esa-sceva/satcom-chunk-collection huggingface dataset:
# EAAnet: Efficient Attention and Aggregation Network for Crowd Person Detection
## 2 Model and Method
### Model Description
#### 2.2.3 Loss Function  
The loss function of YOLOv5 mainly consists of three parts: the loss of classification (cls_loss), loss of bounding box (box_loss), and loss of confidence (obj_loss), which is expressed in Formula (4):  
\[\mathrm{L}=\mathrm{L_{cls}}+\mathrm{L_{box}}+\mathrm{L_{obj}} \tag{4}\]  
The gain of each part is set as 0.5, 0.05, and 1 according to the application scenario. And the commonly used expressions of bounding box loss function mainly include GIoU_Loss, DIoU_Loss, and CIoU_Loss. On the basis of IoU, GIoU_Loss introduces the smallest closed rectangle that can contain the GT (ground truth) bounding box and the predicted bounding box. But it still relies heavily on IoU. In the vertical direction, the error of the two bounding boxes is very large, and the loss is difficult to converge. Based on GIoU_Loss, DIoU_Loss introduced the distance between the center point of the GT bounding box and the predicted bounding box, but did not consider the difference in the aspect ratio between them. Therefore, this model adopts the CIoU_Loss loss function, which comprehensively considers multiple factors such as the overlapping area of the bounding box, the distance from the center point, and the aspect ratio. The bounding box loss function is defined as follows:  
\[\mathrm{L_{box}}=1-\mathrm{CIoU}=1-\mathrm{IoU}+\frac{\sigma^{2}(b,b^{st})}{c^ {2}}+\alpha v \tag{5}\]  
Figure 6: BiFPN structure.  
where \(\alpha\) represents a positive trade-off parameter, and \(\
u\) represents the measure of the similarity of the aspect ratio. \(\sigma\) represents the center point distance of the GT bounding box and the predicted bounding box, and c represents the diagonal length of the minimum rectangle between the detection and target boxes. \(\
u\) and \(\alpha\) are defined as in Equations (6) and (7).  
\[\
u=\frac{4}{\Pi}{\left(\arctan\frac{w^{g\dagger}}{h^{g\dagger}}-\arctan\frac {w}{h}\right)^{2}} \tag{6}\]  
\[\alpha=\frac{\
u}{\left(1-\text{IoU}\right)+\
u} \tag{7}\]  
where IoU is defined as follows:  
\[\text{IoU}=\frac{\text{S}_{1}}{S_{2}} \tag{8}\]  
Compared with the GIoU_Loss and DIoU_Loss functions, CIoU_Loss not only considers the overlap area between the predicted box and the real box, but also considers the center point distance and aspect ratio, which can satisfy the model's recognition and processing of occlusive and dense crowd targets.
## 2 Materials and Methods
### Regression Models  
The regression problem can be formulated as follows:  
\[y_{i}^{*}=f(X_{i},\ y_{i-1}) \tag{3}\]  
where \(y_{i}\) is the actual \(i\)-th value of power, \(y_{i}^{*}\) is the predicted value, \(X\) is features, and \(f\) is a regression model.  
Next, metrics were used to evaluate the forecasting results and compare different forecasting models:  
* R\({}^{2}\) (determination coefficient);
* Adjusted R\({}^{2}\);
* RMSE, kW.  
R\({}^{2}\) was calculated using the following equation:  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(y_{i}-y_{i}^{*}\right)^{2}} \tag{4}\]  
Figure 8: Correlation coefficients.  
\[R^{2}=1-\frac{\sum_{i=1}^{N}\bigl{(}y_{i}-y_{i}^{*}\bigr{)}^{2}}{\sum_{i=1}^{N} \bigl{(}y_{i}-\overline{y}\bigr{)}^{2}} \tag{5}\]  
\[Adj.R^{2}=1-\frac{\bigl{(}1-R^{2}\bigr{)}(N-1)}{N-p-1} \tag{6}\]  
where \(\overline{y}\) is the mean actual power value, \(N\) is the count of samples, and \(p\) is the count of features.  
The following models were used:  
* Linear regression (LR);
* Support vector machine (SVM);
* k-nearest neighbors (kNN);
* Decision tree (DT);
* Random forest (RF);
* Extremely randomized trees (ET);
* Adaptive boosting (AB);
* Gradient boosting (GB);
* Extreme gradient boosting (XGB);
* Categorical boosting, or CatBoost (CB).  
