esa-sceva/satcom-chunk-collection huggingface dataset:
# Curiosity-Driven Camouflaged Object Segmentation
## 4 Experiments
### Discussion  
**Computational cost.** Since the model integrates two backbone networks, its complexity is slightly higher than that of some models. As can be seen from Table 4, the number of parameters of our method is not optimal, slightly inferior to ZoomNet, but it is also within an acceptable range. What is more worth mentioning is that our model performs better than other models in the segmentation effect evaluation index and multiplication and accumulation operations (MACs), which further verifies the excellence of the model proposed in this paper.  
Figure 7: Extension applications. The Visualization results in medicine (1 st and 2 nd rows) and industry (3 rd and 4 th rows).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{4}{c}{**Medicine(CVC-300 Dataset)**} & \multicolumn{4}{c}{**Industry(CDS2K Dataset)**} \\ \cline{2-9}  & \(S_{\mathbf{a}}\uparrow\) & \(E_{\boldsymbol{\phi}}^{ad}\uparrow\) & \(F_{\boldsymbol{\beta}}^{w}\uparrow\) & \(M\downarrow\) & \(S_{\mathbf{a}}\uparrow\) & \(E_{\boldsymbol{\phi}}^{ad}\uparrow\) & \(F_{\boldsymbol{\beta}}^{w}\uparrow\) & \(M\downarrow\) \\ \hline DCNet & 0.827 & 0.852 & 0.706 & 0.035 & 0.822 & 0.851 & 0.731 & 0.028 \\ FEDER & 0.835 & 0.863 & 0.719 & 0.034 & 0.828 & 0.863 & 0.740 & 0.028 \\ HitNet & 0.844 & 0.869 & 0.724 & 0.032 & 0.849 & 0.893 & 0.759 & 0.026 \\ FSPNet & 0.841 & 0.866 & 0.726 & 0.032 & 0.847 & 0.882 & 0.755 & 0.026 \\ \hline Ours & **0.846** & **0.873** & **0.739** & **0.034** & **0.851** & **0.895** & **0.763** & **0.025** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantitative comparison of our method with the state-of-the-art methods on the extension applications. \(\uparrow\)/\(\downarrow\) indicating that higher/lower scores are better.  
**The limitations of the benchmark datasets.** The three benchmark datasets, CAMO [17], COD10K [15], and NC4K [16], are of great significance in the field of camouflaged target segmentation, but they also have limitations in terms of data volume and diversity, annotation quality, application scenario restrictions, and data balance. For example, these datasets mainly focus on naturally camouflaged and artificially camouflaged objects, and may not fully cover all application scenarios of camouflaged object segmentation, such as medical image segmentation. Therefore, to promote the further development of camouflaged target-segmentation technology, it is necessary to continuously explore and build larger, more diverse, and higher-quality datasets.
# PROFEE: A Probabilistic-Feedback Based Speed Rate Adaption for IEEE 802.11 bc
## 7 Conclusions  
This work proposes a novel method to select the best possible MCS in a WiFi broadcast scenario where hundreds or thousands of STAs receive media content from a single AP. The proposed method is composed of two sequential algorithms. The first algorithm allows the AP to find the best probability for the STAs to send ACKs or NACKs to accurately estimate the number of STAs receiving broadcast content correctly/incorrectly. Then, a second algorithm selects the best possible MCS that maintains the number of STAs receiving broadcast content within the desired levels. We implemented both algorithms in ns-3 and found they choose the best possible MCS independently of the number of STAs and the scenario size in only a few seconds. The proposed method is a robust, lightweight, and scalable solution for the widespread adoption of WiFi broadcast, such as in the IEEE 802.11 bc initiative that was recently released.  
In future work, we plan to study mobility issues, ways to speed up the convergence time, and ways to allow for the protocol to operate in the presence of uplink unicast traffic and cases where other APs interfere in the vicinity. We also plan to study the behavior of the proposed method in heterogeneous scenarios. Finally, we plan to implement and test the proposed method in real conditions to learn more about potential issues missed in the simulation phase.  
