esa-sceva/satcom-chunk-collection huggingface dataset:
# Green IoT: A Review and Future Research Directions
## 4 Energy-Efficient M2M Communications
### Energy-Efficient Hardware  
Energy-efficient hardware is important in M2M communication, where the IoT devices may require operating for long duration on a small battery or other limited power sources. Several hardware components are used to boost the energy efficiency of green M2M networks. Some of these components include:  
1. _Energy-efficient sensors (Green WSNs)_: Some sensors including, temperature and humidity sensors, are known to be very power-hungry devices. Therefore, using energy-efficient sensors is the best way to reduce the power consumption in M2M systems. Details are given in Section 5.
2. _Energy-efficient radios (Green RFID)_: Radio transceivers are a major source of power consumption in M2M devices. Thus, using an energy-efficient radio can improve the power consumption in M2M networks. Details are given in Section 5.
3. _Low-power microprocessors (Green MCUs and ICs)_: By using a low-power microprocessor, the overall power expenditure of an M2M device can be considerably reduced. Details are presented in Section 6.  
M2M technology primarily involves the use of sensors and RFID to collect data and transmit information over the network channel. These two components, RFID and sensor networking, are essential to the functioning of M2M systems. Accordingly, the following sections deal with discussing the role and importance of these two pillars in M2M technology.
## 4 Experiments and Results
### Performance Evaluation Parameter
\[\text{KAPPA}=\frac{P_{0}-P_{e}}{1-P_{e}} \tag{15}\]  
Figure 8:0 Performance results of TP and FP rate. Table 6 summarizes the classification model performance. The outcomes are compared with different execution estimates with various evaluation parameters described above. We analyzed and compared the different classification calculations of this study. We can observe that the accuracy, precision, recall, and F1-score obtained by random forest (95.0-95.5%) is better than NB, SMO, SL, CvR, DT, JRip, and OneR. It is also simple to see that RF presents the uppermost value of correctly classified instances and a training time of 0.11 s. In the wake of picking the RF as the anticipated model, we can now investigate the outcomes acquired by assessing the efficiency of our proposed algorithms. Moreover, Table 5 displays that RF achieved the maximum accuracy, precision, recall, and F1-score compared to other advanced algorithms. Additionally, the RF algorithm presents the lowest MSE and MAE rates, as shown in Figure 8:1 \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Algorithms** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F-Measure (\%)** & **MSE** & **MAE** & **KAPPA** \\ \hline Naïve Bayes & 77.6 & 82.5 & 77.7 & 78.8 & 31.0 & 11.7 & 60.7 \\ SMO & 93.9 & 93.9 & 94.0 & 93.9 & 32.3 & 25.7 & 88.6 \\ Simple & 93.9 & 94.0 & 94.0 & 93.9 & 15.8 & 6.07 & 88.5 \\ Logistic & 89.8 & 92.2 & 89.8 & 90.3 & 17.1 & 5.09 & 81.7 \\ CvR & 90.0 & 90.4 & 90.0 & 89.9 & 22.9 & 15.4 & 80.9 \\ \multirow{2}{*}{Decision Table} & \multirow{2}{*}{90.0} & \multirow{2}{*}{91.4} & \multirow{2}{*}{89.0} & \multirow{2}{*}{89.5} & \multirow{2}{*}{23.4} & \multirow{2}{*}{05.4} & \multirow{2}{*}{80.3} \\  & & & & & & & \\ \multirow{2}{*}{Random Forest} & \multirow{2}{*}{95.0} & \multirow{2}{*}{95.4} & \multirow{2}{*}{95.0} & \multirow{2}{*}{95.0} & \multirow{2}{*}{13.3} & \multirow{2}{*}{04.6} & \multirow{2}{*}{90.3} \\ \multirow{2}{*}{JRip} & \multirow{2}{*}{92.1} & \multirow{2}{*}{92.2} & \multirow{2}{*}{92.1} & \multirow{2}{*}{92.1} & \multirow{2}{*}{19.3} & \multirow{2}{*}{04.7} & \multirow{2}{*}{85.3} \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The performance comparison of the classifiers for diabetes classification. Figure 11: Statistical comparison of performance measures of algorithms. From these outcomes, we can infer that random forest (RF) has outflanked the other algorithms in this study.
