esa-sceva/satcom-chunk-collection huggingface dataset:
# AdaptiveConv2d: A Novel Convolutional Module for Medical Image Segmentation
## 2 Materials and Methods
### Dynamic Integration of Global Contexts  
As shown in Figure 5, after the generation of the channel weight \(\alpha_{c}\), a dynamic fusion mechanism is introduced to adaptively refine each layer of the feature map. This mechanism is realized through the use of a dynamic residual connection, which combines the input feature map with the processed feature map from the main convolutional branch. The mathematical expression for this operation is given by  
\[Y_{c,j,j}=\alpha_{c}\times X_{c,j,j}+\beta_{c}\times\text{Conv}_{1\times 1}(X_{c,j,j}) \tag{7}\]  
In this equation, \(X_{c,j,j}\) denotes the pixel value at position \((i,j)\) within the feature map of channel \(c\). The operation \(\text{Conv}_{1\times 1}(X_{c,i,j})\) represents a \(1\times 1\) convolution applied to the feature map at this specific pixel location. Importantly, the \(1\times 1\) convolution is not a spatial operation, but rather a channel-wise transformation that mixes information across the spatial dimensions (\(H\times W\)) while maintaining the number of channels, \(C\). This allows the network to capture inter-channel dependencies and refine the spatial representation of each pixel.  
The learnable parameter \(\beta_{c}\) controls the degree to which the output from the \(1\times 1\) convolution contributes to the final feature map. This parameter is initialized to a small positive value, such as \(10^{-3}\), to prevent excessive reliance on the convoluted feature map at the start of training. A small initial value ensures that the model begins with a conservative balance between the raw input information and the processed feature map, allowing the network to adaptively adjust this balance as training progresses.  
The residual connection facilitates the direct passage of certain information from the input feature map to the output, bypassing the main convolutional path. This allows the network to retain vital original information that may otherwise be lost due to the depth of the network, thereby mitigating the issue of information degradation in deeper layers. This structure improves the adaptability and stability of the module by ensuring that key features in the input are preserved and effectively integrated into the final output.  
By dynamically integrating the global context with the local feature map through this residual mechanism, the model can learn to optimize the balance between fine-grained local features and global context across different layers. This dual-stream information fusion enhances the network's ability to capture both detailed local patterns and broader contextual dependencies, which is particularly beneficial in tasks such as image segmentation, where both local Precision and global coherence are essential for accurate results.
## 4 Results and Discussions
### Coverage Analysis and Path Loss Limits of LoRaWAN(r) Wireless IoT Communication
From the results, it is evident that the data do not fit FSPL. On the other hand, it can be seen that Cloud-RF(r)-based PL models tend to underestimate the PL predictions. Therefore, a set of common prediction error evaluation metrics were also considered to evaluate these modes further, as shown in Table 4.  
These metrics include mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean arctangent absolute percentile error (MAAPE), coefficient of correlation (R), and squared R (R2), also known as the coefficient of determination. Based on these evaluation metrics, it can be emphasized that all models show poor prediction performance, with ECC-33 showing the worst prediction performance. As such, the ITM was selected and was then fine-tuned to minimize the prediction error. In this regard, the RMSE was considered the objective function to be minimized by adding a correction factor (S) to the ITM PL response in dB. The fine-tuning step led to S = 14.664 dB,  
Figure 11: Heatmap of LoRaWAN® coverage at UKM campus based on Cloud-RF® PL simulations. (a) LoRaWAN® coverage based on the ITM PL model. (b) LoRaWAN® coverage based on the ECC-33 PL model. which significantly improved the ITM's prediction performance, as shown in Figure 12b,c and Table 4. For example, in terms of MSE, a 96.2% improvement was observed for the fine-tuned ITM. Similarly, the performance was also improved in terms of other metrics, except for R, showing a good correlation, as shown in Figure 12c. Hence, it can be concluded that the Cloud-RF(r)-based ITM (after fine-tuning) might represent the most suitable model for LoRaWAN(r)-based coverage prediction and planning in the considered study area. However, we suggest that further enhancements or new PL model proposals are required to provide highly accurate predictions. Based on the observations in this section, we utilized the fine-tuned ITM PL model for coverage planning in the considered rural farm. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **MSE** & **RMSE** & **MAE** & **MAAPE** & **R** & **R2** \\ \hline FSPL & 440.58 & 20.99 & 20.77 & 16.63 & 0.92 & \(-\)9.25 \\ \hline ITM & 223.23 & 14.94 & 14.65 & 11.71 & 0.92 & \(-\)4.19 \\ \hline Fine-Tuned ITM & 8.47 & 2.91 & 2.24 & 1.79 & 0.92 & 0.81 \\ \hline ECC-33 & 620.32 & 24.91 & 24.72 & 19.59 & 0.92 & \(-\)13.43 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Considered PL models’ accuracy evaluation metrics. Figure 12: (**a**) Measured PL vs. predicted PL from FSPL (baseline) and two Cloud-RF®-based PL models. (**b**) Measured PL vs. predicted PL from FSPL (baseline) and fine-tuned ITM PL model. (**c**) Actual vs. predicted PL correlation scatter plot for the fine-tuned Cloud-RF®-based ITM PL model.
