esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### The Building Served by the EAHX: Location, Climate and Relevant Features  
The studied large-diameter EAHX serves an office building located in Beja district, Alentejo, in the south of Portugal. Figure 1 provides the geographic location of the building.  
Despite the proximity of Alentejo to the Atlantic Ocean, the presence of mountain ranges of Cercal and Grandola block the Atlantic influence, justifying climate varieties Cas (Mediterranean climate with hot and dry summer) and BSk (cold steppe climate of mid latitude) [25]. With large seasonal and diurnal temperature variations, these climate varieties are particularly suited to room cooling with EAHXs. The large seasonal outdoor temperature variation, with winter minimum temperatures lower than 0 \({}^{\circ}\)C and summer maximum temperatures reaching 40 \({}^{\circ}\)C, justify undisturbed soil temperatures of approximately 17 \({}^{\circ}\)C (at 3 m or larger depths), allowing, in the hot summer period, for significant temperature gradients between outdoor air and soil temperature and, therefore, significant cooling of outdoor air. The large diurnal outdoor temperature variations with nighttime minimum temperatures below soil temperature warrant the uninterrupted cycle of daily heating and cooling of the soil surrounding the EAHX pipes, preventing (or reducing)  
Figure 1: Location and a panoramic photograph of the west and south facades of the building being served by the EAHX.  
soil thermal saturation, and in this way, warranting the daily regeneration of the EAHX cooling capacity.  
Figure 1 also includes a panoramic photograph of the building's west and south facades in the final construction stage. The building is one storey high and is developed along the west-east axis. It was designed with NZEB considerations in mind, which justified, besides the adoption of the EAHX for room cooling, partially buried exterior walls and photovoltaic panels on the south facade. These latter characteristics are visible in the panoramic photograph, namely, the buried walls below the windowsill and the photovoltaic panels extending from the lintel to ceiling height (the photograph depicts the initial stage of photovoltaic panels installation; once completed, photovoltaic panels covered the whole south facade). The EAHX, air handling unit (AHU) and distribution piping, being buried (in the south direction), are not visible.  
Table 1 highlights building characteristics relevant to the study of the built EAHX.  
Considering the low occupancy density, the envelope insulation, the large useful thermal mass and the larger indoor temperatures \"allowed\" by adaptive comfort principles--see item (m) of Table 2 in Section 2.2--20 W/m\({}^{2}\) (a specific load, defined per square meter of cooled floor area) was the _design_ (or peak) cooling demand determined for the cooled rooms. Despite this low value--for an office building and hot summer climate--, to the design of an EAHX for standalone office room cooling it represents a challenging requirement.
## 3 Overview of the Proposed Connection Methods and Specimen Design
This connection method has the advantage that the parts can be finished in the factory, except for the inter-story layer where the floor beam of the upper story module and ceiling beam of the lower story module meet; it also has the advantage of convenience with respect to securing workspace for bolting, as the beam flange is exposed to the outside. The method uses a cross-shaped connector plate combined with the three bolts on each of the left and right modules that penetrate the beam flange in the width direction, and the two bolts on each of the upper and lower modules that penetrate the beam web in the longitudinal direction, along with the straight connector plate. Although forming a flange connection in the exposed beam is relatively easy using the cross-shaped connector plate, bolt connection of the web is only possible when there is a shaft in the corner area of the module, or an opening on the lower story module ceiling or upper story module floor. Considering that the beam has an asymmetric cross-section, and that the connection is eccentric to the column, we fabricated specimens to be symmetric, and instability due to eccentric loading was minimized. As shown in Table 1, cross-shaped specimens were manufactured in the width direction and cantilever specimens were fabricated in the longitudinal direction. A total of four specimens were manufactured, as shown in Figure 2. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Specimen**} & \multicolumn{2}{c}{**Parameters**} \\ \cline{2-3}  & **Connector Shape** & **Loading Direction** \\ \hline K-LS & Straight & Long edge of plate \\ K-LC & Cross & Width (long edge of plate) \\ K-SS & Straight & Longitudinal (short edge of plate) \\ K-SC & Cross & Longitudinal (short edge of plate) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Descriptions of test specimens.
