esa-sceva/satcom-chunk-collection huggingface dataset:
# A Testbed for GNSS-Based Positioning and Navigation Technologies in Smart Cities:
## 2 Innovative Positioning and Navigation Technologies
### GNSS Snapshot Cloud Processing  
Internet of Things (IoT) is undoubtedly one of the main enablers of Smart Cities, bringing the required connectivity for sensors to report their gathered data to the remote control units in an efficient manner. Since most IoT sensors are battery powered, speficic low-power wireless communication technologies have been developed in the recent years such as LoRa, SigFox, or narrowband IoT (NB-IoT) [25]. Apart from gathering data and reporting them to the remote control unit, many IoT sensors do also need to report their position as well. This is often a key requirement because data, per se, may be useless in certain applications if not geo-tagged. It is for this reason that many IoT sensors incorporate GNSS chipsets to exploit GNSS signals that are available anytime, anywhere on Earth, and can be used without the need of any additional infrastructure. Unfortunately, processing GNSS signals is a very demanding and power-hungry task that seriously compromises the battery lifetime of IoT sensors [26]. Actually, many sensors do only activate the GNSS chipset when they have no other choice to locate themselves [27]. This usually comes after trying first with other low-power technologies such as Bluetooth, Ultra-Wideband, or even Wi-Fi(tm). So for IoT positioning, GNSS is often the last card to be played [28].  
The approach adopted in the HANSEL project constitutes a radical paradigm shift in GNSS positioning for IoT sensors. The two key features are the following. First, just a piece of the received GNSS signal is gathered by the sensors, instead of permanently and continuously gathering GNSS signals as in conventional GNSS receivers. This allows moving from an \"always-on\" operation to a much more power efficient \"on-off\" operation. The second feature, and the most important one, is that the gathered piece of signal or _snapshot_ is not processed locally at the sensor. Instead, it is transmitted to a cloud platform where the snapshot is processed remotely and the user's position is ultimately obtained [29]. While there is a cost for transmitting the snapshot of GNSS samples to the cloud, the size of that file can be as small as a few kilobytes, so the power savings are still dramatic [29]. The result is henceforth referred to as the GNSS snapshot cloud processing and it becomes the most power-efficient GNSS positioning technique for IoT, as acknowledged in [26].
## 2 System Description
### Dimensioning of the Boost Converter
Its target value is recommended to be 0.40 [30; 31], although it is acceptable for the final value to be between 0.30 and 0.60 [32]. The initial values implemented in the Simulink model and used for the calculations, are presented in Figure 5.2 The obtained values for the capacitors and the coil, through Equations (2)-(4), are presented in Table 3, which were also implemented in the model. The final value of the inductor used in the model, is based on the coil commercialized by KEMET, model SC-18-085J, with an inductance of 0.85 mH [33]. The final \(r\) value obtained fulfills its design condition, so the coil can be safely used in a prototype working under this conditions. The capacitors are based on the ones commercialized by LGN [34], where the output is composed by two capacitors in series, which equivalent capacity is 110 \(\mu\)F, while the input is composed by two capacitors in series but with 28 \(\mu\)F. Each capacitor supports 600 V [34] and when connected in series they support voltage higher than 1000 V. The capacitor's capacity is much higher than the capacity needed to obtain a voltage ripple of 5 V considered in the design phase, so its voltage ripple is expected to be much lower, even below 1 V.
## 5 Experiments and Results  
### Dataset  
The U.S. Climate Normals dataset ([[https://www.ncdc.noaa.gov/cdo-web/datasets](https://www.ncdc.noaa.gov/cdo-web/datasets)]([https://www.ncdc.noaa.gov/cdo-web/datasets](https://www.ncdc.noaa.gov/cdo-web/datasets)), accessed on 16 June 2023) comprises weather and climate data from over 1100 U.S. stations and territories. It includes hourly, daily, monthly, seasonal, and annual readings of temperature, wind statistics, mean sea level pressure, dew point, and cloud cover. These records undergo thorough quality assurance assessments at the National Centers for Environmental Information (NCEI) of the National Oceanic and Atmospheric Administration (NOAA).  