## 3 Results  
### Reliability, Factorial Components and Global Descriptives of the OCI  
The reliability analysis provides internal consistency values that are high or very high in most scales and in the three OCI cultures. The \"Dependent\" (\(\alpha=0.64\)) and \"Oppositional\" (\(\alpha=0.48\)) styles provide the lowest [PERSON]'s \(\alpha\) results. A three-factor solution resulted from exploratory factor analysis of the OCI data. All three factors are comparable to those found in previous research [11; 64], except for the styles \"Avoidance\" and \"Perfectionistic\", with factorial loading values within the \"Aggressive/Defensive\" and \"Passive/Defensive\" cultures, respectively. To ensure a more accurate comparison between our results and those of previous studies, and considering the high internal consistency in the three culture types, analyses within our study maintain the definitions for the three styles proposed by OCI authors, complementing them with comparative analyses and profiles of each individual style. Table 4 shows results obtained from the reliability analysis, factorial analysis, as well as from descriptive OCI information. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Studied Variables** & \\ \hline  & **Definition** & **Variables** \\ \hline  & & Nuclear Power Plants/Nuclear Public \\ Sector & Type of nuclear sector organization & Companies (companies specializing in \\  & & radwaste and dismantling processes, fuel \\  & & manufacturing and nuclear component \\ Location & Personal workplace. & Facility/Headquarters \\ Contractual relationship & Contractual situation of workforce & Own personnel/Contractor \\ Mintzberg Component & Organizational component in which & Strategic Apex/Middle Line/Operating \\  & personnel are integrated according & Core/Technostructure/Support Staff \\  & to Mintzberg’s classification (1989) & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Variables used in the comparative analyses. Analyses show that the \"Constructive\" cluster is the one that best defines the organizational culture of the Spanish nuclear industry, with an average score of 3.47, followed by the \"Passive/Defensive\" cluster with a 2.92 average. The cluster with the lowest score is \"Aggressive/Defensive\", with an average score of 2.66 (Figure 2). In terms of styles (Figure 3), the highest survey score was reached in the \"Affiliative\" (3.62) style of the \"Constructive\" cluster, whereas the lowest score was in the \"Competitive\" (2.15) style of the \"Aggressive/Defensive\" cluster.
## 2 Preliminary
### Adaptive Traffic Signal Control  
ATSC is a widely studied concept in the past few years. It is usually accomplished by fuzzy logic, swarm intelligence, or neural network [41]. As the traffic lights are supposed to respond to the changing traffic flow, the prevailing DRL framework brings about better solutions to the problem. In this paper, we implement a DRL-based ATSC system to demonstrate the feasibility of our framework, but it should be noted that the methodology is true for most of the state-dependent ATSC approaches.  
The RL-based ATSC also follows the MDP model, and the corresponding definition is as follows:  
1. _State_: The first part of the state for a traffic light is represented by the queue length ratio in each lane. We assume that the length of homogeneous vehicles is denoted as \(l_{v}\), and the minimal gap between vehicles is \(\varpi\). Then the time-dependent queue length ratio of traffic light \(j\) can be defined as: \[\Psi_{j}(t)=min\bigg{(}1,\frac{n_{i}(t)(l_{v}+\varpi)}{L_{i}}\bigg{)},\ i\in l _{j}\,\] (3) where, \(l_{j}\) is the set of the lanes connected to the intersection area, while \(n_{i}\), \(L_{i}\) is the number of halting vehicles (i.e., the cars with a speed less than 0.1 m/s\({}^{2}\)) and the length of the lane, respectively.  