Conceptualization, J.G. and J.J.C.-E.; methodology, J.G.; validation, D.R.-T., J.G. and J.J.C.-E.; formal analysis, J.J.C.-E. and L.O.-B.; investigation, L.O.-B.; resources, J.G.; software, D.R.-T.; supervision, J.G.; visualization, J.J.C.-E.; writing-original draft, J.G. and J.J.C.-E.; writing-review and editing, L.O.-B. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the Consejo Nacional de Humanidades Ciencias y Tecnologias with the Grant Program \"Becas Conacyt Nacionales\" and with the Program \"Paradigmas y Controversias de la Ciencia 2022\" under grant number 0320403. Also, this work was supported in part by the Spanish Ministry of Science, Education and Universities, the European Regional Development Fund, and the State Research Agency, Grant No. PID2021-123627 OB-C52. The APC was funded by the Consejo Nacional de Humanidades Ciencias y Tecnologias with the Program \"Paradigmas y Controversias de la Ciencia 2022\" under grant number 0320403.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflict of interest.
###### Abstract  
Dynamic and accurate identification of pilot intention is an important prerequisite for more accurate identification of control behavior, automatic flight early warning, and human-aircraft shared autonomy. Meanwhile, it is also the basic requirement of microscopic research on flight safety. In response to these demands, the airfield traffic pattern flight simulation experiment was carried out to obtain the pilot's physiological data, such as electrocardiogram, respiration, and skin electricity, under different intentions. The extended symbol aggregation approximation theory (ESAX) and the intelligent icon method were utilized to analyze and extract the characteristics of the pilot's intention. Furthermore, combined with the crow search algorithm (CSA) and extreme learning machine (ELM), a CSA-ELM pilot intention identification model was constructed and it is applied to climb, descend, level flight, and other situations in airfield traffic pattern missions to effectively identify whether the pilot has an intention. The rationality and validity of the identification model were verified through experiments with interactive computer simulations. In addition, compared with the traditional machine learning method, the accuracy of the identification method proposed in this paper is improved by about 10%. The above shows that the research results in this paper can provide support for improving the flight safety early-warning system and the pilot's micro-behavior evaluation system.  
flight safety; flight simulation; pilot's intention; feature extraction; identification Article
# Tunnel-Free Distributed Mobility Management (DMM) Support Protocol for Future Mobile Networks
## 3 Analytical Modeling and Performance Evaluation
### Modeling the Handover Latency
#### 3.2.4 Handover Latency of PMIPv6  
The MN on the move is managed in a localized manner in PMIPv6. The entities providing a mobility service supports the mobility for the MN. The mobile access gateway (MAG) senses and registers the movement of the MN when it gets attached to a new access network. For a handover in the domain of PMIPv6, the Duplicate Address Detection (DAD) process and address configuration is not compulsory since the same home network prefix in the RA message sent from the MAG at the new access network is obtained by the MN. The handover latency for PMIPv6 can be given as [29]:  
\[L_{HO}^{PMIPv6}=T_{L2}+T_{LMA}, \tag{5}\]  
where T\({}_{LMA}\) includes the time needed to receive the LMA's first data packet, time needed to send the RS message, and the time needed to exchange the update and acknowledgement messages between the LMA and MAG.
# Multi-Source Data Fusion-Based Grid-Level Load Forecasting
## 3 Results
### Experimental Setup
#### 3.1.3 Methodological Approach  
We implemented a rolling forecast methodology wherein models were retrained monthly and used to predict the subsequent month's load. This approach resulted in 11 distinct training-prediction cycles, with the final cycle using January through November data to forecast December. For each region, we conducted two parallel forecasting experiments:  
1. Provincial-level forecasting : Using historical load data without additional features but employing a peak-weighted loss function to improve accuracy during high-demand periods.
2. Grid-level integrated forecasting: Incorporating enhanced feature sets (including weather data, calendar effects, and regional economic indicators) alongside peak-weighted loss functions.  