Various models were used, since the primary aim of the research was not to select the best model, but to analyze the possibility of increasing the accuracy of determining the output power of a wind turbine based on wind speed data and additional features.  
The large number of models based on ensembles of decision trees is because of their high efficiency in the analysis of time series in electric power engineering problems, which was confirmed by previous studies by the author of this paper [34; 35] and by other studies, such as [36; 37]. The implementations of the models was taken from the Scikit-learn library [38], except for extreme gradient boosting [39] and categorical boosting [40].
## 5 Discussions  
This paper shows that FPC antennas change their behaviour drastically from off-PCB conditions to on-PCB and are further impacted by the exact placing of the connecting transmission line. Since IoT devices are typically small and low-profile, the antenna in the majority of use cases should be embedded within the device. As mentioned, the size of FPC antennas for multiband operation from 698 MHz to 2690 MHz is such that it requires to be squeezed or folded into the casing of an IoT device resulting in unpredictable performance. Moreover, in smaller devices, the space available compared to the FPC antenna, which is 56 times larger in area (Table 7) than the antenna booster, simply does not fit without folding. Additionally, antenna boosters are off-the-shelf SMD components easily embedded in a PCB. Once its position on the PCB is optimised (usually, the corner of the device provides the best bandwidth and efficiency [14]), the performance remains predictable and stable when mass producing hundreds of thousands of devices. On the contrary, the FPC is more unpredictable since the position of the manually mounted FPC antenna impacts the performance, which may vary around 10 dB, as discussed in Table 6. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Total Efficiency (\%)** & **Top** & **Middle** & **Bottom** & **Maximum Variation (dB)** \\ \hline
698 MHz & 0.6 & 6.4 & 4.8 & 10.2 dB \\
960 MHz & 13.0 & 4.4 & 5.2 & 4.7 dB \\ Average & 3.4 & 6.2 & 9.1 & 4.2 dB \\
1710 MHz & 42.4 & 53.4 & 59.0 & 1.4 dB \\
2690 MHz & 30.6 & 55.9 & 64.4 & 3.2 dB \\ Average & 30.6 & 41.4 & 49.3 & 2.0 dB \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Measured total efficiency for the FPC antenna at 15 mm above the PCB and the three positions shown in Figure 4b. The maximum variation reflects the ratio between the maximum total efficiency value for a given position over the minimum total efficiency value. Figure 14: Measured total efficiency for the FPC antenna at 15 mm above the 131 mm \(\times\) 60 mm PCB for top, middle, and bottom cases as shown in Figure 4b. From a bill of material (BoM) perspective, the antenna booster technology needs an antenna booster and lumped SMD components for the multiband matching network. On the other hand, the FPC needs the FPC with an adhesive so the antenna can be attached to the casing of the device, a micro-coaxial line with a connector, and a receptacle on the FCB connecting the antenna. As a result, the price of an FPC antenna is approximately triple the price of an antenna booster, including the matching network components [24]. Finally, the performance is always better for the antenna booster despite being eight times smaller concerning its maximum length (or 56 times less area) for all PCB sizes and even when the FPC antenna is 20 mm over the ground plane of the PCB. The vast differences are significant in the low-frequency region (698-960 MHz).
# Efficient Next-Hop Selection in Multi-Hop Routing for IoT Enabled Wireless Sensor Networks
## 6 Conclusions  
In this work, we highlighted the energy constraints of sensing nodes involved in routing data to destinations in a hierarchy-based network. EMRP efficiently selects the next-hop by considering the residual energies of the intermediate nodes. We considered the energy-specific utilization of nodes to enhance the expiry time of first nodes as well. We also focused on connection degree and maximum links for data collection from member nodes. It accelerates data exchange to the BS by enhancing the lifetime of the network. To validate our work, we performed simulations in NS 2.35 along with TCL scripts and C code to implement send and receive functionalities in a heterogeneous environment where node configurations are separately specified in code. Furthermore, results were obtained from the trace files by utilizing AWK files. Simulation results showed the supremacy of our  
Figure 5: Packet delivery ratio is presented in (**a**) and amount of data lost in (**b**). The communication area and the number of alive nodes are illustrated in (**c**,**d**).  
proposed EMRP as compared to counterparts in terms of improved average lifetime, higher data collection rounds, improved PDR, reduced communication cost, increased residual energy, improved communication area and number of alive nodes. In future work, we will test our EMRP in grid topology along with available mobility models.  
This research received no external funding.  
Not applicable.  
