esa-sceva/satcom-chunk-collection huggingface dataset:
# Implementation of an IoT Based Radar Sensor Network for Wastewater Management
## 3 Implementation and Results
### Sewer Pumpstation Universal Drawdown Device (SPUDD)
#### 3.1.2 Results  
Summarised below are the achieved results of all three test-cases.  
(a) _SPS--Site A_  
Site A comprises of one pump with flush valve (Pump 1) and one pump DOL (Pump 2). The flush valve fitted to Pump 1 is utilised to clean the base of the well prior to pump activation. When the Pump 1 Run contact is energised, for the initial 15 seconds the flush valve is activated prior to the pump running. This action stirs up the contents within the SPS well, allowing any settled solids to be flushed through the pump once running and not build up on the base of the SPS well.  
Figure 11 below shows the time-series radar plots of outflow measurements. Samples were derived from both the current manual drawdown method as well as the prototype drawdown device. Manual tests here were conducted over a 60 second period.  
These sample measurements were then normalised to a Gaussian distribution and plotted to clearly indicate variability between results. Figure 12 below shows these Gaussian functions. One can see that while both pumps manual drawdown tests are lower than that of the prototype radar device, the variability between Pump 1's samples is significantly less.  
This can be explained by the initial 15 second zero-outflow period whilst the flush valve is activated. This transient time significantly affects the manual drawdown test results as for one quarter of the flow measurement period, zero outflow is produced. Pump 2's results however, do show a closer recorded outflow to the radar device as the DOL pump is producing outflow for the full sample period, though variability is much higher due to human error of recorded values. One can see that the mean recorded outflow rate for both pumps 1 and 2 at Site A by means of radar drawdown test are approximately 24 L/s, which corroborate the designed duty point for the pumps installed at this site, while both manual results are significantly less than this duty point.  
Figure 11: Site A time series radar plots.
# Fake Base Station Detection and Link Routing Defense +
## 2 Related Work
### Security Research on Communication Links  
Link routing is a novel concept of switching to a legitimate base station to recover from a fake base station threat against availability, but it was inspired by the packet routing in wired networking and by base station handover. Among the previous security research studies in packet routing, particularly relevant are those focusing on the communication links as opposed to the entire forwarding path. These previous research works include link-targeted DoS for more advanced DoS (e.g., Refs. [26; 27]) and virtual link protection through moving-target-defense randomization (e.g., Refs. [28; 29]). There are also previous security research works on base station handover, including security and vulnerability analysis [7; 30; 31] and secure handover/mobility management [21; 32; 33]. Furthermore, related to our link routing technique in that they share the same security objective are the spread spectrum techniques in wireless communication link protection for availability (e.g., Refs. [34; 35; 36; 37]); however, these previous works implement the availability protection via wireless channel control against the jamming threat in general wireless contexts, while our work defends against a fake base station using coded and modulated communications, which follow the standardized protocol, unlike jamming. However, link routing is different from these technologies, as it is specific to the wireless communication link for cellular networking (e.g., 4G/5G) between the user equipment and the base station, which is both the first-hop and the last-hop link. To the best of our knowledge, our link routing is the first to apply routing control (selection and forwarding) of a base station. We better distinguish link routing from packet routing and base station handover in Section 5.2.1.
## Materials and Methods
### The Research Framework  
The three main steps of the method used in this research are illustrated in Figure 2. First, snow drought years and snow-abundant periods in the nearly last 20 years were detected and selected by using the precipitation and temperature data from the national meteorological stations. Snow-abundant years were also determined to compare the forest and grass growth conditions to the snow drought year. Second, the forest and grass phenological period was calculated, including the start of the season (SOS) and the end of the season (EOS), by using remote sensing data EVI (enhanced vegetation index) and the logistic model method. Landsat data were selected to obtain the land use and land cover data, and gross primary productivity (GPP) data production from moderate resolution imaging spectroradiometer (MODIS) present the forest and grass growth process and biomass in the growing season in one year. We used logistic modeling to calculate the SOS and EOS in forests and grassland. Under the intersect-grouping method, the selected years (snow drought and snow-abundant years) were divided into 16 phenological periods, which were placed with the rule of \"snow-drought year-snow-abundant year\" (e.g., 2019-2016) to be further analyzed. In addition, the relationship between phenological period and meteorological data were analyzed combined with the temperature and precipitation data in this paper (16 phenological periods: 2003-2005, 2003-2010, 2003-2015, 2003-2016, 2012-2005, 2012-2010, 2012-2015, 2012-2016, 2014-2005, 2014-2010, 2014-2015, 2014-2016, 2019-2005, 2019-2010, 2019-2015, and 2019-2016). Third, by using the above data, the influences on the forest and grass growth process and the phenological period from snow drought in the mid-high latitude area were quantitatively evaluated. Spatial statistical and overlay methods were also used to analyze the last two steps of the spatial variation in this area.