The second part of the state for a traffic light is the one-hot encoding of its current phase with a predefined phase scheme. Let the encode vector at time \(t\) be \(E_{j}(t)\), and the state of traffic light \(j\) is \(s_{j}(t)=\big{[}\Psi_{j}(t),E_{j}(t)\big{]}\).
2. _Action_: The action in this paper is defined as \(a=1\): switch to the next phase, and \(a=0\): maintain the current phase. The action is carried out with the interval of 25 s, while the specific definition of the signal phases is given in Figure 2. It should be noted that a 3 s yellow phase will be inserted if the phase changes.
3. _Reward_: The reward of the traffic signal agents is defined as the summation of the waiting time of vehicles on the lanes set \(l_{j}\). The definition aims to reduce the average waiting time of vehicles to improve traffic efficiency.  
We adopt the RAINBOW algorithm to control the traffic light, whereas the algorithm uses several tricks to improve the performance of the original DQN. The details of the algorithm can be found in [42]. In addition, a signalized corridor is taken as the research  
Figure 2: The phase diagram for the traffic signals in this paper.  
object, which means that the multi-agent system is built for the traffic lights. The training process is shown in Figure 3. The figure manifests the change in the total reward of the multi-traffic signal agents, and a distinct convergence trend is shown, which demonstrates the effectiveness of the training algorithm.  
More details of the implementation of the DRL-based ATSC can be found in Appendix A, as it is not the core part of the presented paper.
# Corrosion and Scaling in Geothermal Heat Exchangers
## 6 Degradation Mitigation for Heat Exchangers
### Anti-Scaling Agents and Corrosion Inhibitors
Additionally, the deposition of metallic lead and arsenic could result in heat exchanger failure due to galvanic corrosion [112]. In the project's second phase, the highest scale reduction was achieved using a combination of barium sulfate antiscalant and amine- and imidazoline-based corrosion inhibitors, resulting in a scale mass reduction of 60 to 70% (scale thickness \(<\) 1 mm after 4 months) [29]. Additionally, the use of corrosion inhibitors effectively prevented localized corrosion incidents [109]. However, amine-based inhibitors, such as Cetamine G815, exhibited reduced efficiency in the presence of Ca\({}^{+}\) ions, emphasizing the significance of inhibitor concentration, with maximum efficiency reported at 200 ppm [24]. Several factors hinder the widespread adoption of inhibitors in geothermal plants. Existing studies may lack completeness (e.g., medium temperature data or undisclosed inhibitor nature) or may be outdated, necessitating the exploration of improved products. Moreover, there is limited knowledge regarding behavior at high temperatures (\(<\)160 \({}^{\circ}\)C), and the potential impact of inhibitors on localized corrosion remains insufficiently investigated despite concerns that they may induce localized corrosion [113; 114].
# Micro-Satellite Systems Design, Integration, and Flight
## 3 ChipSat RTOS and RF Development
### Packet Formulation
#### 3.4.2 S/N Ratio  
One way to improve the signal-to-noise ratio (S/N) ratio when sending signals is to increase the signal's amplitude with a gain. This needs more power and makes the signal stronger so that the noise around it does not affect it as much. But, because the ChipSat mission has very little power available, other ways had to be used to make up for the S/N ratio. The two ways used were forward error correction (FEC) and matched filtering. These methods add extra bits that repeat the data. If a bit changes from one to zero or zero to one (a bitflip), the original data can be recovered from the repeated data that were sent.
## 2 Dynamics Model  
### Vehicle-Track Coupled Dynamics Model  
To simulate the dynamic response of the axlebox acceleration excited by the wheel flat, consider as an example a CRH380B high-speed vehicle, where a three-dimensional vehicle-track coupled dynamics model is established based on the multi-body dynamics [31], as shown in Figure 1. The vertical wheel-rail contact forces are calculated by Kik-Poitrowski model, creep, and tangential force in the contact area are calculated by [PERSON]'s linearized theory. The differential equation of the vehicle system vibration response is [31]:  
\[\mathbf{M}\ddot{x}+\mathbf{C}\dot{x}+\mathbf{K}x=\mathbf{F} \tag{1}\]  
where \(\mathbf{M}\), \(\mathbf{C}\), and \(\mathbf{K}\) are the mass, damping, and stiffness matrices, respectively; \(\boldsymbol{x}\) is the vehicle displacement; \(\mathbf{F}\) is the vector of the nonlinear wheel-rail contact forces, which is a function of the track irregularities and the vibration state of the track structure.  