The NDBC-TAO dataset [[https://tao.ndbc.noaa.gov/tao/data_download](https://tao.ndbc.noaa.gov/tao/data_download)]([https://tao.ndbc.noaa.gov/tao/data_download](https://tao.ndbc.noaa.gov/tao/data_download)) (accessed on on 10 June 2023) was collected from sensors in the Tropical Pacific Ocean and sent to the National Oceanic and Atmospheric Administration (NOAA). These sensors measure different attributes including sea surface temperature, wind speed, conductivity, sea level pressure, and salinity.  
Details regarding truth data tensors for both the U.S. Climate Normal dataset and the NDBC-TAO dataset are shown in Table 3.
## 2 Methodologies  
The proposed RR-DTDWT consists of four parts; namely, DTDWT-based signal domain transformation, feature extraction, AGRM-based feature selection and SVM-based classification. The framework of the proposed method is shown in Figure 1. The principles of the four parts are given separately in the following subsections.
# A Project Scheduling Game Equilibrium Problem Based on Dynamic Resource Supply
## 5 Practical Application
### Calculation Results  
The designed hybrid algorithm was run in MATLAB(R2018b) on the collected data. After running the program 30 times, an optimal solution was obtained. The total project scheduling time and cost were 48.9 and 8326.54, respectively. The upper planning MRCPSP calculation results are listed in Table 7, showing the start-end time and mode selection of each activity; the corresponding Gantt chart is shown in Figure 6. The calculation result of the MPSCP of the lower planning is shown in Table 8, which defines the transportation volume of the three resources on each transportation route in each time period. The total transportation cost and time were 1144.38 and 13.73, respectively. The convergence iteration is 146 times, and the computation time is 956.3 s.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{6}{c}{**GADS**} & \multicolumn{6}{c}{**DIWPSO**} \\ \cline{2-13}  & \(\mathbf{L_{1}}\) & \(\mathbf{T_{1}}\) & \(\mathbf{p_{c}}\) & \(\mathbf{p_{m}(0)}\) & \(\mathbf{L_{2}}\) & \(\mathbf{T_{2}}\) & \(\mathbf{c_{1}}\) & \(\mathbf{c_{2}}\) & \(\mathbf{\sigma}\) & \(\mathbf{\eta_{\max}}\) & \(\mathbf{\eta_{\min}}\) & \(\mathbf{\omega(1)}\) & \(\mathbf{\omega(T_{2})}\) \\ \hline Values & 100 & 300 & 0.7 & 0.1 & 100 & 200 & 2 & 2 & 0.1 & 0.6 & 0.2 & 0.9 & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Hybrid algorithm parameter setting.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Cost Parameters**} & \multicolumn{6}{c}{**Resource Types**} & \multicolumn{6}{c}{**Resource Types**} \\ \cline{2-13}  & \(\mathbf{k_{1}}\) & \(\mathbf{k_{2}}\) & \(\mathbf{k_{3}}\) & \multirow{2}{*}{**Time Parameters**} & \(\mathbf{k_{1}}\) & \(\mathbf{k_{2}}\) & \(\mathbf{k_{3}}\) \\ \hline \multirow{9}{*}{\(c_{ik}\)} & \(c_{11k}\) & N(5.20,3.1) & N(6.00,4.2) & N(5.82,3.8) & \multirow{9}{*}{\(t_{11k}\)} & N(0.34,0.21) & N(0.37,0.22) & N(0.32,0.18) \\  & \(c_{21k}\) & N(3.25,2.1) & N(3.66,2.2) & N(3.72,1.8) & & \multirow{2}{*}{\(t_{21k}\)} & N(0.25,0.1) & N(0.26,0.15) & N(0.22,0.18) \\  & \(c_{31k}\) & N(4.23,1.7) & N(4.43,2.1) & N(4.59,2.4) & & & \(t_{31k}\) & N(0.23,0.12) & N(0.21,0.1) & N(0.19,0.13) \\  & \(c_{41k}\) & N(6.12,3.8) & N(6.44,4.2) & N(6.40,4.0) & & & & & & & & & \\  & \(c_{12k}\) & N(5.57,2.8) & N(5.41,3.0) & N(5.77,4.2) & & & & & & & & & & \\  & \(c_{22k}\) & N(6.21,4.1) & N(6.33,4.2) & N(6.50,3.8) & & & & & & & & & & \\  & \(c_{32k}\) & N(5.60,3.1) & N(5.41,3.0) & N(5.77,4.2) & & & & & & & & & & & \\  & \(c_{42k}\) & N(3.63,2.1) & N(3.84,2.2) & N(4.00,2.0) & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Unit transportation cost and time of resources.