## 2 Seawater Channel Modelling Elements
### Apparent Optical Properties  
As mentioned, the illumination, such as a beam travelling in a specific direction inside and outside the medium, depends on the AOP. Generally, AOP depends on the medium and the geometrical arrangement of polarised radiance distribution, like diffusion and collimation [67]. The AOP is influenced by the composition of ocean water and the water body's illumination conditions at the time of acquisition. The three main AOPs of water are irradiance, reflectance, and radiation [29]. In addition, the water body's AOP includes its upward and downward irradiance. When examining the transmission of radiant energy into depths in ocean waters, AOP is significant [48]. The studies in [10; 68; 69] provide more information about AOP, including definitions and measurements.  
Every type of optical property contributes to the creation of UOWC systems in some way, with AOP being used to calculate ambient luminescence close to the water's surface and IOP being used to calculate communication link budgets [70].
## 5 Results
### Berger-Duffie (BDD) Results  
From Figure 3, BDD overestimated GHI in four stations, namely Marrakech (\(-\)8.2%), Tangier (\(-\)5.2%), Fes (\(-\)9.1%), and Agadir (\(-\)6.8%), whereas it underestimated GHI at Ouarzazate (14.9%) and Tantan (12.9%). From Figure 4, which gives the hourly rRMSE, rRMSE was less than 15% in Agadir (13.1%), Tangier (6.3%), and Marrakech (13.4%), which shows average agreement at these station between McClear and BDD. rRMSE was more than 15% at the rest of the stations, which indicates poor agreement between McClear and BDD. From Figure 5, which indicates rMAE, two stations, Agadir (10.5%) and Fes (11.2%), had rMAEs of less than 15% and thus showed good agreement, while poor agreement was shown at the rest of the stations with rMAE of greater than 15%, indicated by high bias.  
From Table 5, which indicates the coefficient of determination (R\({}^{2}\)), R\({}^{2}\) > 0.99 in all stations indicating excellent performance with the exception of Ouarzazate, showing good performance (<0.99), and the Tantan (0.97) station, where the coefficient indicates poor corelation between BDD and McClear clear sky model based on Table 4 model benchmarks. From Table 5, BDD was not feasible at any of the six stations based on Section 4.6 criteria when compared to other clear sky models. From Figure 6, BDD was not closer to McClear, which shows poor agreement that varies at different stations. The performance of BDD clear sky model with stations is also summarised in the supplementary material Tables S1 to S6 and Figure S3.  
whereas it was more than 15% at the Tantan (20.0%) and Ouarzazate (20.1%) stations, which indicates poor agreement in these stations between McClear and SOLCAST. From Figure 5, which indicates rMAE, all stations' rMAE values were less than 15%, which shows good agreement with the exception of the Tantan (19.8%) station, where rMAE was greater than 15%, thus indicating poor agreement as indicated by high bias.  
From Table 5, which indicates the coefficient of determination (R\({}^{2}\)) at all six stations, R\({}^{2}>0.99\) in all stations, indicating excellent performance based on Table 4 except the Tanat (0.97) station, where the coefficient indicates poor correlation between the INP and McClear clear sky models. From Table 6, INP was feasible at Tangier station, showing minimum rMBE at Tangier and maximum R\({}^{2}\) of (0.99) at Ouarzazate, Agadir, and Fes. From Figure 6, INP was closer to McClear, which shows good agreement that varies at different stations. The performance of INP clear sky model with stations is also summarised in the supplementary material Tables S1 to S6 and Figure S5.
## 2 Microchip Laser  
The microchip laser is a solid-state laser that is efficient and compact, which makes it very practical for space applications. The components of the laser have small dimensions, which results in a short optical cavity in the range of millimeters. They can be optically pumped using a laser diode, and there are already configurations for passively Q-switched microchip lasers [16; 17; 18; 19; 20; 21].  