# TARS: A Novel Mechanism for Truly Autonomous Resource Selection in LTE-V2V Mode 4
## 3 Preliminaries of D2D Communications  
### D2D Communication  
The 3 GPP is developing an enhanced LTE radio interface called LTE-Advanced (LTE-A) to overcome the shortcomings of LTE. The LTE-A radio interface has many features including D2D communication, Multiple-Input Multiple-Output (MIMO), carrier aggregation, and millimeter wave. D2D communication is a direct communication amongst two closely located UE without passing over the base station (eNodeB/eNB) or the central network Evolved Packet Core (EPC). The communication occurs on a Side Link (SL) or PCS interface between the two devices [25]. D2D communication promises three kinds of benefits: (1) the proximity of UE; this permits enhanced bit rates, lower delays, and less power consumption; (2) reuse gain; radio resources can be used by cellular and D2D links at the same time; (3) hop gain; there is one link in the D2D mode instead of the Uplink (UL) and Downlink (DL) when communicating through eNodeB [26].  
Based on the coverage of the LTE network, D2D communication can occur in three ways: within network coverage, outside network coverage, and partial network coverage. In the partial network coverage scenario, the in-coverage UE acts as a relay between the out-of-coverage UE and eNodeB, resulting in an increase in the coverage area of eNodeB [27]. Figure 2a,b and c shows the communication scenarios for D2D.  
Release 14 of 3 GPP reveals that vehicles can communicate with other vehicles based on the principles of D2D communication. Figure 2b represents Mode 4 communication in Release 14, wherein vehicles can manage and select resources from available resources without the involvement of the core cellular network [12].  
Figure 2: D2D communication scenarios: (**a**) within network coverage, (**b**) outside network coverage, and (**c**) partial network coverage.
## 2 Results and Discussion  
### Gas Chromatography-Mass Spectrometry Analysis  
Phenols and flavonoids are frequently recorded bioactive phytochemicals that may potentially be used to develop novel active drugs [21]. In plants, the possession of those phytochemicals is attributed to the biological activities [22]. Similarly, the presence of these phytochemicals along with other chemical compositions in the honey depends on various factors, ranging from the source of the flower to geography and entomology [23]. Phytochemicals are known to be significant components in ensuring plant defense against multiple factors, ranging from microorganisms, environmental stress as well as to inter-species protections. These phytochemicals have been incorporated in therapeutic works for ages [24]. Hence, the initial step in this study was to screen for potentially active chemical compounds in MH. This was achieved by GC-MS analysis (Figure 1) to highlight the main bioactive compounds within the tested MH sample (Table 1). Overall, GC-MS analysis has revealed the presence of 37 different phytoconstituents belonging to various chemical classes were tentatively identified. Compounds which were identified in larger percentages within the tested MH sample are as follows: benzofuranone (16%), methylethylamino thiazole (10%), propenal nitrophenyl (3.7 %), heptyl caprylate (3.3%) and furan-2-yl ethan 1-amine (3.2 %). The remaining compounds constitute of 63% of the tested MH, each of which is present with less than 3%. These different chemical constituents belong to diverse, yet important classes of phytochemicals, such as phenol, sesquiterpenenes, fatty acids, methyl esters and others. The presence of varied bioactive components in the MH sample might justify its use for the treatment of numerous diseases [25]. For example, Benzofuran derivatives are well known for its multiple biological activities, including anticancer, antibacterial and antioxidation [26]. Methylethylamine thiazole has also shown to have an antibacterial activity that has shown high level of interaction with bacterial proteins [27]. Furthermore, the geographical location from which the honey is originated plays a major role in its composition. The climatic conditions, such as humidity, sunlight, chemical nature of soil and other factors would determine the concentration of phytochemicals, including carbohydrates, phenolic compounds and volatile compounds in the honey. Thus, it is very logical to assume that the components of any honey are different, depending on the place of origin. In part, this may also be attributed mostly to the difference in compositions of pollen or nectar that influences the chemical composition of the honey [28; 29].