## 3 Results and Discussion
### Adsorption Studies of Cu\({}^{2+}\) Metal Ion
#### 3.2.4 Effect of Cu\({}^{2+}\) Aqueous Solution Initial Concentration Using WPET-SiAMPy Nanofibres  
The effect of the Cu\({}^{2+}\) metal ion concentration was investigated to determine the optimum Cu\({}^{2+}\) concentration using the modified nanofibres (WPET-SiAMPy nanofibres) as the adsorbent. The Cu\({}^{2+}\) adsorption from the 100 mL of different concentrations (10, 50, 100 and 200 mgL\({}^{-1}\) Cu\({}^{2+}\)) were carried out using the WPET-SiAMPy nanofibres (0.001 g) (see Figure 7) with the pH of the Cu\({}^{2+}\) aqueous concentrations held at 5 (see Figure 4). The obtained experimental results of the effect of initial concentration of Cu\({}^{2+}\) on WPET-SiAMPy nanofibres are shown in Figure 8.  
Figure 8 presents the binding capacity of Cu\({}^{2+}\) based on Cu\({}^{2+}\) adsorbed on WPET-SiAMPy nanofibres at different Cu\({}^{2+}\) initial concentrations. The results obtained show that the binding capacity directly increased with an increase in initial concentration of Cu\({}^{2+}\). The effect of the initial concentration of Cu\({}^{2+}\) provides the necessary experimental data to understand the resistance, if any, to the adsorption of Cu\({}^{2+}\) ions transferred between the aqueous solution and WPET-SiAMPy nanofibres. The increase in the initial concentration of Cu\({}^{2+}\) improved the uptake of Cu\({}^{2+}\) adsorption as the Cu\({}^{2+}\) concentration increased from 10 mgL\({}^{-1}\) with a binding capacity of 0.13 mmol/g, to 200 mgL\({}^{-1}\), in which case the binding capacity was 2.34 mmol/g. The saturation point was not observed for even the highest  
Figure 8: Effect of initial Cu\({}^{2+}\) concentration on WPET-SiAMPy nanofibres (contact time 1 min, pH 5, dosage 0.001 g, 10 mL).  
Cu\({}^{2+}\) initial concentration, showing a very high number of Cu\({}^{2+}\) binding sites available on the dosed adsorbent. The availability of sufficient Cu\({}^{2+}\) ions and adsorption sites increased the Cu\({}^{2+}\) ions binding capacity onto the chelating adsorbent nanofibres [20].
## 5 Results and Analysis
### Proficiency Examination  
When the metrics for efficiency and practicality yield conflicting results, proficiency serves as a composite metric to mitigate evaluation uncertainties. Figure 11 illustrates the proficiency values (\(\zeta\)) corresponding to each IM and DM. A lower \(\zeta\) value indicates higher proficiency. The results demonstrate that when each DM employs different IM parameters, the variation in \(\zeta\) remains consistent. DMs such as \(\theta_{\text{max}}\), \(\theta_{\text{c}}\), \(D_{\text{e}}\), and \(F_{\text{NC}}\) exhibit superior proficiency. Conversely, \(R_{\text{E}}\), \(F_{\text{SC}}\), and \(M_{\text{C}}\) rank lower in terms of proficiency. For a more nuanced comparison of IM proficiency, Figure 12 presents \(\zeta\) values for each IM when \(R_{\text{E}}\) is utilized as the DM. It becomes evident that spectral-type IMs display lower \(\zeta\) values, signifying higher proficiency. This is followed by velocity-type and acceleration-type IMs.  