This dual approach allowed us to evaluate the efficacy of both feature engineering and specialized loss functions in improving forecast accuracy.
## 4 Experiments and Results
### Implementation Results  
In this section, we present the results obtained after testing our prototype implementation, following the goals defined in the Section 4.1. A test benchmark has been established to assess the response times in the proposed scenario. This benchmark starts from an initial set up with the following characteristics:  
* All the involved devices are powered up and in a stable state.
* The IoT device MQTT client has a valid RPT (used for the action of subscribing and publishing).
* The IoT device MQTT client has performed a subscribing action to a specific test queue.
* The RS Broker has validated the subscription by verifying its permissions.  
The test consists in performing a complete cycle of publication and reception (with a previous subscription) of an MQTT message. The IoT device publishes a message to the test queue and waits for its reception. To perform measurements, two locations have been selected:  
* In the IoT device the measurement starts when it performs the publishing action, and stops when it confirms the message reception.
* In the RS Broker the measurement is performed inside of the PyAuth authorization module. It starts when the permission validation request is sent to the AS (OAuth Introspection flow) and stops once the response is received.  
To obtain a reference measurement for each scenario, the same cycle has been performed in each one, disabling the authorization module. This way, it is possible to determine the processing time for each MQTT message by the Mosquito Broker.  
The following equations express the time measurements obtained from the IoT device:  
\[T_{T\_NoAutth}=T_{P\_IoTD}+T_{Tx\_Net}+T_{P\_RSB} \tag{1}\]  
\[T_{T\_Autth}=T_{P\_IoTD}+T_{Tx\_Net}+T_{P\_RSB}+T_{P\_pyAutth}+2\times T_{P\_Intros} \tag{2}\]  
Equation (1) represents the complete test cycle time when the authorization module is not active. It consists of the sum of the IoT device processing time (\(T_{P\_IoTD}\)), the packet transmission through the specific network (\(T_{Tx\_Net}\)) and the Mosquito RS Broker processing time (\(T_{P\_RSB}\)). The same cycle time measurement, but when the authorization module is active, is represented by Equation (2), where the partial time of the PyAuth authorization module (\(T_{P\_pyAutth}\)) and the time needed to obtain the AS validation response (\(T_{P\_Intros}\)), which includes both the processing time of the validation in the AS and packet transmission time, are incorporated. There are two introspection requests in each cycle: one for the publishing action and another for the subscribing actions.  
Applying Equations (1) and (2), it is possible to obtain the additional time imposed by the authorization module (\(T_{P\_pyAutth}\)) as shown in Equations (3) and (4).  
\[T_{T\_Autth}=T_{T\_NoAutth}+T_{P\_PyAutth}+2\times T_{P\_Intros} \tag{3}\]  
\[T_{P\_PyAutth}=T_{T\_Autth}-(T_{T\_NoAutth}+2\times T_{P\_Intros}) \tag{4}\]  
This same cycle has been repeated 1000 times to obtain a representative mean value of each measurement. These results, accompanied with standard deviation values, are shown in Table 2.
# Graph Neural Networks for Routing Optimization: Challenges and Opportunities
## 3 Supervised Learning for Network Modeling  
### Overview  
In the traditional network modeling scheme, many assumptions are required to simplify the problem and achieve an efficient solution process using off-the-shelf mathematical tools under specific application scenarios. However, these ideal assumptions are rarely met in reality, making the follow-up optimized solutions invalid when deployed in real-world scenarios. As networks continue to grow in size and complexity, traditional network modeling approaches are becoming more cumbersome.  
ML-based network modeling overcomes this problem by collecting real-world network data and training/updating ML models without making complex and unrealistic assumptions. When a model is trained well, it can be deployed in production with fast reasoning ability, usually of polynomial time complexity. Supervised learning algorithms can be applied to routing problems to improve the dynamic response capability of routing by predicting essential network status information, for example, the traffic matrix and link load.  