## 3 Methods
### Remote Sensing Data
#### 3.2.1 ALOS-2 PALSAR-2 Radar Imagery  
Radar imagery was collected by the PALSAR-2 sensor onboard the Advanced Land Observing Satellite 2 (ALOS-2) platform. PALSAR-2 (L-band, \(\sim\)23 cm wavelength) images were recorded in Fine Beam Dual (FBD) mode, meaning that the sensor transmitted the signals horizontally and received them both horizontally (HH, known as co-polarization) and vertically (HV, known as cross-polarization). These data were collected at high resolution (\(10\times 10\) m).  
PALSAR-2 imagery was captured on two dates, 18 May 2018 and 13 July 2018. The imagery was collected in ascending orbit at 28.6\({}^{\circ}\) (all incident angles given apply at the center of the scene but vary across the extent of the scene). Only one frame was required to cover the entire extent of the study area. Images were calibrated to sigma-naught. We used a \(3\times 3\) median filter to account for speckle, the coherent addition of backscatter from multiple scatterers in the same resolution cell, which is inherent to all SAR imagery [27; 28].
## 6 Open Challenges, Future Prospects and Research Directions
### Open Challenges
#### 6.3.2 Integration with Emerging Technologies  
The integration of FSO communication with emerging technologies like Li-Fi and 5G/6G networks presents exciting opportunities and challenges. This involves exploring the seamless integration of FSO with Li-Fi to enable high-speed, high-capacity indoor and outdoor communication networks. This includes addressing handover issues between indoor Li-Fi and outdoor FSO links [201]. Similarly, it entails investigating the role of FSO in supporting ultra-high-speed data rates and low-latency communication in 5G and future 6G networks. This involves the development of protocols and standards for FSO integration with existing and future wireless networks. This will facilitate the development of FSO-based solutions to support the growing demand for high-capacity, low-latency communication in IoT applications and smart city infrastructures [378; 493].
## 4 System Model
### Task Model  
There are \(n\) number of tasks arriving dynamically from different users and the set of the task is represented by \(T=\{t_{1},t_{2},\ldots,t_{n}\}\). Each task \(t_{i}\) in \(T\) is having four attributes; \(t_{i}=\{T_{\text{id}},AT_{i},TS_{i},DL_{i}\}\), where \(T_{\text{id}}\) is the unique task ID, \(AT_{i}\) represents the arrival time of the \(i\)-th task, and \(TS_{i}\) is the task size (in terms of Million Instruction(MI)) and \(DL_{i}\) is the deadline of the \(i\)-th task [46]. Since the tasks are heterogeneous, the expected execution time \(ET_{\text{ikj}}\) of the \(i\)-th task on the \(k\)-th container of the \(j\)-th server can be calculated as  
\[ET_{\text{ikj}}=\frac{TS_{i}}{ProC_{\text{kj}}} \tag{1}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbols** & **Description** \\ \hline \(S\) & Set of servers \\ \(s_{\text{i}}\) & \(j\)th server \\ \(V\) & Set of virtual machine \\ \(v_{l}\) & \(l\)th virtual machine \\ \(C\) & Container \\ \(C_{\text{kj}}\) & \(k\)th container on \(j\)th server \\ \(MemS_{\text{kj}}\) & Memory size of \(k\)th container on \(j\)th server \\ \(ProC_{\text{kj}}\) & Processing speed of \(k\)th container on \(j\)th server \\ \(T\) & Set of tasks \\ \(t_{i}\) & \(i\)th task \\ \(T_{\text{id}}\) & Unique task ID \\ \(AT_{i}\) & Arrival time of \(i\)th task \\ \(TS_{i}\) & Size of \(i\)th task in terms of Million \\ Instruction(MI) \\ \(DL_{i}\) & Deadline of \(i\)th task \\ \(ET_{\text{ikj}}\) & Expected execution time of \(i\)th task on \(k\)th container of \(j\)th server \\ \(CT_{\text{i}}\) & Expected completion time of \(i\)th task \\ \(CT_{\text{ikj}}\) & Expected completion time of \(i\)th task on \(k\)th container of \(j\)th server \\ \(WT_{i}\) & Total waiting time of the \(i\)th task \\ \(MS\) & Makespan \\ \(L_{\text{var}}\) & Variance of load factor of all the servers \\ \(L_{j}\) & Load factor of \(j\)th server \\ \(\mu\) & Mean of load factors of all the servers \\ \hline \hline \end{tabular}
\end{table}
Table 4: Description of Symbols Used.  
