esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Test Case and the Data Sets  
The wind turbine of interest is sited in Italy and the model type is Vestas V52. The rated power is 850 kW; the cut-in wind speed is 4 m/s; the rated wind speed is 14 m/s. The rotor diameter is 52 m.  
Two data sets were employed:  
1. \(D_{1}\) goes from 1 January 2019 to 31 December 2019;
2. \(D_{2}\) goes from 1 January 2020 to 31 December 2020. The available validated measurements have 10 min of sampling time, and those at disposal for the present study are:  
1. Produced power \(P\);
2. Undisturbed wind speed \(v_{\infty}\) reconstructed by the nacelle anemometer;
3. Rotor speed \(\omega\);
4. Blade pitch \(\beta\);
5. Ambient temperature \(T\). The data pre-processing was based on the following steps, which are easily replicable in industrial applications:  
* The data were filtered on the wind turbine's normal operation using the appropriate runtime counter provided by the SCADA system;
* Industrial wind farms rarely operate under curtailment dictated by grid requirements, and this is the case also for the present wind turbine. This aspect has nothing to do with wind turbine performance, and therefore, operation under curtailment should be filtered out for the purposes of the present work. This can be done by noticing that a wind turbine operates in derated conditions by pitching anomalously with respect to the normal. Therefore, the average wind speed/blade pitch [40] can be used for individuating outliers associated with derating. The filtering can be practically performed by eliminating time steps where the blade pitch deviates more than a threshold (2\({}^{\circ}\) in this study) with respect to the average blade pitch for the given wind speed;
* For each wind turbine, data were filtered between cut-in and the rated speed because the power monitoring becomes trivial above the rated speed. Upon filtering, \(D_{1}\) constitutes 18,000 samples and \(D_{2}\) 19,930 samples. A summary of the features of the data sets is reported in Table 1.  
Figure 1: The workflow of the proposed method.  
The dependence of wind turbine power on environmental conditions is a widely debated issue, in particular as regards the external temperature [41]. Given the applied point of view of the present study, the simplest method was selected: it consists of the renormalization of \(v_{\infty}\) by considering the effect of air density as indicated in Equations (2) and (3):  
\[v_{c}=v_{\infty}\left(\frac{\rho}{\rho_{ref}}\right)^{\frac{1}{3}} \tag{2}\]  
\[\rho=\rho_{ref}\frac{T_{ref}}{T} \tag{3}\]  
where \(v_{c}\) is the corrected wind speed, \(\rho\) is the air density measured on site, \(\rho_{ref}=1.225\) kg/m\({}^{3}\) is the air density in standard conditions, \(T_{ref}\) is the absolute temperature in standard conditions (288.15 K), and \(T\) is the absolute ambient temperature measured on site.  
An example of the raw data set and of the pre-processed data set (resulting in \(D_{1}\)) is reported in Figure 2.
## 3 Analysis of Original Research Articles
### Machine Learning Techniques  
Many techniques of machine learning have been applied to predict commodity prices in agriculture using different methodologies, as illustrated in Table 3, to improve predictive accuracy. The indirect influence of crude oil on food prices was examined by [PERSON] and [PERSON] [18] in a study using Principal Component Analysis (PCA) to study co-movement between the food prices of seven major food commodities (eggs, meat, milk, oilseeds, rice, sugar, and wheat) and macroeconomic indices (including crude oil price, CPI, Food  
Figure 2: PRISMA 2020 flow diagram illustrating the study selection process. Diagram generated using the online PRISMA2020 Shiny App developed by [PERSON] et al., 2022 [17], available at: [[https://estech.shinyapps.io/prisma_flowdiagram/](https://estech.shinyapps.io/prisma_flowdiagram/)]([https://estech.shinyapps.io/prisma_flowdiagram/](https://estech.shinyapps.io/prisma_flowdiagram/)) (accessed on 10 June 2025). Production Index, and GDP). They found that crude oil prices did not directly affect the primary food price component, but they had an impact on the food production index, and in this way, they indirectly affected food prices. Finally, [PERSON] et al. (2019) applied machine learning methods to ascertain whether nonlinear effects and asymmetries in price transmission were present and showed that local rice prices in Dakar are being affected by world prices and are more sensitive to world price increases than to price declines. Figure 4: Distribution of papers by country. Figure 3: Taxonomy of the agriculture commodity price prediction techniques. Figure 5: Published articles per year. Figure 6: Percentage of papers published by subject area. Machine learning models have been tailored to forecast both yields and prices for crop-specific forecasting. [PERSON] et al. (2020) developed a semiparametric regression model to forecast yield and a futures-based model for price forecasting, emphasizing within- and between-year relationships and incorporating futures prices with basis adjustment to
## 4 Experiments and Discussion
### Data Analysis  
The dataset was derived from the RCS message database of our cooperated company. Since the RCS service starts in China, most of the RCS messages do not contain much information, and look like traditional SMS messages. A total of 440 RCS messages with rich information were manually selected for this experiment. The contents of spam RCS messages include lottery, money or tax defrauding, and advertisements with illegal content. The legal RCS messages refer to government notifications, express delivery notifications, bank notifications and legal advertisements. The data of the mentioned 210 fields were extracted. Then, these messages were manually labeled, including each message and its cards. The statistics of these messages are outlined in Table 2.  