Figure 8: The value of \(b\) with different IMs and DMs.  
Figure 10: Practicality ranking under different DMs.  
Figure 9: The value of \(b\) under each IM with \(D_{\text{e}}\) as the DM.  
On the contrary, vector compound-type IMs manifest significant fluctuations in \(\zeta\) and poor proficiency, and displacement-type IMs demonstrate the least proficiency. A detailed analysis of \(\zeta\) values across different IMs for each DM is depicted in Figure 13, which reveals that the IMs with higher proficiency include \(I_{\rm v}\), \(I_{\rm a}\), PGA, SMA, and ASI. Furthermore, IMs with low proficiency are displacement-type IMs, especially DI and CAI.
## 3 Dataset Descriptions
### Unsw-Nb15  
The Cyber Range Lab of the Australian Center for Cyber Security released this dataset in 2015, and it is frequently utilized in the research community (ACCS). For the UNSW-NB15 dataset [25], the authors used raw network packets generated by the IXIA perfect storm program. At the bottom of the test, nine attack scenarios are implemented: DoS, fuzzes, analysis, backdoor, generic, reconnaissance, shellcode, exploits, and worms. Forty-nine network traffic features were extracted, and the Argus and Bro-IDS programs were employed. There are 2,540,044 streams in the dataset, including 2,218,761 benign and 321,283 aggressive streams [17; 28]. We split it into 20% for tests and 80% for training, with a stratification option to keep these percentages static for all classes. A total of 2,540,047 were taken from this dataset: 2,032,037 for the training set and 508,010 for the test set. They were distributed into 10 classes, as shown in Table 5.  
\begin{table}
\begin{tabular}{l l l} \hline
**IoT Traffic** & **Type of Event** & **Data Record** \\ \hline Normal & Normal & 1,091,198 \\ \hline \multirow{6}{*}{Attack} & DDoS-UDP & 97,253 \\ \cline{2-3}  & DDoS-ICMP & 54,351 \\ \cline{2-3}  & SQL-injection & 40,661 \\ \cline{2-3}  & DDoS-TCP & 40,050 \\ \cline{2-3}  & Vulnerability scanner & 40,021 \\ \cline{2-3}  & Password & 39,946 \\ \cline{2-3}  & DDoS-HTTP & 38,835 \\ \cline{2-3}  & Uploading & 29,446 \\ \cline{2-3}  & Backdoor & 19,221 \\ \cline{2-3}  & Port-scanning & 15,982 \\ \cline{2-3}  & XSS & 12,058 \\ \cline{2-3}  & Ransomware & 7751 \\ \cline{2-3}  & Fingerprinting & 682 \\ \cline{2-3}  & MITM & 286 \\ \hline \end{tabular}
\end{table}
Table 4: The total numbers and the different types of records in the Edge-IIoT dataset.
# Reservoir Computing Based Echo State Networks for Ventricular Heart Beat Classification
## 2 Materials and Methods
### ECG Data Sources  
The experiment was conducted based on two different databases named the American Heart Association Ventricular Arrhythmia ECG database (AHA) [34] and Massachusetts Institute of Technology-Beth Israel Hospital Supraventricular Arrhythmia database (MIT-BIH SVDB) [35]. These databases are used to train the study's proposed classifier. On the other side, the paper used MIT-BIH arrhythmia to evaluate the performance of our proposed classifier. The list of all acronyms which are used in this study is present in Table 1 and the overall description of these above-mentioned datasets is defined in Table 2. According to the American National Standards Institute/Association for the Advancement of Medical Instrumentation (ANSI/AAMI) standards, the ECG signal recordingshave annotated beats in five groups [36]: Ventricular ectopic beat (VEB), Supraventricular ectopic beat (SVEB), Fusion beat (F), Unclassified and paced beat (Q), and Non-ectopic beat (N).  