In this section, we focus on the supervised learning approach for network modeling with GNNs, which is highly connected to routing optimization. The GNN model first predicts network performance under different routing configurations in a supervised learning approach. Then, the network administrator chooses the optimal routing configuration based on GNN predictions.
## 2 Materials and Methods
### Device Simulation in the Human Body Application  
A thermal resistance model based on human body application was constructed in order to calculate the power density of the device that consists of the material from the simulation. A thermal resistance model based on previous studies [18; 20] was used, as shown in Figure 1b. The Peltier effect and load matching were considered while Joule heat was negligible in the model.  
Under steady-state conditions, the maximum power density at the matched load derived from the model is given by the following equation.  
\[P_{\text{max}}=\frac{\left[N\alpha\Bigg{\{}\frac{R_{t,tgg}}{R_{t,air}+R_{t,tgg }+R_{t,body}+\left\{R_{t,tgg}\frac{N^{2}\alpha^{2}T}{T_{\text{EC}}+R_{L}}(R_{t, air}+R_{t,body})\right\}}\right\}(T_{body}-T_{air})\Bigg{]}^{2}}{R_{L}(1+R_{TEG}/R_{L})^{2}} \tag{2}\]  
Figure 2: Material data simulated by using first-principles calculations. (**a**) The Seebeck coefficient, the electrical conductivity; (**b**) the thermal conductivity, and the ZT according to the carrier concentration were obtained.  
where  
\[R_{L}=R_{TEG}+R_{t,leg}N\alpha T\Bigg{(}\frac{R_{t,air}+R_{t,body}}{R_{t,air}+R_{t, leg}+R_{t,body}}\Bigg{)}, \tag{3}\]  
Here, \(N\) is the number of TE leg couples and \(\alpha\) is the Seebeck coefficient of one couple. \(R_{TEG}\) and \(R_{L}\) are electrical resistances of the \(TEG\) and matched load, respectively.  
The power density according to the carrier concentration obtained from the material data and Equation (2) was compared with the ZT of the material. The N-type and P-type materials used in the device simulation are assumed to have identical properties to make device ZT the same as material ZT. The conditions related to the device structure are listed in Table 1.  
These conditions are based on a device that can be used in practical applications on the human body. The fill factor (FF) is lower than that of commercial devices because the optimum FF in the human body application is much lower than the FF of commercial devices [20]. Table 2 shows the conditions related to environments of human body application. The heat transfer coefficient of air corresponds to the natural convection condition in a typical indoor environment.
## 2 Materials and Methods
### Parameter Calibration Experimental Design
#### 2.5.5 Box-Behnken Design Experiment  
To identify the optimal simulation parameters, the response surface methodology was applied to identify the optimal simulation parameters, following the principles of the Box-Behnken design (BBD). The significant influencing factors identified through the two-level factorial test were selected as the influencing variables. Their value ranges were refined through the steepest ascent method. Subsequently, a five-level BBD test was conducted, where the response variable was defined as the relative error (\(E\)) between the measured maximum shear force and the simulation result. The formula used to calculate relative error is shown in Equation (7):  
\[E=\frac{|F_{1}-F_{2}|}{F_{1}}\times 100\% \tag{7}\]  
In Equation (7), \(E\) is the relative error; \(F_{1}\) is the measured maximum shear force (N); \(F_{2}\) is the simulated maximum shear force (N).
## 6 Results and Discussion
### Case 5. Field Atmospheric Data of Beijing  
Several factors are used to demonstrate the PV system's feasibility. Solar PV systems are commonly used in open regions where they are exposed to adverse weather conditions. Case 5 is all about a field study on a Chinese city, namely Beijing. Field atmospheric data are used to assess the proposed InC-DFO MPP searching method in real-world situations. Seasonal temperatures and irradiation levels are available from the Chinese meteorological agency (CMA) [46]. Total direct sunlight temperature and irradiance of two seasons are considered throughout the course of a day. The PV system consists of 4 \(\times\) 1 arrays, each rated at 315.072 watts, for an installed capacity of 1260.288 watts. The data provides information on weather factors such as temperature and irradiance of the PV system. Transients of The 24-h have a 10-min resolution. Maximum power obtained during a 24-h period is used to determine the efficiency of the MPP tracking InC-DFO algorithm.  