In addition, we randomly chose two-thirds of the messages to generate the training set in the experiment. The rest of the messages form the testing set as shown in Table 3.
# Real-Time Location-Positioning Technologies for Managing Cart Operations at a Distribution Facility
## 4 Case Study: Tracking of Carts in a Large and High-Traffic RFID-Enabled Air Mail Centre
### Staging Durations of Carts at Different Locations  
Data visualization is one of the most powerful tools of data analytics. Figure 8 shows the cart locations for a selected hour (left) and a selected date (right). The carts are represented by different colors. The circle size is directly proportional to the total duration that the cart was staying at the location. That is, a larger circle means a longer duration the cart was staying there. Figure 8 shows that the carts spent most time near the pocket sorting machine and at/near the ULD loading area (i.e., the area of ETV in the figure)  
were trying to relocate the cart. Such movements happen when a crowd of carts blocked a cart that was required to be moved to the ULD loading area, and hence relocations of carts were needed. The AMC management can consider the number of occurrences of such clusters of small dots with the same color as a measure of operational efficiency. If this occurs very frequently, it indicates the rules of guiding the staging operations are not good enough and the operational team should reconsider the staging rules.
# A Survey on Applications of Reinforcement Learning in Flying Ad-Hoc Networks
## 2 Fundamentals of Deep Reinforcement Learning
### Reinforcement Learning  
Reinforcement learning is an effective and extensively used tool of AI which learns about the environment by taking different actions and achieves an optimal policy for  
Figure 1: Flying ad-hoc network of UAVs.  
operation. The RL consists of two main components: an agent and an environment. The agent explores the environment and decides which action to take using the Markov decision process (MDP) [18].  
MDP is a framework for modeling decision-making problems and helping the agent to control the process stochastically [18]. MDP is a useful tool for dynamic programming and RL techniques. Generally, MDP has four parameters represented by the tuple \((S,A,p,r)\), where \(S\) is a finite state space, \(A\) is a finite action space, \(p\) is the transition probability from the present state \(s\) to the next state \(s^{\prime}\) after taking action \(a\), and \(r\) is the immediate reward given by the environment for action \(a\)[19]. As shown in Figure 2, at each time step \(t\), the agent observes its present state \(s_{t}\) in the environment and takes action \(a_{t}\). Then, the agent receives a reward \(r_{t}\) and the next state \(s_{t+1}\) from the environment. The main goal of the agent is to determine a policy \(\pi\) to accumulate the maximum possible reward from the environment. In long term, the agent also tries to maximize the expected discounted total reward defined by \(\max[\sum_{t=0}^{T}\delta r_{t}(s_{t},\pi(s_{t}))]\), where \(\delta\in[0,1]\) is the discount factor. Using the discounted reward, a Bellman equation named the \(Q\)-function (1) is formed to take the next action \(a_{t}\) using MDP when the state transition probabilities are known in advance. The \(Q\)-function can be expressed as  
\[Q(s_{t},a_{t})=(1-\alpha)\times Q(s_{t},a_{t})+\alpha[r+\delta(\max Q(s_{t+1},a_{t}))], \tag{1}\]  
where \(\alpha\) is the learning rate.  