In this work, a manufacturing assembly process was developed and tested to produce a microchip laser cavity with pulsed emission. An erbium glass as the active medium, a Co:Spinel glass as a Q-switch, and an external output coupler were used for the purpose. For safety reasons, the wavelength of the emission of the LiDAR should operate in the \"eye-safe\" region of the spectrum. The erbium ions emit in the 1550 nm wavelength region, which is considered \"eye-safe\".  
The microchip laser was implemented in two setups. In the first, the glass components of the laser were placed in special holders that allowed movement between them. Their alignment forms the microchip's optical cavity. In the second setup, the glass components were glued to a common substrate. The pumping of the microchip laser was performed by a laser diode coupled with an optical fiber. The light emitted was collimated and focused with a lens with a focal length of 35 mm. The pump laser had a typical wavelength of 975 nm, which corresponds to an absorption peak of the active medium material. Pumping was performed with quasi-continuous wave (QCW) emission to prevent damage to the active medium and its coatings. Its frequency was 10 Hz, with 10 ms of pulse width and 100 ms of pulse period.
# Mdpj _sciences_
## 4 Experiment Result and Analysis
### Ablation Study  
In order to conduct a thorough analysis of each module's effectiveness in our proposed model, we compared three ablation variants of the model, each with distinct settings. These variants are as follows:  
* **Mila-mileage:** This variant attempts to elucidate the role of mileage in the model by omitting the input of mileage from the model such that \(A_{t}=\text{Atten}(V_{t},R_{t},R_{t})\), \(J=D\).
* **Mila-atten:** This variant attempts to elucidate the role of the attention mechanism by omitting inputs to the attention mechanism from the learning module of key temporal information such that \(B=\frac{O_{1}+O_{2}+\dots+O_{T}}{T}\), \(D=\frac{E_{1}+E_{2}+\dots+E_{T}}{T}\).
* **Mila-gated:** This variant accounts for its role in the model by omitting the gated unit information fusion module, making \(I=[M_{1};M_{2};\dots;M_{T}]\).  
The outcomes of the ablation studies, presented in Table 4, reveal several critical conclusions. Initially, each model variant shows a reduced performance compared to the complete Mila model, indicating that mileage, key temporal information learning, and gated unit information fusion are all critical components. The Mila model outperforms the other three variants across all evaluation metrics, notably in \(w\)-\(F_{1}\) scores and \(R@k\) performance, signifying that each component is crucial for enhancing overall performance.  
Several key conclusions can be drawn from the results:  
* **Overall performance:** The complete Mila model demonstrates the highest performance across all evaluation metrics. This indicates that each component (mileage input, key temporal information learning, and gated unit information fusion) is crucial for improving overall performance.
* **Component importance:** The results for Mila-atten and Mila-gated are comparable; however, Mila-gated marginally outperforms the former in most metrics, suggesting  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model** & \(\mathbf{w}\)-\(\mathbf{F_{1}}\) & **R@5** & **R@10** & **R@15** & **R@20** & **R@25** & **R@30** & **R@35** \\ \hline Mila-mileage & 40.25  
that the attention mechanism might be slightly more crucial than the gated unit information fusion module. Moreover, the performance of Mila-mileage lags behind the other two ablation variants, indicating that mileage plays a significant role in forecasting vehicle maintenance demand.  
These findings demonstrate that each module in our proposed model contributes indispensably to the overall performance enhancement.