## 2 Distributed Fair Rate Allocation Algorithm
### Proposed Algorithm
#### 2.4.4 Dual Projection
In this case, we use the previous definitions of \(y^{k}_{j,ag}\) as the aggregated allocation demand (before the primal projection) at the j-th node and the k-th iteration and \(\hat{y}^{k}_{j,ag}\) as the aggregated allocation, _i.e._, after the primal projection. For example, in the small network in Figure 3, we have \(y^{k}_{1,ag}=y^{k}_{1}\) and \(y^{k}_{2,ag}=\sum_{j=2}^{4}y^{k}_{j}\). Then, we collect at the sink node the values of \(y^{k}_{j,ag}\) and \(\hat{y}^{k}_{j,ag}\) for all its child nodes in the vectors \(\mathbf{y}^{k}_{ag}\) and \(\hat{\mathbf{y}}^{k}_{ag}\), respectively. Using these vectors, we compute the relative distance between them as in Inequation (20). Note that using this approach, we do not introduce additional signalling as far as these quantities are necessary in the primal projection of the method. Furthermore, the decision to stop or continue with the iterations of the CDM can be piggybacked in the messages that send the values of \(\hat{y}^{k}_{ag}\) from the parent nodes to the child nodes and thus, the impact in the signalling requirements of the method is very small.
# Design of Edge-IoMT Network Architecture with Weight-Based Scheduling
## 2 Related Works
### Scheduling on IoMT  
This subsection briefly describes the latest scheduling techniques for IoMT applications. [PERSON] et al. [14] propose traffic priority-based delay-aware and energy-efficient path allocation routing protocol for wireless body area network, which selects the optimal paths with high residual energy of nodes with minimum temperature rise. An energy-efficient and delay-aware path allocation algorithm is developed for normal data, performing the selection of optimal and shortest paths. Then, a data on-demand algorithm is derived for on-demand data dissemination. Finally, criticalities detection algorithms are developed for  
Figure 1: The overall architecture for edge computing enabled IoMT systems (modified and reproduced from [5]).  
measuring criticalities of vital signs and allocation of adaptive and energy-efficient paths on the priority basis. [PERSON] et al. (2015) propose a priority-based MAC protocol for transmitting normal data, periodic data, and emergency data in wireless body area networks. This MAC design aims to guarantee energy consumption and delay reduction with traffic prioritization and classification for achieving the desired performance.  
To avoid data losses and reduce dissemination time with the priority-based data, [PERSON], et al. (2016) present the selective decision modes (i.e., congestion mitigation and perfect queuing modes) for achieving successful data delivery. Depending on the rate of congestion and priority of the data sensed, transmission is balanced using queuing or delivery as modeled in the decision mode. For real-time remote monitoring of the patient, [PERSON] et al. (2017) propose a new task scheduling and allocation technique for healthcare monitoring implemented in IoT cloud-based architecture. The proposed technique performs a prioritized sorted task-based allocation scheme for selecting the best virtual machine to execute the health task, solving the problem of delay, missed tasks, and failure rate, and the guarantee ratio.  
Besides the notable scheduling techniques, the genetic algorithm (GA), a metaheuristic approach that deals with the foundations of hereditary qualities and regular determination, is applied for scheduling in IoMT applications. [PERSON] (2018) puts forth the significance of scheduling management and the application of AI in the IoMT-cloud environment. [PERSON] et al. (2019) explore the energy utilization of the IoMT with the GA evolutionary processing based on the dynamic sensor range, introducing multiple sinks and direct information collection concepts, which provide a robust approach for massive data collection.
# A Vision-Based Method for Simultaneous Instance Segmentation and Localization of Indoor Objects
## 3 System Overview and Method  
Initially, we provide a comprehensive overview of the entire system, followed by an explanation of the system procedures encompassing camera calibration, target recognition and segmentation, and stereo matching, among others.