However, paced beat recordings were omitted from the datasets. These annotated signal-based ECG recordings have a 30 min long duration, whereas the MIT-BIH-SVDM dataset has two types of lead recordings: the modified lead II (MLII) and V1; a few patients also have V5 and V2 instead of V1. The AHA dataset has no information about the lead recordings. Hence, this study's methodology is based on the classification of a ventricular heartbeat. Therefore, in this paper we consider two classes of AAM/ANSI standards for our binary class models to determine the normal and abnormal beats: Supraventricular beats as normal morphology and has a label 0, whereas the ventricular beats as abnormal morphology and has a label 1.
## 2 Pneumatic Muscles vs. Pneumatic Cylinders
### Computation of the Forces Developed by the Analyzed Pneumatic Actuators  
A number of papers on applications of pneumatic muscle assert that this type of actuator offers more advantages than pneumatic cylinders, without, however, following up with concrete arguments in this respect. Some of the advantages of pneumatic muscles enumerated in [33] are:  
* The developed initial force is significantly larger at the same diameter;
* Small mass per unit force;
* Smaller consumption of compressed air;
* Superior energy density, etc.  
In order to prove these assertions, further discussion on the authors present and a comparison between single-acting pneumatic cylinders and pneumatic muscles manufactured by Festo are presented (Figure 1). These specific actuators were selected for the study because the forces developed by them have similar evolutions over the length of the stroke. The maximum force is developed at the beginning and reaches its minimum by the end of the stroke.  
In the case of single-acting cylinders, compressed air is applied to a single face of the piston and, consequently, motion is generated in a single direction. The piston is then reversed automatically by means of a built-in spring. The force developed by a cylinder of this type is calculated by Equation (1):  
\[F=p\times\frac{\pi\cdot D^{2}}{4}-F_{FR}-F_{spr} \tag{1}\]  
where \(p\) is the pressure of the compressed air, \(D\)--the piston diameter, \(F_{FR}\)--the friction forces, \(F_{spr}\)--the spring force.  
The pneumatic muscle generates displacement by changing its geometrical form when fed compressed air. As the air pressure increases, a traction force acting along the muscle axis causes it to shorten. Such axial contraction represents the stroke of the motor. The maximum axial contraction is of about 20% of the muscle's initial length. The axial  
Figure 1: Types of studied pneumatic motors: (**a**) Single-acting cylinder; (**b**) Pneumatic muscle.  
force is maximum at the onset of contraction and falls to zero as the end of the stroke is reached. The force developed by a pneumatic muscle is calculated by Equation (2) [30]:  
\[F=p\times\frac{\pi}{4}\times d^{2}\times\left[\frac{3\times\cos^{2}\alpha-1}{1- \cos^{2}\alpha}\right] \tag{2}\]  
where \(p\) is the working pressure, \(d\) the diameter of the pneumatic muscle, and \(\alpha\) is the winding angle of the synthetic fiber tissue the envelopes the inner tube of the muscle.  
The following models of the two types of pneumatic motors were selected for the purpose of this comparative study:  
* Round cylinders, single-acting, pushing, based on ISO 6432, manufactured by FESTO, Esslingen, Germany, shown in Figure 2a [34];
* Pneumatic muscles, models DMSP-xx-N, manufactured by FESTO, Esslingen, Germany, shown in Figure 2b [35].
## 2 Problem Statement
### Simulation Scenarios  
There is bit error rate (BER) in wireless transmissions, and packet error rate (PER) occurs as long as BER exists. In order to demonstrate the transmission performance of SCTP and TCP over different links on wireless wired networks and wireless multi-hop networks, simulations were performed using NS2.35.  
The simulations were done on the IEEE 802.11 based on the wireless network, and the wireless network was connected to the wired network. The simulation topology was a link with seven nodes. In Figure 1, from left to right, the start node, node M\({}_{0}\), and node M\({}_{1}\) were connected by the wired network; node M\({}_{1}\) also had a wireless interface, which connected the end node (on the right side) through wireless node N\({}_{0}\), node N\({}_{1}\), and node N\({}_{2}\). In the simulations, the start node and the end node ran TCP protocol and SCTP protocol separately, and file transfer protocol (FTP) was used to send data on the application layer. During the simulations, the error-module was used to simulate the PER in the paths. Table 1 shows the detail parameter configurations. The PER was between 0% and 50%. The simulation results show that with the change of PER value in the wireless network, the packet delay, throughput, and packet loss rate in the network had certain changes.  