The expected completion time \(CT_{\text{libj}}\) of the \(i\)-th task on the \(k\)-th container of the \(j\)-th server can be calculated as  
\[CT_{\text{libj}}=WT_{i}+ET_{\text{libj}} \tag{2}\]  
where \(WT_{i}\) is the total waiting time of the \(i\)-th task.  
The total time taken by all the tasks to complete their execution is called makespan (MS) and is calculated as  
\[MS=\sum_{i=1}^{n}CT_{i} \tag{3}\]  
The objective is to minimize \(MS\) as much as possible.  
The load distribution among the servers is evaluated by calculating the variance of the load factor of each server.  
\[L_{\text{var}}=\frac{\sum_{i=1}^{m}(L_{j}-\mu)^{2}}{m} \tag{4}\]  
where, \(L_{\text{var}}\) is the variance of load factor of all the servers, \(L_{j}\) is the load factor of the \(j\)-th server and \(\mu\) is the mean of load factors of all the servers and \(m\) is the total number of servers.
## Overcoming the Challenges of BeiDou Receiver Implementation  
**[PERSON] *, [PERSON], [PERSON], [PERSON] and [PERSON]**  
Department of Navigation and Positioning, Finnish Geodetic Institute, 02430 Kirkkonummi, Finland; E-Mails: [EMAIL_ADDRESS] (S.S.); [EMAIL_ADDRESS] (S.T.);  
[EMAIL_ADDRESS] (L.R.); [EMAIL_ADDRESS] (H.K.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +358-50-3726-569; Fax: +358-92-9555-211.  
External Editor: [PERSON]  
_Received: 26 August 2014; in revised form: 26 September 2014 / Accepted: 12 November 2014 / Published: 21 November 2014_  
**Abstract:** Global Navigation Satellite System (GNSS)-based positioning is experiencing rapid changes. The existing GPS and the GLONASS systems are being modernized to better serve the current challenging applications under harsh signal conditions. These modernizations include increasing the number of transmission frequencies and changes to the signal components. In addition, the Chinese BeiDou Navigation Satellite system (BDS) and the European Galileo are currently under development for global operation. Therefore, in view of these new upcoming systems the research and development of GNSS receivers has been experiencing a new upsurge. In this article, the authors discuss the main functionalities of a GNSS receiver in view of BDS. While describing the main functionalities of a software-defined BeiDou receiver, the authors also highlight the similarities and differences between the signal characteristics of the BeiDou B1 open service signal and the legacy GPS L1 C/A signal, as in general they both exhibit similar characteristics. In addition, the authors implement a novel acquisition technique for long coherent integration in the presence of NH code modulation in BeiDou D1 signal. Furthermore, a simple phase-preserved coherent integration based acquisition scheme is implemented for BeiDou GEO satellite acquisition. Apart from the above BeiDou-specific implementations, a novel Carrier-to-Noise-density ratio estimation technique is also implemented in the software receiver, which does not necessarily require bit synchronization prior to estimation. Finally, the authors present a BeiDou-only position fix with the implemented software-definedBeiDou receiver considering all three satellite constellations from BDS. In addition, a true multi-GNSS position fix with GPS and BDS systems is also presented while comparing their performances for a static stand-alone code phase-based positioning.  