The vehicle model consists of one car body, two bogies, eight axleboxes, and four wheelsets. In the model, the dampers, air spring, anti-rolling torsion bar, and traction rod are all expressed by elastic force element. Rails are considered as Timoshenko beams, and fasteners are simulated by special force. Table 1 lists partial parameters of the vehicle dynamic model.  
Figure 1: Vehicle-track coupled dynamics model: (**a**) lateral view; (**b**) front view.
# Capturing Features and Performing Human Detection from Human Gaits Using RFID
## Materials and Methods
### Related Work
#### 2.1.2 RFID-Based Sensing  
RFID has been widely used in various innovative fields. For example, [17; 18] used RFID to identify target materials, especially for liquids. In [19], RFID was used for vehicle positioning without Global Positioning System (GPS), and the Chinese Remainder Theorem (CRT) was used to solve the problem reasonably. At the same time, RFID can also complete more complex tasks, such as activity recognition [20], human body recognition [21], vital signs monitoring [22; 23], personnel positioning [24] and so on. [PERSON] et al. [25] performed respiratory monitoring with a commercial RFID device by detecting signal fluctuations at the receiver caused by chest displacement during breathing. They also used optimization technology to locate multiple RFID tags so that the system can monitor the respiration of two target persons. [PERSON] et al. [26] used RFID to achieve item level temperature sensing. By observing the current changes around the tag circuit, ordinary passive tags are used as thermometers. In addition, [27] realized multi-object tracking based on RFID with the aid of vision, combined computer vision and RFID technology with a pair of correctly deployed RFID antennas and cameras, and proposed a new recognition mode which can be used together with image-level trajectory information. [PERSON] et al. [28] used the vertical arrangement of multiple tags to identify different persons through RFID and make a detailed distinction between dynamic and static persons, while [PERSON] et al. [29] used tag array to capture the distinguishing features between individuals for device-free user identification. [PERSON] et al. [30] extracted the signal angle-of-arrival (AOA) information from the multi-antenna array to realize device-free activity identification. [PERSON] et al. [31] identified sitting posture by pasting labels on people's back This paper focuses on building a contact-free person detection system using RFID, which can capture the gait information of all people in the current range and estimate their height and the number of people. Different from the above works, this paper no longer uses complex tag array to obtain human features, but uses fewer tags to obtain human gait information in a specific combination way and takes step length as an important standard for people detection. In addition, the number of people in the area can only be obtained from gait features without visual assistance, which greatly reduces the cost. According to the experimental data, RF-Detection can well distinguish people with different step lengths and it has a good performance in the identification of continuous people.
## 2 A Review of g3 Gas  
g3 is a gas mixture that is composed of NOVEC\({}^{\text{TM}}\) 4710 commercialized by 3M company and CO\({}_{2}\). Physical and chemical properties of SF\({}_{6}\), NOVEC\({}^{\text{TM}}\) 4710, and CO\({}_{2}\) are shown in Table 1[19; 20; 21; 22]. NOVEC\({}^{\text{TM}}\) 4710 cannot be used alone owing to its high liquefaction temperature, and therefore, CO\({}_{2}\) that has a superior arc-quenching capability is selected to make the gas mixture [23]. g3 is a promising alternative that compromises between the dielectric performance and minimum operating temperature of the electrical facility. The ratio of NOVEC\({}^{\text{TM}}\) 4710 in g3 is typically from 4% to 10%.  
The dielectric performances of g3 were investigated by the AC breakdown test and lightning impulse breakdown test [6; 14; 19; 24]. The AC breakdown voltage was studied in a sphere-to-sphere electrode arrangement and in a 145 kV GIS at atmospheric pressure. It was verified that the dielectric strength of pure CO\({}_{2}\) was lower than 40% of that of SF\({}_{6}\), whereas adding a small amount of NOVECTM 4710 to CO\({}_{2}\) resulted in a significant increase in the breakdown voltage. The lightning impulse breakdown voltage was determined by the up and down method by applying a 1.2/50 us standard lightning impulse voltage. The dielectric performance of gas mixtures at pressures of 0.88 MPa and 1.04 MPa was equivalent to that of SF\({}_{6}\) at 0.55 MPa and 0.65 MPa, respectively.  