Figure 1: Simulation scene topology.
## 3 Materials and Methods
### Evaluation Metrics and Feedback Adjustment Strategies
#### 3.4.1 Reward-Based Feedback Adjustment  
In this approach, the system adjusts the objectives' weights based on the reward signal derived from the performance metrics. If the system's performance in one objective area (e.g., environmental sustainability) improves significantly, the corresponding weight is increased, making that objective more influential in future decisions. Conversely, if an objective is not performing well, its weight is reduced to allow the system to focus on other more urgent objectives.  
\[r(t)=\lambda_{1}\cdot ESI(t)+\lambda_{2}\cdot EGI(t)+\lambda_{3}\cdot SWI(t)- \lambda_{4}\cdot STO(t) \tag{14}\]  
where \(ESI(t)\), \(EGI(t)\), and \(SWI(t)\) represent the environmental, economic, and social indices at time \(t\); \(STO(t)\) is the sustainability trade-off at time \(t\); and \(\lambda_{1}\), \(\lambda_{2}\), \(\lambda_{3}\), and \(\lambda_{4}\) are the coefficients that scale the relative importance of each metric. The reward function dynamically adjusts the weights of the objectives. Positive feedback from high-performance metrics leads to an increase in the corresponding weights, while negative feedback drives the system to reduce the focus on low-performing objectives.
# Recommendation Systems: Algorithms, Challenges, Metrics, and Business Opportunities
## 3 Challenges in Recommendation Systems
### Cold-Start  
The term 'cold start' stems from automobiles. When the engine is cold, they have difficulty starting up, but they have no problems running once they reach their optimal temperature. The same problem can be applied to RSs. When there is insufficient information or metadata available, a RS does not perform optimally. Cold starts can be classified into two distinct subsets: product cold starts and user cold starts [25]. Whenever a new product is displayed on an e-commerce site, it goes through the product cold start, and this means that there are no reviews due to the lack of user interaction. If there are not enough user interactions, the RS will not know when to display the ad related to that product. The cold-start behavior occurs when a user creates an account for the first time and does not have any product preferences or history available to base recommendations. The cold start problem always exists for new or existing users. For example, [PERSON] searches for new televisions on an e-commerce site; within a week, he purchases one and is no longer interested in purchasing televisions; whatshould the RS display now? Users will always be interested in new and different things. Analyzing the metrics and methods for cold-start recommendations, we find that the Bayes classifier is most used [26]. Bayesian models are graphical models used in probability and artificial intelligence. In model-based RSs, a form of Bayesian reasoning is likely to be applied, whether it is content- or collaborative-based. The most popular method of utilizing Bayesian models is the naive Bayes model [25]. Despite its simplicity, it has proved to be the most accurate. In the naive Bayes classification, different attributes are assumed to be mutually independent features of the items [26]. With this, one can estimate new item's characteristics with a set of attributes not found in the training data. The projection in WALS (weighted alternating least squares) and heuristics are used to address the cold-start problem to a certain degree. For the projection in the WALS method, if there is a new item not seen in training, yet the system has a few interactions with users, the user's embeddings for this item can be calculated easily, without the need to retrain the entire model as shown in Equation (1).  
\[min_{ui_{0}\in R^{d}}\parallel A_{i0}\;-\;u_{i0}V^{T}\parallel \tag{1}\]  
Equation (1) is equivalent to one iteration in the WALS method, where the user's embeddings are kept exact, and the system solves for the embedding of the new item, and the same process can be performed for a new user to keep the model up to date. In the heuristics methods that generate fresh items' embeddings, the embeddings can be approximated if the system does not have interactions. This is completed by taking the average of the item's embeddings of the same category.