RL with a \(Q\)-function is also known as \(Q\)-learning. Initially, the agent explores every state of the environment taking different actions and forms a \(Q\)-table using the \(Q\)-function for each state-action pair. Then, the agent starts exploiting the environment by taking actions with the maximum \(Q\)-value from the \(Q\)-table. This policy is known as the \(\epsilon\)-greedy policy, where the agent starts exploring or exploiting the environment depending on the value of the probability \(\epsilon\). An illustration of \(Q\)-learning is presented in Algorithm 1.  
```
Require:\(Q(S,A)=0\). Ensure: Initialize \(\alpha,\delta,\epsilon\). for\(t=1,2,\dots,T\)do  Choose an action \(a_{t}\) for present state \(s_{t}\) based on the value of \(\epsilon\).  Obtain an immediate reward \(r_{t}\) and next state \(s_{t+1}\).  Update \(Q(S,A)\) via [PERSON] decision process (1). \(s_{t}\gets s_{t+1}\)  end for  Optimal policy, \(\pi(s)=\arg\max Q(S,A)\)
```  
**Algorithm 1** The \(Q\)-learning Algorithm.  
Figure 2: The agent-environment in [PERSON] decision process.
###### Abstract  
Driven by the fast growth of wireless communication, the trend of sharing spectrum among heterogeneous technologies becomes increasingly dominant. Identifying concurrent technologies is an important step towards efficient spectrum sharing. However, due to the complexity of recognition algorithms and the strict condition of sampling speed, communication systems capable of recognizing signals other than their own type are extremely rare. This work proves that multi-model distribution of the received signal strength indicator (RSSI) is related to the signals' modulation schemes and medium access mechanisms, and RSSI from different technologies may exhibit highly distinctive features. A distinction is made between technologies with a streaming or a non-streaming property, and appropriate feature spaces can be established either by deriving parameters such as packet duration from RSSI or directly using RSSI's probability distribution. An experimental study shows that even RSSI acquired at a sub-Nyquist sampling rate is able to provide sufficient features to differentiate technologies such as Wi-Fi, Long Term Evolution (LTE), Digital Video Broadcasting-Terrestrial (DVB-T) and Bluetooth. The usage of the RSSI distribution-based feature space is illustrated via a sample algorithm. Experimental evaluation indicates that more than 92% accuracy is achieved with the appropriate configuration. As the analysis of RSSI distribution is straightforward and less demanding in terms of system requirements, we believe it is highly valuable for recognition of wideband technologies on constrained devices in the context of dynamic spectrum access.  
technology recognition; RSSI; multi-modal distribution; experimental study; constrained devices; NGWN +
Footnote †: journal: Sensors
## 4 Model and Design of SPIDEP Platforms
### Variants of Software Architectures
#### 4.2.2 SPIDEP SOA Variant: Implementation of the Platform  
For the implementation of the SPIDEP-SOA platform, we opted to modernize the first version developed (Beta \"traditional implementation of seven SOA services, without API support\") [23] to the release candidate (RC) (\"Modernization of services and current technologies (language, database, libraries and integration of an API\")), whose purpose is to provide the necessary attributes for the implementation of versatile high-performance applications and contrast it with the MSA variant. Therefore, we proceeded to the refactoring and restructuring of the existing components along with their data [53; 82], as shown in Table 1 for each version.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Description**} & \multicolumn{2}{c}{**Version**} \\ \hline