Table 4 shows the results of Case 5. Summer is probably the most productive season in Beijing, having peak production power of 897.2 W between 10 a.m. and 3 p.m. In the fall, the highest hour of average power is 811 W. Spring and winter are the least useful seasons in terms of power achievement. Beijing is located at 39.9042 North latitude and 116.4074 East latitude. According to the findings in all-weather situations, the InC-DFO algorithm gathers the most power. Figures 14 and 15 demonstrate a comprehensive examination of the InC-DFO algorithm collected power with, ACO, FA, DFO, InC, PSO, and CS in Beijing over the course of 24 h in the summer and spring. The results demonstrate that the proposed InC-DFO algorithm delivers better results and is more efficient.
# UAV-Based Privacy-Preserved Trustworthy Seamless Service Agility for NextG Cellular Networks
## 4 Privacy-Preserved Trustworthy Seamless Service Agility
### Signature Preparation  
Once the delegation process ends, the supporting UAV for the UE provider (home network) will prepare the proxy signature keys that will be used to produce the authentication signature as follows,  
* The UAV \(D_{i}\) creates the proxy signature keys, \((xp_{i},yp_{i})\), using the delegation tuple from the delegation phase as follows: \[xp_{i} =s_{i}+h(w_{i},r_{i})x_{i},\] (3) \[yp_{i} =(y_{c}y_{i})^{h(w_{i},r_{i})}r_{i},\] where \((y_{i},x_{i})\) are the public-private key pair for the UAV \(D_{i}\), respectively.
* The UAV then creates the following signature: \[\sigma_{i}=\mathbf{S}(m,xp_{i},T_{i}),\] (4) where \(m\) is a random plain text message and \(T_{i}\) is a timestamp. Note that since \(xp_{i}\) is only known by \(D_{i}\), the proxy signature can only be created by a legitimate \(D_{i}\).
# Novel Semantic-Based Probabilistic Context Aware Approach for Situations Enrichment and Adaptation
## 3 Situation-Based Contextual Model: Definitions and Formalizations
### Classification of Situation Rules  
The content of situation rules have different nature and can be defined for any smart domain, which we classify them as follows:  
* **Role-based rules:** This kind of rules depends on the user's role. As an example of role-based rules, Rule#1(_Work_) rule checks time axis is equal to the _planed time_ and user's job is an _employer_. This rule is described as follows: \[Rule\#1\;:Work\;;P_{1}[Time(PlnedTime(\text{work}));Role(is(employer))]\] \[EXCEPT[Situation(vacation)\;]\rightarrow\] \[Actions[deploy\;work\;space,\;put\;the\;phone\;silent]\]
* **Localization-based rules:** This kind of rules depends on the user's location. As an example of localization-based rule, Rule#2 (_Alarm home_) situation rule consists of two projections: \(P\)1 and \(P\)2. \(P\)1 checks the user's location is outside home and \(P\)2 checks time is between 8 pm and 5 am. It is, exceptionally, unchecked in Sunday. It is described as follows: \[Rule\#2\;:Alarm\;home\;;P_{1}[Location(Outside(Home,\;20))]\;OR\] \[P_{2}[Time(After(8 pm)Before(5 am);Location(Inside(Home,10)]\] \[EXCEPT[Time(Sunday)\;]\rightarrow\] \[Actions[deploy\;security\;alarm\;application]\]* **Rules-based on localization and role:** These kinds of rules depend on both location and role of a user. For example, Rule#3 (_Meeting_) situation rule checks the user's location is inside the meeting room and time axis is equal to the meeting time and his role _axis_ is employer. \[Rule\#3\ :Meeting\ ;\ P_{1}[Location(Inside(\"MeetingRoom\",\ 2))\] \[Time(PlnedTime(Meeting));Role(is(employer))]\rightarrow\] \[Actions[deploy\ meeting\ application]\]