The data used in the simulations have been reported in the manuscript.  
The author wishes to acknowledge the approval and the support of this research study from the Deanship of the Scientific Research in Northern Border University, Arar, Kingdom of Saudi Arabia.  
The author declares no conflict of interest.
## 4 Research Challenges and Directions
### Channel State Estimation in UAVSNs  
Owing to mobility and dense obstacles, both GU2U and U2U links are highly dynamic, and frequent link outages may occur. To adopt the mmWave and free space optic in UAVSNs, the power allocation, beamforming, beam training, adaptive beamwidth control, and controlling the phase shift from the reflected intelligent surface (RIS) require accurate channel state information by sending pilot signals [66; 67; 68]. Nevertheless, the frequent transmission of pilot signals triggers a waste of resources and delays in communication. Thus, channel state information in highly dynamic UAVSNs can be predicted by designing transformer-based encoder-decoder frameworks with less pilot transmission, which can be trained further by leveraging reinforcement learning [38; 69].  
Joint RIS-Assisted Adaptive Beamforming, Power, Caching, Computing, and Communication Resource Allocation in Dynamic UAVSNs  
Considering the next generation of wireless communication, driven by the demand for computationally intensive and latency-sensitive applications such as virtual reality, augmented reality, and metaverse platforms, high-frequency spectrums like mmWave and terahertz are promising solutions to meet data rate and QoS requirements. However, these high-frequency solutions come with challenges such as complex antenna geometry design [70; 71] and narrow beamforming in U2 GU, U2U, and U2H links, which are critical for ensuring reliable communication and maintaining network topology [72]. Narrow beamforming also results in reduced coverage and introduces more complex issues in high-mobility scenarios involving ground users or UAVs. RIS-assisted integrated sensing and adaptive beamwidth-based beamforming from UAVs can mitigate these challenges by reducing frequent link outages and extending coverage [73; 74; 75; 76]. Moreover, efficient resource allocation, including transmit power, bandwidth, computing, caching, and UAV trajectory optimization, significantly enhances overall system performance. Addressing the complexity of such optimizations, particularly in environments with multiple agents, dynamic state-action spaces, and nonlinear objectives and dynamic constraints, makes MA-DRL-based actor-critic frameworks highly suitable, which require further research attention.
# Business Impact Analysis of AMM Data: A Case Study
## 5 Results
### Risk Assessment by BIA Guidelines by Asset
#### 5.2.4. Customer System (CS)  
This system can also only be operated in the form of an application; the data are provided to the customer for informational purposes only. _Legal and contractual obligations_:If continuous metering is implemented on the MP, the distributor is obliged to transfer the metering data to the customer according to Decree 359 / 2020 Coll. However, there is no penalty for violations of this Decree. _Management and operation of the organization_: This system is not completely central to the organization and its failure, even for a month, may not be dramatic for the company and its operation. _Loss of credibility_: An outage of this system can affect several thousand people, or, in some cases, tens of thousands of people. It is quite likely to damage the company's reputation, but not for long or in any fatal way. Therefore, there is a medium risk. _Financial loss/disruption of activities_: From this point of view, it is not certain whether there would be any sanctions from any customer on CEZ Distribuce, a.s., due to the non-functionality of this system. However, any sanctions would probably be of a negligible amount. Therefore, the risk ranges from low to very low. _Provision of essential services_: In terms of service provisioning, tens of thousands of people may be affected by an outage of this service, but these are services that are not essential to the customer. Therefore, the risk is medium due to the number of customers affected by the outage. A comprehensive assessment of the risks and parameters of data availability and maturity is presented in Table 16.
# Laser Spot Centering Algorithm of Double-Area Shrinking Iteration Based on Baseline Method
## 2 Traditional Central Positioning Method
### Gaussian Fitting Method  
The energy of the laser spot imaged by the CCD is dispersed from the center of the spot to the surrounding area under ideal conditions, which is expressed as a two-dimensional Gaussian surface distribution of gray values. The Gaussian fitting method can be used to locate the center of the spot accurately for Gaussian-shaped spots. The formula is as follows:  
\[I(x,y)=A\exp\left[-\frac{(x-x_{0})^{2}}{\sigma_{x}^{2}}-\frac{(y-y_{0})^{2}}{ \sigma_{y}^{2}}\right], \tag{5}\]  
where \(I(x,y)\) is the intensity value when the coordinates of the image element are \((x,y)\), \(A\) is the amplitude of the spot, \(x_{0}\) and \(y_{0}\) are the center coordinates of the laser spot, and \(\sigma_{x}\) and \(\sigma_{y}\) are the standard deviations in the \(x\)- and \(y\)-directions, respectively.  