**Category** & **Subcategory** & **Beta** & **RC** \\ \hline \multirow{5}{*}{System architecture} & Architecture styles & SOA & SOA-based on Quality Requirements \\  & SOA patterns & Enterprise Service Bus (ESB) & Service Implementation Patterns \\  & Communication protocol & HTTP & HTTP & HTTP \\  & HTTP methods & Disable & GET, POST, PATCH \& DELETE \\  & Messaging type & Synchronous & Synchronous/ Asynchronous \\ \hline \multirow{5}{*}{System architecture} & Programming language & PHP 5.6 & PHP 7.4 \\  & Framework & Laravel 5.1 LTS & Laravel 6 LTS \\  & & acoustep/entrust-gui; & Guzzle; fidelometer/proxy; \\  & & zizaco/entrust, Laravel Passport; & kylekatarnls/laravel-carbon-2; \\  & Core libraries & mockery/mockery; & zizaco/entrust; Queues; Laravel Sanctum; \\  & & phpunit/phpunit; tinker; & mockery/mockery; phpunit/phpunit; \\  & & composer, and others & tinker; composer, Custom libraries, and others \\  & UI & Bootstrap 3 & Bootstrap 4 \\  & Web server & Apache & Apache as an endpoint and NGINX as a load balancer for the implementation \\ \multirow{2}{*}{Used technologies} & DBMS & MariaDB 10.1 & Postgreglosed 10 \\  & Data access methods & Stored procedures & Stored procedures \\ \multirow{2}{*}{Used technologies} & Object–relational mapping & Elogquent & \multirow{2}{*}{Elogquent} & \multirow{2}{*}{Elogquent} \\  & API Scheme & Disable & & \\ API Gateway & Disable & & & \\ \multirow{2}{*}{Deployment OS} & Bare Metal & Custom Docker Container \\  & & Ubuntu 16 LTS & Ubuntu 18 LTS \\ \multirow{2}{*}{Authentication Scheme} & HTTP Basic Authentication & Custom Authentication & OAuth1, OAuth2 or JWT Web Token) \\  & & OpenSSL (AES-256 and AES-128) & OpenSSL (AES-256 and AES-128) & \\  & & & & public/private ssa key pair \\ \hline \hline \end{tabular}
\end{table}
Table 1: General structure in the SPIDEP-SOA platform.
## 5 The SBS Threshold for Optical Pulses with MI Considered
ASE noise level leads to higher pump depletion induced by MI. Therefore, the Brillouin gain decreases and the SBS threshold increases correspondingly. To verify the results of this theoretical analysis, we conducted an experiment to measure the SBS threshold with different repetition rates and ASE noise levels. The experimental setup is shown in Figure 7, and is similar to the one in our previous paper [17]. The difference is that an ASE source is added to adjust the ASE noise level of the input light. The optical pulse used in the experiment has rectangular envelope. The duration of the pulse is 200 ns. The rising and falling times are both less than 10 ns, so the effect of the edge of the pulse can be ignored. The repetition rates of the optical pulses increase from 50 kHz to 1000 kHz. The SBS threshold is defined as the input power at which the Brillouin-Stokes power is equal to the Rayleigh scattering power [19]. This is a lower criterion than the traditional 1% criterion [20], which is aimed at avoiding the effect of the pump depletion induced by Brillouin scattering. The measured SBS thresholds are shown in Figure 8. It is clearly shown that the SBS threshold remains constant with the different repetition rates and ASE noise levels when the repetition rate is higher than 200 kHz. Under these conditions, the average SBS threshold is about 4.2 mW, and the corresponding peak powers are lower than 100 mW with different repetition rates. Therefore, MI cannot occur at this power level, and SBS threshold is not affected by MI. When the repetition rate is 100 kHz, the SBS threshold at first keeps constant and then increases with the ASE noise level. When the repetition rate further decreases, the pump depletion induced by MI gets more serious, so the SBS threshold further increases. The experimental results shown in Figure 7 present the same tendency as the theoretical results in Figure 6. The deviation is attributed to the inaccuracy of the parameters and the nonideal experimental conditions.
# A Replica-Selection Algorithm Based on Transmission Completion Time Estimation in ICN
## 5 Performance Evaluation
### Performance Comparison
#### 5.2.4. NDC Download Delay  
In order to observe the relationship between NDC download delay and the number of users, we modified the number of users under the edge nodes in Experimental Scenario 1 and Experimental Scenario 2 for experiments. The number of users was 30, 40, 50, 60, 70, 80, 90, and 100. In addition, in order to make the experimental results universal, each experiment was repeated 10 times and the average and standard deviation of the average download delay of 10 experiments were extracted.  
Figure 12 is the result of Experimental Scenario 1. The download delay of the TCTE algorithm is the lowest, the IDRS algorithm is second, and the NR algorithm is the worst. With the increase in the number of users, the download delay of the three algorithms continued to increase, and the gap became larger and larger. Figure 12b is a change in the standard deviation. It can be seen that TCTE has the most stable performance, and the performance of the other two algorithms has relatively large fluctuations.  