# Joint Resource Allocation and Learning Optimization for UAV-Assisted Federated Learning
## 3 Joint Optimization Algorithm
### Subproblem 3: User Computation and Communication Resources
#### 3.3.1 Subproblem 3-1: **f**, **t**, **p** Optimization Problem
Equation (28) differentiates the transmission time \(t_{n}\) to obtain:  
\[\frac{\partial E_{n}^{comm}}{\partial t_{n}}=\frac{N_{0}b_{n}}{g_{n}}\Big{(}2^ {\frac{s}{l_{n}b_{n}}}-1\Big{)}-\frac{N_{0}}{g_{n}}2^{\frac{s}{l_{n}b_{n}}} \ln(2)\bigg{(}\frac{s}{t_{n}}\bigg{)} \tag{30}\]however, we cannot find the relation between (30) and zero, so we can find the second derivative:  
\[\frac{\left(\partial E_{n}^{commn}\right)^{2}}{\partial t_{n}{}^{2}}=\frac{N_{0} }{g_{n}}2^{\frac{s}{n\delta_{n}}}\left(\ln(2)\right)^{2}\frac{s^{2}}{b_{n}t_{n }{}^{3}}\geq 0 \tag{31}\]  
So \(\partial E_{n}^{commn}/\partial t_{n}\) is an increasing function of \(t_{n}\), and when \(t_{n}\) tends to infinity, \(\partial E_{n}^{commn}/\partial t_{n}=0\), for all \(t_{n}\), \(\partial E_{n}^{commn}/\partial t_{n}\leq 0\), so the training energy consumption \(E_{n}^{commn}\) is a decreasing function of \(t_{n}\). Take the absolute value of \(\partial E_{n}^{compn}/\partial t_{n}\) and \(\partial E_{n}^{commn}/\partial t_{n}\), when one of them is bigger, such as \(\left|\partial E_{n}^{comp}/\partial t_{n}\right|>\left|\partial E_{n}^{commn} /\partial t_{n}\right|\), we can reduce the value of \(t_{n}\), in this case the training energy consumption becomes smaller and the communication energy consumption becomes larger. However, since \(\left|\partial E_{n}^{compn}/\partial t_{n}\right|>\left|\partial E_{n}^{commn} /\partial t_{n}\right|\), the reduction in the training energy consumption is more than the increase in the communication energy consumption. In general, the energy consumption of user \(n\) is reduced. Since \(E_{n}^{compn}\) and \(E_{n}^{commn}\) have nice monotone properties about \(t_{n}\), so we can apply the bisection method to find \(t_{n}^{*}\). The details are shown in Algorithm 3. \(\square\)  
```
Input: \(\mathsf{t}^{\min}\), \(\mathsf{t}^{\max}\), user set \(\mathcal{N}_{m}\), iteration \(r_{3}=0\), maximum iterations \(r_{3}^{\max}\), and set the accuracy \(e_{1}\) Output: \(\mathsf{f}^{*}\), \(\mathsf{t}^{*}\), \(\mathsf{p}^{*}\)
1:for\(n=0,\ldots,N_{m}\)do
2:while\(r_{3}\leq r_{3}^{\max}\)do
3: Set \(t_{n}=\left(t_{n}^{\max}+t_{n}^{\min}\right)/2\)
4:if\(\left|\left|\partial E_{n}^{compn}/\partial t_{n}\right|-\left|\partial E_{n}^ {comm}/\partial t_{n}\right|\right|\leq e_{1}\) or \(r_{3}>r_{3}^{\max}\)
5:\(t_{n}^{*}=t_{n}\)
6:return\(t_{n}^{*}\)
7:elseif\(\left|\partial E_{n}^{comp}/\partial t_{n}\right|>\left|\partial E_{n}^{commn} /\partial t_{n}\right|\)
8:\(t_{n}^{\max}=t_{n}\)
9: Set \(r_{3}=r_{3}+1\)
10:elseif\(\left|\partial E_{n}^{comp}/\partial t_{n}\right|<\left|\partial E_{n}^{comm} /\partial t_{n}\right|\)
11:\(t_{n}^{\min}=t_{n}\)
12: Set \(r_{3}=r_{3}+1\)
13:endifendif
14:endwhile
```  
**Algorithm 3** Solve The Subproblem 3-1: \(\mathbf{f}\), \(\mathbf{t}\), \(\mathbf{p}\)
## 3 Framework Description
### Process Controller Adapted to the Simulator
#### 3.2.1 State Manager  
The state manager is a centralized way to store the state of the entire simulation. The state of the simulation is defined by all the variables that are relevant during the industrial process. These variables are named the global conditions.  
Figure 2: UML class diagram of the process data model.  
A global condition is an independent requirement related to the achievement of a certain action. It is simply defined by a Boolean variable that expresses the state of an entity inside the simulation.  
Each global condition is related to one or more conditions (goals), each one belonging to a subtask. Each goal listens to its related global condition, waiting for a state change. If the value of the global condition is the same as the value required by the goal of the subtask, the goal is considered achieved. A subtask is complete once all its goals are achieved.  