Taking the logarithm of both sides of Equation (5) and simplifying it to a polynomial, we obtain  
\[z=ax^{2}+by^{2}+cx+dy+f. \tag{6}\]  
The coefficients corresponding to the polynomial can be calculated using the least squares method, and then the coordinates of the center of the laser spot can be calculated as follows:  
\[x_{0}=-c/2a,\ y_{0}=-d/2b. \tag{7}\]  
Although the overall accuracy of this method is high, some uncertainty bias occurs after the background noise is removed due to the CCD having a certain degree of background noise and a complicated calculation.
## 5 Discussion
### Soil Salinity and Potential Groundwater Availability  
The strongly and very strongly saline soil classes of two measuring modes covered areas of two potential groundwater availability classes, TDS) \(>\) 1500 mg/L with the expected well yield of <2 cu. m/h and TDS \(>\) 1500 mg/L with 2-10 cu. m/h, about 82% and 90%, respectively. This finding is consistent with a previous report about soil salinity in the northeast region of Thailand. According to the Bureau of Groundwater Conservation and Restoration, the groundwater supply region in the Non Thai district ranges from brackish to salt water. It has a high chloride (Cl) chemical composition and high total soluble TDS [(70)].  
Likewise, [PERSON] [(83)] studied the reforestation of landscape salinity project conducted in the Nakhon Ratchasima province's potential salt source recharge area using an EM survey to identify and map the recharge and discharge areas and migration of subsurface brine. It was found that groundwater levels were low in the recharge area, ranging from 4.81 to 5.87 m below the soil surface, while in the discharge area, the groundwater levels were high, ranging from 0.48 to 1.04 m. The lower groundwater levels are attributed to the high consumptive water use of trees, which was supported by another finding using sap flux density and measurements of heat pulse velocity, finding that the annual water use of eucalyptus plantations amounted to 1230 and 270 mm/year in moderately and strongly saline soils, respectively.
## 2 Materials and Methods
### Satellite Data  
The launch of the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra and Aqua spacecrafts in December 1999 and May 2002, respectively, has greatly contributed to our understanding of Earth's dynamics. In this study, a group of MODIS Land products were used (Table 1) that were obtained from the National Aeronautics and Space Administration's Earth Observing System Data and Information System (EOSDIS; [[http://reverb.echo.nasa.gov/reverb/](http://reverb.echo.nasa.gov/reverb/)]([http://reverb.echo.nasa.gov/reverb/](http://reverb.echo.nasa.gov/reverb/))). Specifically, the NDVI was extracted from the MOD13A2 product, with a 16-day and 1-km resolution. The leaf area index (LAI) and fractional photosynthetically active radiation (FPAR) were extracted from the MOD15A2 product with an 8-day and 1-km resolution. The LAI was used to quantify the amount of foliage area per unit ground surface area [25], and FPAR was used to measure the vegetation-absorbed radiation in the 0.4-0.7 \(\upmu\)m spectral region of solar radiation [26].  
Note: MODIS, Moderate Resolution Imaging Spectoradiometer; NDVI, normalized difference vegetation index; LAI, leaf area index; FPAR, fractional photosynthetically active radiation; GPP, gross primary production; PsnNet, net photosynthesis; LST, land surface temperature.  
Gross primary production (GPP) [27] and net photosynthesis (PsnNet) [28] were derived from the MOD17A2 product with an 8-day and 1-km resolution, and were used in this study. GPP provides an accurate regular measure of the capacity of the vegetation to capture carbon and energy during photosynthesis, and PsnNet is used to measure the net uptake of carbon dioxide into a leaf.  
The 8-day land surface temperature (LST) data was derived from the MODIS MOD11A2 product. The time range of the MODIS products used was from 2005 to 2012, and all the datasets were aggregated into monthly values by taking the maximum of all values recorded within the month.