Figure 13 is the result of Experimental Scenario 2. The download delay of the TCTE algorithm is the lowest, and the NR and DRS algorithm download delay is large. With the increase in the number of users, the download delay of the three algorithms continued to increase. Figure 13b is a change in the standard deviation. It can be seen that TCTE has the most stable performance, and the performance of the other two algorithms has relatively large fluctuations. Different from Experimental Scene 2 is that the NR algo  
Figure 11: Mean and confidence intervals for JFI.  
Figure 10: Variation in [PERSON]’s Fairness Index over time.  
rithm is better than IDRS, and the gap between NR and TCTE download delay does not significantly expand.  
Based on the results of the two experimental scenarios, TCTE always has the smallest NDC average download delay average and standard deviation, while NR and IDRS always have a large average and standard deviation. This means that NR and IDRS can only perform well in very few experiments, but TCTE has the best performance in most cases.  
Figure 12: Mean and standard deviation of the NDC average download delay in Experimental Scenario 1.  
Figure 13: Mean and standard deviation of the NDC average download delay in Experimental Scenario 2.  
To illustrate the effectiveness of the experimental results and analysis in Sections 5.2.1-5.2, we conducted a t-test on the NDC download delay when the number of users is 60. Table 3 shows the results, and the \(p\)-value retains four significant figures. It can be seen that the maximum \(p\)-value in experimental scenario 1 is \(4.512\times 10^{-3}\), so the experimental results are significantly different at the 99.55% confidence level. In experimental scenario 2, the maximum \(p\)-value is \(3.952\times 10^{-2}\), so the experimental results are significantly different at the 96.05% confidence level. Therefore, the experimental results are statistically significant and were not obtained by chance.
## 1 Introduction  
The current COVID-19 pandemic curves are routinely used to investigate and inform governments, healthcare professionals and society on, essentially, the dynamics of contaminations and deaths in a nation. Pandemic curves are presented for several countries on a semi-logarithmic scale and normalized per \(10^{5}\) inhabitants, either on the number of infections or on the number of deaths per day, \(d\)[1]. Limited growth models are mathematical tools employed to describe growth in pandemic scenarios, among others. One reliable and widely verified model of limited growth is the generalized logistic equation proposed by [PERSON] [2]. In the Verhulst model, \(N(t)\) represents the cumulative number of infected people over a time \(t\). The epidemic curve, \(\dot{N}\equiv dN/dt\), is in the run up proportional to \(e^{t/\tau_{grow}}\) with \(\tau_{grow}\), a typical characteristic time, and it shows a maximum at time, \(t_{inf}\propto\tau_{grow}\). The inflection point \(t_{inf}\) is the value that holds:  
\[d^{2}N/dt^{2}\equiv dN/dt=0, \tag{1}\]  
The \(\tau_{grow}\) depends on the sensitivity of the subgroup to get infected and the transmission speed of infection for this group. After \(t_{inf}\), \(N(t)\) increases less rapidly and levels at the value \(M\). The epidemic curve is proportional to \(e^{-t/\tau_{recov}}\). As often observed, the cumulative number, \(N\), and the epidemic curve, \(\dot{N}\), are averaged over a week to reduce the noise in the data. Here, we simulate \(N\) and \(\dot{N}\). The simple substitution \(N^{1/n}=T\), which is a function of time like N, in [3], shows that the generalized logistic equation, which is denoted as \(V_{n}\), is a recast of the Verhulst model denoted as \(V_{n1}\) (logistic equation).  
In this work, we chose the flexible analytical phenomenological model \(V_{n2}\) instead of discrete-time mechanistic models with 10 coupled equations with one-hour step size and a substantial number of unknown parameters and online-available models [4; 5]. The refined discrete SEIR model considers: \(S\), susceptible individuals, \(E\), infected individuals not being infectious yet, and \(I\) infected individuals, the latter subdivided into six classes, \(D(t)\) number of deaths and \(R(t)\) the recovered individuals. All this at the expense of too many unknown parameters and a set of 10 equations with less insight. To overcome this, in this work, heterogeneity in the population is simplified. Statistical inhomogeneity for COVID-19 stems mainly from the age difference in risk to get infected and for deaths. The difference in transmission rate is also different between age groups. In the refined models, as mentioned in [5], ten age groups are considered (0-10; 10-20; \(\dots\) 90-more) with different parameters. The isolation between the fast group characterized by a low \(\tau\) and a slow group with high \(\tau\) is most efficient. Interimingling is the worst condition.