Keywords:BeiDou Navigation Satellite system; acquisition; tracking; software-defined receiver; multi-GNSS
# Comparison of L1 and L5 Bands GNSS Signals Acquisition
## 4 Application to GPS and Galileo Signals
### Comparison of GPS L1 C/A and L5 Signals Acquisition
#### 4.2.1 Determination of Acquisition Parameters
Consequently, the same number of frequency bins is considered as simplification, keeping in mind that for these two cases the performance will be a little bit optimistic compared to the real one. Note also that another approximation is made, namely that a Gaussian variable is considered for the detection test. This is the case when the number of non-coherent accumulations is high, however, when there is not or a just few non-coherent accumulations, the variable is not Gaussian, which would affect the way the probabilities of false alarm and detection are calculated [8]. Now that the required SNR is determined, the coherent and total integration times can be estimated following Figure 11, and the values are summarized in Tables 5 and 6, considering the average and worst case regarding the implementation losses (quantization, frequency step, and code step), namely \(0.55+0.53+1.16=2.24\) dB and \(0.55+1.65+2.50=4.70\) dB respectively. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Context** & **Signal** & \(T_{\text{C}}\) & \(N_{\text{FB}}\) & \(N_{\text{CB}}\) & \(N_{\text{CELL}}\) & \(P_{\text{FA,C}}\) & **SNR** \\ \hline no & L1 C/A & 20 ms & 300 & 2048 & 614,400 & \(1.63\times 10^{-9}\) & 17.15 dB \\ assistance & L5 & 20 ms & 225 & 409,600 & 92,160,000 & \(1.09\times 10^{-11}\) & 18.04 dB \\  & L5 & unlimited & 225 & 409,600 & 92,160,000 & \(1.09\times 10^{-11}\) & 18.04 dB \\ \hline  & L1 C/A & 20 ms & 16 & 2048 & 32,768 & \(3.05\times 10^{-8}\) & 16.52 dB \\ assistance & L5 & 20 ms & 12 & 20,480 & 245,760 & \(4.07\times 10^{-9}\) & 16.96 dB \\  & L5 & unlimited & 12 & 20,480 & 245,760 & \(4.07\times 10^{-9}\) & 16.96 dB \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Search space and statistical parameters for \(P_{\text{FA,G}}=10^{-3}\) and \(P_{\text{D}}=0.9\) for different contexts with GPS signals. Before going on the evaluation of the different implementations, Table 7 shows the ratio of the total integration time between several cases to quantify the impact of using the L5 signal, unlimited coherent integration time, and assistance. From Table 7, it is clear that the main way to reduce the total integration time is to increase the coherent integration time, especially for very weak signals. Then, the use of L5 brings an interesting reduction, thanks to its higher power, even if the required SNR after correlation is higher due to the bigger search space. Finally, the assistance has the lowest effect on the total integration time, but the reduction is still appreciable (and remember that the assistance reduces the frequency search space, which will have a significant positive impact on the acquisition time).
# Impact of CoAP and MQTT on NB-IoT System Performance +
## 2 Performance Evaluation
### Traffic Modeling  
Sensor reports were randomly generated by means of a Poisson process. For TCP, the session is terminated after each sensor report was received in the other end of the connection, however, the termination procedure of a TCP connection has not been explicitly modelled. Sensors are distributed uniformly around the simulation area. When a new sensor report is to be sent a UE is created to the system to transmit the report. When a UE arrives to the system, it immediately tries to establish a RRC connection and transmit its application layer data. After successful transmission of the data the UE is removed from the system after a short, fixed delay to make sure all data transfer has ended on all layers.
# A Robust Multi-Watermarking Algorithm for Medical Images Based on DTCWT-DCT and Henon Map
## 3 The Proposed Algorithm
### Watermarks Pretreatment  
Before embedding the watermark, henon chaos must be applied, and a preprocessing operation is carried out to obtain the watermark of chaotic encryption. Figure 9 shows the steps of watermarks pretreatment.  