This article presents a dual-band nested circularly polarized antenna terminal for non-terrestrial automotive 5G applications operating in the n78 band (3.3... 3.8 GHz) and, at the same time, in the n257 band (26.5... 29.5 GHz) band. The two bands were selected based on the aforementioned KPI and cover both the UL and DL frequency bands according to 3 GPP [11]. The antenna combines a 2 \(\times\) 2 aperture-coupled patch array for the lower band and a 4 \(\times\) 4 single-layer patch array for the higher band. The focus of this paper is on the design of the former, while the latter was presented in [12]. The nested dual-band UE antenna array design makes it easy to manufacture, and its low profile lends well to mounting locations such as the rear spoiler of a car, roof-top shark-fin antenna cavities, or other similar mounting locations. The article is divided as follows. Section 2 outlines the link budget and the resulting antenna specifications. Section 3 describes the proposed nested antenna array design. The results for numerical simulations and measurements are summarized in Section 4, followed by conclusions in Section 5.
# Experimenting with Routing Protocols in the Data Center: An ns-3 Simulation Approach
## 3 FRR Port to ns-3 DCE
### Previous Work: Quagga Port  
Quagga is a routing software suite, providing implementations of OSPFv2, OSPFv3, RIP v1 and v2, RIPng and BGP-4 for Unix platforms. FRR is a fork of Quagga, which has been embraced by both industry and the community, replacing Quagga as the suite of choice for open source routing projects. FRR incorporates implementations of protocols used in data centers such as OpenFabric [26], and allows the necessary modifications to be incorporated into BGP for routing in large-scale data centers.  
Figure 3: Architecture of DCE. The application layer is where our programs will be executed using DCE to connect to the core of the network simulator (ns-3). [Prepared by the authors on the basis of an image obtained from [25]].  
Quagga has been ported to DCE in 2008 [27]. The Quagga module in DCE allows using Quagga routing protocols implementation as models in the network simulation. To date, the Quagga DCE project is no longer actively maintained, being its last update in 2012. Despite this, the project is still functional and can be executed with DCE without major problems. Quagga support in DCE is not complete.  
To make it easier to use Quagga in simulations, the project provides a QuaggaHelper class. This class provides methods that can be used from the simulation scripts to install a protocol on a node and configure it. During the port of FRR, we drew heavily on this class to develop a FrrHelper to provide similar facilities. Additionally, the fact that not all features work with the ns-3 stack (Basic Mode) motivated us to focus on the Linux stack (Advanced Mode) for the FRR port.
# Marine Bioactives and Their Application in the Food Industry: A Review
## 2 Marine Sources of Bioactive Molecules
### Marine Invertebrates
#### 2.3.4 Crustaceans
Several studies have isolated and characterized the different carotenoids and their prosthetic groups. Astaxanthin, astaxanthin esters, and zeaxanthin were identified from shrimp _Pandalus borealis_ and canthaxanthin, astacene, and lutein from processing discards of snow crab _Chinoecetes opilio_[3; 126]. Moreover, a high level of astaxanthin, which exists as a chemical complex with proteins (carotenoproteins) or lipoproteins (carotenolipoproteins), was identified in by-products of shrimp processing discards [114]. This astaxanthin is considered a super antioxidant and possesses various bioactivities, including antioxidative, anticancer, immunomodulating, antidiabetic, and anti-inflammatory effects. Moreover, offals from shrimp processing have been used as a source of carotenoid astaxanthin for aquaculture, particularly for salmon and trout. In addition, carotenoids play a vital role in various physiological processes, and as provitamins, they can also ensure the provision of the daily vitamin A requirements [127]. Chitin is another well-known bioactive material derived from the skeletal material of crustacean shells. As a linear amino polysaccharide, chitin is considered the second most abundant natural polymer in the world. From a chemical viewpoint, chitin consists of repeating \(\beta\)-1,4 linked \(N\)-acetylated glucosamine units, whereas its \(N\)-deacetylated derivative, chitosan, is composed of randomly distributed \(\beta\)-(1\(\rightarrow\)4)-linked-D-glucosamine and \(N\)-acetyl-D-glucosamine. Moreover, chitosan is prepared by alkaline or enzymatic deacetylation of chitin and is also widely distributed as a polymeric product in nature. Moreover, one of the limitations of chitin is its hydrophobicity, and as a water-insoluble derivative, chitosan expands its applications in various fields. These two biopolymers possess high biological and mechanical properties due to their unique structures. They have received significant interest as bio-functional, renewable, biocompatible, and biodegradable materials for a wide range of biomedical and biopharmaceutical applications (Figure 2) [116,128]. However, the exoskeletons of crabs and shrimps are widely used as chitin sources for commercial use due to the availability of wastes from the seafood processing industry. Moreover, producing chitin-like important biopolymers using these discards directly helps to manage the waste of the shellfish industry and also mitigate possible environmental hazards.