## 4 Design of Deep Reinforcement Learning Controller for Stabilized Platform
### Selecting State Vectors  
In the context of stabilized platform control, the reference tool face serves as the system input, denoted as \(\theta_{t}\). The difference between the reference tool face angle and the current tool face angle is \(e_{t}=\theta_{r}-\theta_{t}\), while the difference between the reference tool face angle and the tool face angle from the previous moment is \(e_{t-1}=\theta_{r-1}-\theta_{t-1}\).  
To achieve the desired tracking performance in the stabilized platform control problem, it is essential to adjust the current tool face angle in accordance with the reference tool face angle. As a result, when selecting state variables, the current tool face angle \(\theta_{t}\) and the current error \(e_{t}\) become pivotal considerations. Moreover, to ensure that the current state progresses in the direction of a minimized error, we include the previous moment's error \(e_{t-1}\) as a state value. In summary, the state vector at the current moment is constructed as follows:  
\[s_{t}=[e_{t},e_{t-1},\theta_{t}]^{T}. \tag{10}\]
## 4 Results and Discussions
### Generalizability Analysis  
The generalizability of the proposed model is validated using an independent experiment. For this purpose, some clips of male and female students in the offline classroom environment were recorded and tested using the proposed model. These students were not incorporated for training or even for the testing phase of model building. Figure 9 presents one frame from the testing video through which we may observe that all students  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Independent Variable** & **Dependent Variable** & **Const.** & **Coefficient** & **Std. Err** & **Z Value** & **Prob** & **Test** \\ \hline \multirow{2}{*}{Gender} & \multirow{2}{*}{Non-engaged} & 2.720974 & \(-\)0.0379002 & 0.0682888 & \(-\)0.55 & 0.579 & PR \\  & & 2.720974 & \(-\)0.0379002 & 0.0753602 & \(-\)0.50 & 0.615 & NBR \\ \hline \hline \end{tabular}
\end{table}
Table 11: Results of the proposed system model’s classification using 16 batches of data.  
Figure 9: Engaged and non-engaged distribution.  
are detected, and the proposed model labeled them according to their engagement and non-engagement states. The attained average accuracy for this independent experiment on unseen data is found to be 83%, which is close to 90% of model accuracy, so the absolute error is 7%, which can be ignored. Hence, the generalizability of the proposed model is validated. Our proposed model can be embedded with a hardware system to be deployed in a real-time classroom environment to detect the engagement and non-engagement state of the students.  
Figure 10: Confusion matrix represents results obtained from Engaged and non-engaged distribution with 0-index and 1-index, respectively, where figures show **(a)** Neural Networks, **(b)** CNN, **(c)** LSTM, **(d)** InceptionV3, **(e)** VGG16, and **(f)** proposed VGG16-Dense model.  
A comparison in Table 12 shows the key characteristics of various machine learning algorithms such as SVM (Support Vector Machines), Random Forest, Neural Networks, CNN (Convolutional Neural Networks), LSTM (Long Short-Term Memory), InceptionV3, and VGG16 for object recognition in video scene analysis. This table shows the advantages of the proposed VGG16 model with extended layers compared to state-of-the-art machine learning algorithms. A random video clip is also visually represented in Figure 11 to show the detect human faces with engage and non-engage states of students.
# Medium Access Control Protocols for Cognitive Radio Ad Hoc Networks: A Survey
## 3 Survey of State-Of-The-Art MAC Protocols
### Random Access Protocols  
Random access protocols arise from protocols such as Carrier Sense Multiple Access (CSMA) where the use of the communication channel is permitted to a random number of nodes without central coordination. This group of protocols is also known as Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA). The advantage of CSMA-based protocols is that resources are allocated on demand, making it easily adaptable to traffic fluctuations and protocol changes.  