* Step 1: Choose the initial values \(x_{0},x_{1},x_{2}\) to create chaotic sequences \(X_{n}(j)\), and get binary encryption matrixes \(C_{n}(i,j)\) via symbolic operations. Instead the values of \(X_{n}(j)\) with '1' if they are bigger than the average, and instead with '0' if smaller.
* Step 2: Obtain the encrypted watermarks \(BW_{n}(i,j)\) by operating the binary watermarks \(W_{n}(i,j)\) and the binary encryption matrixes \(C_{n}(i,j)\) through the hash function.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Image** & **Brain** & **Elbow** & **Foot** & **Knee** & **Internal Auditory Canal** & **Neck** & **Shoulder** & **Orbits** \\ \hline
**Brain** & 1.00 & 0.13 & 0.25 & \(-\)0.07 & 0.06 & \(-\)0.04 & 0.45 & 0.31 \\
**Elbow** & 0.13 & 1.00 & 0.25 & \(-\)0.19 & 0.44 & 0.19 & 0.06 & 0.06 \\
**Foot** & 0.25 & 0.25 & 1.00 & 0.19 & 0.06 & 0.32 & 0.19 & 0.19 \\
**Knee** & \(-\)0.07 & \(-\)0.19 & 0.19 & 1.00 & \(-\)0.25 & 0.27 & 0.25 & \(-\)0.00 \\
**Internal auditory canal** & 0.06 & 0.44 & 0.06 & \(-\)0.25 & 1.00 & 0.27 & 0.00 & \(-\)0.25 \\
**Neck** & \(-\)0.04 & 0.19 & 0.32 & 0.27 & 0.27 & 1.00 & 0.37 & \(-\)0.24 \\
**Shoulder** & 0.45 & 0.06 & 0.19 & 0.25 & 0.00 & 0.37 & 1.00 & \(-\)0.25 \\
**Orbits** & 0.31 & 0.06 & 0.19 & \(-\)0.00 & \(-\)0.25 & \(-\)0.24 & \(-\)0.25 & 1.00 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Values of the correlation coefficients between different images (32 bit).  
Figure 8: Some tested images: (**a**) Brain; (**b**) Elbow; (**c**) Foot; (**d**) Knee; (**e**) Internal auditory canal; (**f**) Neck; (**g**) Shoulder; (**h**) Orbits.
## 3 Results
### System Performance Evaluation
#### 3.1.2. Ventilation and Smoke Extraction Efficiency  
Effective removal of smoke and toxic gases is critical during the early stages of a fire, as visibility and air quality directly affect occupant evacuation and response coordination. The proposed system integrates motorized ceiling diffusers with an energy recovery ventilation (ERV) unit to establish a rapid and directional smoke extraction pathway immediately after fire detection.  
In experimental trials, the average activation time of the motorized diffusers--measured from the moment of fire detection to full vane rotation--was 1.8 s, enabling fast airflow redirection with minimal mechanical delay. Once activated, the ERV operated in maximum extraction mode, achieving a volumetric flow rate of up to 550 cubic meters per hour (CMH) per zone.  
Smoke opacity was monitored using optical density (OD) sensors placed at occupant height (1.5 m) and ceiling level. The system achieved 90% smoke clearance--defined as a drop below 10% OD--within 88 to 95 s, depending on the fire load and zone geometry. These results indicate a substantial improvement compared to passive ventilation or delayed sprinkler-triggered ventilation systems, which typically require over 180 s for equivalent clearance [56; 57].  
The system also maintained consistent directional flow through controlled diffuser zoning, avoiding undesirable backflow or pressure imbalances between adjacent areas. This contributed to maintaining breathable air pockets for evacuation routes and preventing smoke recirculation.  
Additionally, the ERV's integrated heat exchanger maintained thermal balance in adjacent zones, preventing abrupt HVAC disruption and supporting energy stability at the building level following the event. These findings confirm the system's capability to provide rapid, energy-efficient, and spatially targeted smoke ventilation during fire scenarios. This operational effectiveness is directly attributed to the system's structural integration, as illustrated in the exploded and cross-sectional views in Figure 2 (see Section 2.4, for structural details).