2\) & \(2.853\times 10^{-7}\) & \(6.926\times 10^{-9}\) & \(2.573\times 10^{-12}\) & 5.3 & 8.3 \\ \(0.3\) & \(1.572\times 10^{-7}\) & \(5.933\times 10^{-9}\) & \(3.983\times 10^{-11}\) & 4.7 & 5.9 \\ \(0.4\) & \(3.659\times 10^{-7}\) & \(2.799\times 10^{-9}\) & \(1.204\times 10^{-10}\) & 7.0 & 5.7 \\ \(0.5\) & \(2.010\times 10^{-5}\) & \(6.285\times 10^{-7}\) & \(1.954\times 10^{-8}\) & 4.9 & 5.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The absolute errors of the second order derivatives of \(y_{2}\) at the knots and the numerical convergence orders.
## 3 Results and Discussion
### Residtm_Saliva and Residtm_Reader Experiments  
Assuming 100% extraction efficiency, 50 \(\upmu\)L of saliva deposited on a sterile cotton swab and extracted in 1 mL of the RSIDtm extraction buffer will produce an extract that contains 50 nL of saliva per microliter of extract. When 0.4, 1, 2, and 10 \(\upmu\)L of saliva extracts are adjusted to a total volume of 200 \(\upmu\)L with RSIDtm running buffer, 100 \(\upmu\)L of these extracts will contain 10, 25, 50, and 250 nL of saliva, respectively. Two different Readers were evaluated in this study, each with two sets of RSIDtm cassette replicates. All results were concordant.  
Figure 3 shows results of RSIDtm Saliva as they appear in a digital photograph (Nikon's Coolpix 5600) and in the digital images generated by the RSIDtm Reader. Extraction negative controls (Figure 3, cassettes 1 and 2) and blood-semen-urine cross-reactivity controls (Figure 3, cassettes 11 and 12) were scored as \"negative\" by both RSIDtm Readers tested. Samples with calculated amounts (10, 25, 50, and 250 nL) of saliva were scored as \"positive\" by both RSIDtm Readers tested (Figure 3, cassettes 3-10).  
Cassettes 3 and 4, on which 10 nL of saliva were tested had very light positive signals, which was expected as 10 nL of saliva is at the detection limit of RSIDtm Saliva test [2]. To measure reproducibility of the RSIDtm Reader, cassette 3 was analyzed three times on both RSIDtm Readers. Both RSIDtm Readers detected signal and registered positive in all three replicates.  
Figure 2: Results of RSIDtm Blood and RSIDtm Reader Experiments. Results of two replicates for each analyzed blood volume from one Reader are shown. Cassette number and calculated volume of blood added to each cassette are shown (top). Digital camera capture of cassettes and RSIDtm Reader images are shown below, respectively. **Top—**Digital photograph of the cassettes (C—Control; T—Test lines) with blood volumes analyzed indicated above the images of the corresponding RSIDtm cassettes. **Bottom—**Images taken from RSIDtm Reader. RSIDtm Reader results (positive [\({}^{+}\)] or negative [\({}^{-}\)]) are indicated under each RSIDtm Reader image. Note: Camera capture or RSIDtm Reader images are slightly _less_ sensitive than visual observation of the cassettes.
# Energy Consumption Analysis of LPWAN Technologies and Lifetime Estimation for IoT Application
## 8 Discussion  
LPWAN technologies are limited by system regulation of the duty-cycle, which restricts the number of message transmission. The energy consumption analysis helps to calculate the energy requirement in different phases but its important to include other factors such as the battery, hardware, and firmware to calculate the end-device lifetime. Energy consumption depends on the type of hardware picked and the attached peripherals such as sensors. There are elements such as LEDs, resistors, and likewise part of the PCB design that consumes additional energy. For our experiments, we removed such connections to make it close to the ideal setup for better comparison. _LoRaWAN_ has class A type of device that is most power efficient as compared to other classes of devices. However, we observed that after each transmission there is a waiting period for two receive windows which consumes energy. This pure Aloha design scheme can be solved by using a better approach such as time synchronization or co-ordination scheme to make it efficient and qualify for multi-hop communication. _DASH7_ is different than LoRaWAN in the context of range and it does the polite channel access before it sends. The optimized utilization of PUSH and PULL mode helps to qualify for numerous applications while saving energy. On top of the existing solution design scheme such as waking up radio using a preamble based approach and using better channel coding scheme can help further in energy optimization. _Sigfox_ has an advantage over other only from coverage perspective, otherwise, it has the highest energy consumption. It sends two more replica of a message in addition to the original message to support mobility and packet loss. This increases the radio transmission time and leads to spending more energy. Acknowledgement based approach or scheduling among end-devices can help the Sigfox network to extend the battery life. _NB-IoT_ is the most robust technology among all due to its availability, coverage, and throughput. The end-device spends initial time in registration and communicating with network which is fixed from the service provider; optimization of this period can save energy. Also, the right selection of eDRX and PSM mode can help the application to have a prolonged lifetime. In our experiments, we observed that LoRaWAN and DASH7 have better energy performance as compared to NB-IoT and Sigfox. The most crucial part which impacts the overall energy consumption is the sleep or the idle time. As the end-device spends the maximum period in this state so it should consume the minimal energy consumption. Most of the research paper, claims the ideal energy consumption using the LPWAN commodity hardware but it turns upside down on the real-time deployment. As we saw, that in ideal case we got the LoRaWAN lifetime for several years but with the real time deployment it narrowed down to a few months. Thereby, its important to note this trade-off and estimate the lifetime of the LPWAN based network on the basis of environment, sensors, reading interval, and firmware.
# Usage of V2X Applications in Road Tunnels
## 2 Background
### V2X Requirements  
Determining the position of the vehicle, mobile phone, advanced units, and technologies is crucial, especially in traffic and smart mobility solutions. In the field of C-ITS and VANETs, where vehicles should communicate with each other, position accuracy is essential. It is necessary to know where the vehicle is located and to whom it will send information. In the basic principles that are stated, recommended, and standardized for the European region by ETSI (European Telecommunications Standards Institute), for vehicular communication, the accuracy of the position can vary, but in some new cases, such as Platooning (see Figure 2), the information about the time and position is required at intervals of at least 20 times per second [31]. This is practically impossible to achieve on the basis of the received GNSS signal in the tunnel, where no satellites are visible [32].  
The facts, prerequisites, experiences, and suggestions mentioned above led to the idea to test V2X communication in the tunnel and its surroundings. This was primarily to enhance knowledge about the usage of V2X applications with limited GNSS signal availability, such as tunnels, by practical experiment and measurement, to test further operating characteristics in the tunnel environment in parallel, but most of all, to obtain an answer on how the tunnel is prepared for the upcoming challenges of smart cities.