There are several random access MAC protocols proposed for CRAHN where each uses different approaches to improve network performance. For example, the hardware-constrained MAC protocol [63] tries to simplify the hardware requirement by considering the hardware constraint. The Dynamic Open Spectrum Sharing (DOSS) protocol [64] tries to overcome the exposed node and  
Figure 3: Classification of the MAC protocols studied.  
hidden node problems. Dynamic channelization proposed in the Single Radio Adaptive Channel (SRAC) [65] combines frequencies based on the requirements of the CR user. Distributed Channel Assignment (DCA) uses spectrum pooling to increase spectral efficiency as an extension of IEEE 802.11 CSMA/CA. In the remainder of this section, we take a closer look at some of the proposed random access-based MAC protocols for CRAHN.
# A Secure Blockchain-Based Authentication and Key Agreement Scheme for 3 GPP 5G Networks
## 2 Related Work  
Recently, various solutions have been proposed to improve security in the Authentication and Key Agreement (AKA) process in the 5G network. In this section, we first discuss the security vulnerabilities in the existing 5G-AKA protocol. Then, we briefly review the major research work related to our work, including blockchain-related 5G authentication schemes and AKA schemes against DoS attacks.
## 3 Some Key Issues on the Use of SLNs and NLCs as Oral Nanovehicles for Nutraceuticals or Bioactives
### Liposoluble Nntraceuticalticals
#### 4.1.1 Carotenoids
This carotenoid shows a free radical scavenging activity 100 times more potent than vitamin E, and 10 times more than \(\beta\)-carotene. It also exhibits great potential for use in preventing and treating oxidative stress-related diseases [71], thus having a high commercial value. Like other carotenoids, astaxanthin has low solubility in water, easy degradation, and low bioavailability. Astaxanthin oleo-resin (40 wt%), or astaxanthin extract solubilized in sunflower oil, has been successfully incorporated in NLCs with oleic acid or sunflower oil as the liquid lipid using the hot melt-emulsification process [72; 73]. The encapsulation of astaxanthin (in oleoresin form or in sunflower oil) within the NLCs had similar EE and LC values of -90% and 1.2-1.8% (relative to total lipid content), respectively (Table 1). The nanoencapsulation in NLCs enhanced the antioxidant capacity of astaxanthin [73]. [PERSON] et al. [71] successfully fabricated SLNs using a double emulsion solvent displacement method, with a much higher LC of 6.11%. They demonstrated that the encapsulated astaxanthin could provide protection against oxidative stress in neurological disorders.
## Automated Negotiation for Resource Assignment in Wireless Surveillance Sensor Networks  
**[PERSON] 1, [PERSON] 1, [PERSON] 1,* and [PERSON] 2**  
\({}^{1}\) Departamento de Automatica, Universidad de Alcala, Edificio Politecnico,  
28805 Alcala de Henares (Madrid), Spain; E-Mails: [EMAIL_ADDRESS] (E.H.);  
[EMAIL_ADDRESS] (J.M.G.-G.)  
\({}^{2}\) Departamento de Fisica y Matematicas, Universidad de Alcala, Edificio Politecnico,  
28805 Alcala de Henares (Madrid), Spain; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +34-91-885-6828.  
Academic Editor: [PERSON]  
_Received: 23 September 2015 / Accepted: 18 November 2015 / Published: 24 November 2015_  
**Abstract:** Due to the low cost of CMOS IP-based cameras, wireless surveillance sensor networks have emerged as a new application of sensor networks able to monitor public or private areas or even country borders. Since these networks are bandwidth intensive and the radioelectric spectrum is limited, especially in unlicensed bands, it is mandatory to assign frequency channels in a smart manner. In this work, we propose the application of automated negotiation techniques for frequency assignment. Results show that these techniques are very suitable for the problem, being able to obtain the best solutions among the techniques with which we have compared them.  