As to the environmental impact, the GWP of a gas mixture can be calculated according to the Intergovernmental Panel on Climate Change and the Regulation No 842/2006 of the European Parliament and of the Council of 17 May 2006 on Certain Fluorinated Greenhouse Gases [5; 25]. The 4% NOVECTM 4710/96% CO\({}_{2}\) has a GWP of 378, which is only 1.6% of that of SF\({}_{6}\)[14]. As shown in Table 1, the NOVECTM 4710/CO\({}_{2}\) gas mixture has an ozone depletion potential of zero. The atmospheric lifetime of NOVECTM 4710 is much shorter than that of SF\({}_{6}\), but much longer than some of the other alternatives, such as C\({}_{5}\)F\({}_{10}\)O or CF\({}_{3}\)I.  
In addition, g3 is classified as nontoxic, non-flammable, and non-corrosive, and is not a carcinogenic and mutagenic gas [6; 15]. The temperature test was conducted on the main components of GIS, such as busbar conductors, enclosures, and disconnectors, when a 3150A current flowed. It was indicated that a temperature rise difference of 5 K to 6 K was observed with respect to SF\({}_{6}\), which can be compensated for by adequate design improvement. The switching bus-transfer current capability test showed that the arcing time was stable over 100 close/open operations and the average arcing time in the gas mixture was about 12 ms compared with a typical value of 15 ms for SF\({}_{6}\)[23; 24]. Therefore, g3 is an effective technique that optimizes the insulation capability and advantageous features of SF\({}_{6}\).
## 4 Discussion
### Quality Assessment and Defect Analysis Comparison
Compared to conventionally cast or wrought HX (\(\sigma\)YS = 350-400 MPa; A \(\approx\) 35% [6; 7]), LPBF-processed HX exhibits approximately double the yield strength and superior ductility along the build direction. This strength enhancement primarily arises from Hall-Petch strengthening due to the refined cellular-dendritic structure and solution hardening by Mo and Cr enrichment at the cell boundaries. The absence of deleterious Laves phases and constitutional liquation--often encountered in LPBF Inconel 718--suggests that the HX composition and thermal history here are inherently more resistant to microsegregation-induced embrittlement ([PERSON] et al. [3]; [PERSON] et al. [21]). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Material** & **Reference** & **Relative Density (\%)** & **Primary Defect Type** & **Porosity Size (\(\upmu\)m)** & **Defect Density (Defects/mm\({}^{2}\))** & **Microhardness (HV)** \\ \hline Nickel Alloy HX & This study & 99.8 & Minimal spherical & \textless{}10 & \textless{}5 & 285 \(\pm\) 8 \\ Hastelloy X & [17] & 99.2 & Lack of fusion & 15–50 & 15–25 & 265 \(\pm\) 12 \\ Hastelloy X & [18] & 98.8 & Gas porosity & 5–25 & 20–35 & 248 \(\pm\) 15 \\ Inconel 718 & [19] & 99.4 & Spherical pores & 10–40 & 8–15 & 310 \(\pm\) 18 \\ Inconel 718 & [21] & 99.6 & Minimal defects & \textless{}15 & \textless{}8 & 295 \(\pm\) 22 \\ Inconel 625 & [16] & 99.3 & Gas porosity & 8–30 & 12–20 & 245 \(\pm\) 14 \\ CM247 LC & [22] & 98.5 & Microcracks & 20–80 & 50–120 & 420 \(\pm\) 35 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Quality metrics and defect analysis comparison.
## 4 Classification of Auto-Configuration Protocols
### Stateful Protocols  
#### 4.1.1 MANETConf  
MANETConf [10], which is an improvement of [11], is based on existence of a common distributed table so that all the nodes are able to assign IP addresses. When a node wants to join the network, it sends broadcast messages to other nodes and the first one which replies to the message, chooses it as an initiator node and it can supply an IP address. The initiator node chooses one of the free IP addresses located in the network and before assigning it, asks for permission from the rest of the nodes, because it is possible that another node may have also chosen this address or the tables might not be totally synchronized due to message delays. If the answer from the nodes is positive, it assigns the IP address to the joining node and it communicates this action by _broadcasting_ so that the rest of the nodes keep their tables updated. If there is a node which does not reply, it is put in direct contact with it (_unicast_) to get an answer. If it still cannot get it, it will assume that the node has left the network, and will communicate this to the rest of the nodes in the network in order to keep the tables updated.