Figure 3 presents a schematic example of the operation of the state manager. In this case, an action performed in the simulation environment triggers an event. A global condition, \(a\), which is listening to this event switches its value (from false to true), changing the state of the simulation and triggering another event that checks that its associated condition \(a\) on the current subtask, \(\mathit{Subtask}_{0}\), has been satisfied. In this case, when both conditions \(a\) and \(c\) on \(\mathit{Subtask}_{0}\) are satisfied, the subtask is considered complete and it finishes, allowing \(\mathit{Subtask}_{1}\) to begin.  
The first step to adapt the process controller to a specific simulator is to define the set of global conditions and link them to the events of the entities inside the simulator. Thus, when an action changes the status of an entity, an event is triggered, changing the global condition related to it. These global conditions are defined in the process definition (see Section 3.1).
# Application of Cinnamon Essential Oil in Active Food Packaging: A Review
## 6 Cinnamon Essential Oil in Food Industry  
EOs have been used in the food industry as flavor and aroma ingredients, but due to their properties, they are increasingly being used for preservative purposes [38]. The possibility of developing active packaging systems containing natural bioactive compounds such as EOs is a promising alternative, as it may reduce the food safety risks associated with chemically synthesized additives [19].  
As previously discussed, EOs are widely used in the food industry due to their antimicrobial, antioxidant, or natural preservative effects, which help extend the shelf life of foods. Fruits and vegetables are the most common types of foods where EOs are applied, but they have also been used in fish products, meat, milk and dairy products, bread, and baked goods [3].  
Currently, the application of EOs in active food packaging is strongly linked to its incorporation into biodegradable films and edible coating [68]. Both models also make it possible to replace commonly used petrochemical-based polymers (such as polyethylene, polyvinyl chloride, polypropylene, and polystyrene) that cause serious environmental problems with biodegradable materials [69].  
Films are thin sheets that are pre-made and then can be applied on the product as a wrapper or between product layers. On the other hand, edible coatings are films formed on the product, whose base is directly applied on the product surface, where drying occurs and, thus, the formation of the coating [70]. Figure 2 presents a graphical elaboration of films and edible coatings.  
It should be noted that the most significant limitations of using EOs in the production of biodegradable films and edible coatings are their high volatility and thermal degradation [71]. Therefore, there are more methods developed than those explained here, such as cast film and blow molding extrusion, to overcome these barriers [72], which are also utilized in industry or at an industrial level.  
The main biopolymers used to manufacture both films and coatings are proteins (gelatin, wheat protein, corn protein, casein, etc.), lipids (waves), and polysaccharides (cellulose, starch, chitosan, etc.) [68]. These polymers can incorporate natural and synthetic substances responsible for creating their active function, such as nisin, BHT, BHA, sodium lactate, potassium sorbate, plant extracts, and EOs [1].  
Figure 2: Film and edible coating production. Schematic representation of the production of films and edible coatings.  
Initially, EOs were added directly to the food matrix. The issue with this method is that they begin to degrade rapidly due to the interaction between their unstable and volatile composition and external factors such as light, oxidation, and heating. Therefore, new methods have recently been developed to improve the stability of EOs by encapsulating them in liposomes, polymeric particles, and solid lipid nanoparticles, thus enhancing their stability and efficacy [73].
# A New Dynamic Game-Based Pricing Model for Cloud Environment
## 3 System Model  
The system model adopted in this study is introduced in this section. This study aims to propose a game-based pricing model; by using it, a cloud provider can set a fair price for their services and increase its economic profits while attracting customers. The cloud environment consists of \(m\) physical machines (\(\{PM_{1},PM_{2},\ldots,\ PM_{m}\}\)), and each one can host multiple virtual machines. Each physical machine is defined by three characteristics: processing power, amount of memory, and price coefficient. The purpose of defining the price coefficient is to take into consideration the price difference when using different machines in the model. In addition, the system model incorporates a resource broker for the cloud environment. This particular component is responsible for resource management and serves as an intermediary between the cloud user and the cloud provider. The cloud broker component receives the user request, which includes the required amount of resources, the task's volume (measured in million instructions), the task's deadline, and the user's total budget. Therefore, a user request can be described by the following Quadruple.  
\[User\_Request=(Num\_of\_Computing\_Elements,\ Task\_volume, Deadline, Budget)\]The cloud user who is a player in the dynamic game of the proposed method is identified by their request. The cloud broker identifies the balance of the competition among participants and assigns the appropriate virtual machine to the user's inquiry at a reasonable cost. The general model of the system is shown in Figure 1.