**Keywords:** wireless sensor networks; surveillance; resource assignment; graphs; automated negotiation
## 7 Conclusions  
The suggested diplexer makes use of a complementary circuit that operates between 1.66 GHz and 2.52 GHz. The proposed design incorporates a meander line resonator and small square patches, coupled with SIR and UIR features, as well as microstrip feed lines. Modifying the meander line resonator by adjusting its length and number of turns has achieved the goal of obtaining a suitable electrical performance. A solver developed by Advanced Wave Research (AWR) was used to describe the reaction of the diplexer, which was constructed on a FR4 substrate. A diplexer that can operate for Tx and Rx channels under resonances of 1.66 and 2.52 GHz has been constructed using the suggested design. The first frequency band's transmission channel (Tx) has a fractional bandwidth (\(\Delta F\)) of 4.94% and extends from 1.626 GHz to 1.708 GHz. The reception channel (Rx) for the second frequency band spans the range of 2.469 GHz to 2.598 GHz, with a central frequency of 2.52 GHz and \(\Delta F\) of 5.12%. Furthermore, the insertion losses and return losses for this channel are detected to be 2.88 dB and 21 dB, respectively. Additionally, this diplexer can keep the two frequencies separate by 31 dB at the same time with viable phase and group delay values. The proposed diplexer can be easily incorporated into numerous wireless designs due to its small size (\(32\times 26\) mm\({}^{2}\)) and outstanding electrical performance. Manufacturing and measurement testing of the suggested diplexer were both fruitful outcomes of a simulation that made use of dual channels with highly narrow bandwidths.
# Automatic Medical Image Segmentation with Vision Transformer
## 3 Method
### Infection Segmentation Network
#### 3.2.4 Reverse Attention Module
This erasing strategy aligns with the intuition of selective attention, where the network iteratively attends to and refines the most infection-relevant features while filtering out distracting or less informative features. Through this progressive selective attention and erosion of non-essential features, the reverse attention module disentangles infections from complex surroundings and refines the segmentation map for improved accuracy and localization.
## 3 Results and Discussion  
### Spectral Characteristics  
Figure 1 shows the average of the raw spectra measured both on the lean muscle and on the fat for each piece of equipment. The spectral interval ranged from 740 nm (shortest wavelength acquired by SciO Sensor) to 2500 nm (longest wavelength acquired by NIRFlex N-500). Within the overlapping wavelength range (1000 to 1700 nm) where most devices collected spectra, a general similarity in the absorption line shapes can be observed (Figure 1). Higher absorbance values are seen in the regions associated with C-H groups from fatty acids (around 1200 nm, 1700-1750 nm, and 2300-2350 nm, as highlighted in yellow in Figure 1) and O-H groups linked to moisture (around 1000 nm, 1400-1450 nm, and 1950 nm, as highlighted in blue in Figure 1) [46; 47]. These features are particularly strong in the case of the NIRFlex N-500 benchtop spectrometer, followed by the Enterprise Sensor.  
Regarding the spectra of fat, higher spectral intensities are observed around 1200 nm, a wavelength related to absorption in the second overtone of the C-H stretching vibration, associated with intramuscular fat [48; 49], and at 1700-1750 nm, corresponding to the region of CH\({}_{2}\) stretching first overtones and binary combination bands, i.e., the signal highly indicative of fat and saturated fatty acids. Further characteristic features can be identified between the 2300 and 2350 nm region measured by the NIRFlex N-500. The bands corresponding to absorption of the C-H combinations associated with unsaturated fatty acid content are observed there [50]. Differences associated with moisture are clearly observable in the 1400-1450 nm and 1950-2000 nm bands with markedly higher intensity in the lean muscle spectra than in the fat spectra due to the third, second, and first overtones of the O-H stretching mode [51]. The absorption at around 1000 nm is also indicative of O-H bonds, and it can be concluded that the spectra of lean meat showed stronger absorption than the spectra of fat; this behavior manifests a relatively stronger magnitude for the SciO Sensor. Previous studies [52; 53] on Iberian ham have shown that different fatty acids exhibit similar absorption bands due to the high number of shared absorbing molecular groups (-CH2-), a similarity that is particularly reflected in regression models. Nevertheless, the absorption bands at 1696 and 1720 nm have been related to the particular case of linoleic acid [54].