esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Analysis and Simulation Results
### Energy Consumption and Network Lifetime
Unlike the fixed duty cycle strategy, although the energy consumption in different regions has a certain fluctuation, it is maintained within a certain range. Therefore, compared with the fixed duty cycle strategy, the PNLDC approach can make use of network energy more effectively. And the energy consumption curves of the dynamic duty cycle strategy are different from those of the other two strategies. For the sensor nodes in the hotspots, the energy consumption of the dynamic duty cycle strategy is only slightly lower than that of the PNLDC approach. For the nodes in the non-hotspots, the energy utilization of the dynamic duty cycle strategy is obviously lower than that of the PNLDC approach. This is because the duty cycle in the dynamic duty cycle strategy varies according to the traffic load in the network [50], that is, the duty cycle is large when sensors have high traffic loading. Therefore, in the non-hotspots far away from the sink, the duty cycle in the dynamic duty cycle strategy is relatively small. In contrast, in the PNLDC approach, partial nodes in the region far away from the sink are selected as the nodes with a full duty cycle. Thus, for the region far away from the sink, the PNLDC approach can make better use of energy than the dynamic duty cycle strategy. The network lifetime is defined as the time that the first node in the network dies out due to energy exhaustion, which is closely related to the initial energy and the maximum of energy consumption. Suppose that the initial energy in the network is \(E_{ini}\), the network lifetime is a result of the initial energy divided by the average energy consumed in a cycle, which can be expressed as follows:  
\[N=\frac{(\tau+1)\cdot\tau r}{2x}+\tau+1 \tag{17}\]1  
where \(\mathbb{E}_{T}^{x}=\varepsilon_{T\uparrow\uparrow}^{x}\mathbb{E}_{T}^{x}\), \(\mathbb{E}_{R}^{x}=\varepsilon_{R}^{x}\mathbb{E}_{R}^{x}\), \(\mathbb{E}_{SEN}^{x}=\mathcal{T}_{SEN}\mathbb{E}_{SEN}^{x}+\mu_{S}\), and \(\mathbb{E}_{LPL}^{x}=\mathcal{T}_{COM}\Big{[}\delta_{COM}\mu_{R}-\frac{(1- \delta_{COM})\mu_{S}}{2}-\varepsilon_{T}^{x}\Big{]}-(\mathcal{T}_{B}+\mathcal{ T}_{\Lambda C})\mu_{S}-\mathcal{T}_{B}\mu_{R}\). Figure 17 shows the network lifetime of the PNLDC approach, the dynamic duty cycle strategy, and the fixed duty cycle strategy under three values of transmission range. As can be seen in the figure, the network lifetime increases with the increase of the transmission range. Compared with the fixed duty cycle strategy and dynamic duty cycle strategy, the network lifetime is not reduced in the  
\[N=\frac{(\tau+1)\cdot\tau r}{2x}+\tau+1 \tag{17}\]2 The energy consumption under different distance. \[N=\frac{(\tau+1)\cdot\tau r}{2x}+\tau+1 \tag{17}\]3 The energy consumption under different distance. PNLDC approach. This is because the energy consumed by nodes with the duty cycle of 1 is derived from the residual energy of nodes in the network. In combination with the above analysis, the PNLDC approach can increase the utilization of energy without reducing the network lifetime.
# Evaluation of IEEE802.15.4g for Environmental Observations
## 2 Materials and Methods
### Hardware  
This section describes the hardware used in the range measurement campaign. We used 4 nodes, 1 configured as a TX (transmitter) and 3 others as RX (receiver). Each node is mounted on a 1.8 m PVC tube. From [PERSON] et al. [1] and [PERSON] et al. [4], we see their choice of putting their sensors at a height of 4 m on top of fixed wooden poles, reducing the impact the ground poses as an obstacle to the radio links. In our case, 1.8 m tubes reach the same height at which smart meters are located in a house. Therefore, the impact of the ground being an obstacle (e.g., Fresnel Zone obstruction) is a condition nodes have to cope with.
## Materials and Methods  
### Materials and Characterization  
The biomass materials investigated were a forest residue (FOR) and pincones (PCO) collected from local forests in West Crete and refused derived fuel (RDF) provided from a solid waste management enterprise in the Chania area of Crete. The RDF sample was free of glass and metals, which were removed by industrial crushing, sieving and magnetic separation prior to being pelletized. Its average composition was 49% organic materials, 31% paper, 12% leather and wood waste and 8% inert and other materials. After air drying, materials were separated in a mechanical splitter and ground in a Fritsch Pulverizette 15 cutting mill to a final particle size of less than 1 mm. Representative samples were ground to a particle size of less than 250 um and characterized according to the European Standard CEN/TC335 [35] in terms of proximate analysis (using programmable laboratory furnaces), ultimate analysis (using a Flash 2000, Thermo-Scientific, MA, USA, CHNS analyzer) and higher heating value (using an AC-300, Leco, MI, USA, calorimeter).  
Chemical analysis of the ash to identify its major inorganic elements was conducted using a S2 Ranger/EDS (Bruker AXS, Karlsruhe, Germany) X-ray fluorescence spectrometer (XRF). In order to predict the ash deposition tendency in boilers during the utilization of the waste materials studied for energy production, the following slagging and fouling indices were calculated based on their wide acceptance for decision-making when combined with pilot plant testing.  
The alkali index (AI) expresses the amount of alkali oxides in the sample per unit of energy [36]:  
\[\text{AI}=\text{kg}\;(\text{K}_{2}\text{O}+\text{Na}_{2}\text{O})/\text{GJ} \tag{1}\]  
When AI \(>\) 0.34 kg/GJ, deposition tendency is high, while when AI = 0.17-0.34 kg/GJ, deposition tendency is low.  
The base-to-acid ratio (B/A) is defined as:  
\[\text{B/A}=(\text{Fe}_{2}\text{O}_{3}+\text{CaO}+\text{MgO}+\text{K}_{2}\text {O}+\text{Na}_{2}\text{O})/(\text{SiO}_{2}+\text{TiO}_{2}+\text{Al}_{2}\text {O}_{3}) \tag{2}\]  
When B/A \(>\) 0.5, deposition tendency is high; when 0.5 \(<\) B/A \(<\) 1, deposition tendency is medium; while when B/A \(<\) 0.5, deposition tendency is low.  
The Babcock index (Rs) is expressed as [37]:  
\[\text{Rs}=(\text{B/A})\,\text{S} \tag{3}\]When Rs \(>\) 2, deposition tendency is high; when 0.6 \(<\) Rs \(<\) 2, deposition tendency is medium; while when Rs \(<\) 0.6, deposition tendency is low.  
The slag viscosity index (Sv) is calculated by the equation:  
\[\mathrm{Sv}=\mathrm{SiO_{2}}\times 100/(\mathrm{SiO_{2}}+\mathrm{Fe_{2}O_{3}}+ \mathrm{CaO}+\mathrm{MgO}) \tag{4}\]  
If Sv \(<\) 65, slagging tendency is high; if 65 \(<\) Sv \(<\) 72, slagging tendency is medium; while if Sv \(>\) 72, slagging tendency is low.
## 2 Materials and Methods
### Machine-Learning Approaches
#### 2.4.3 K-Nearest Neighbors (KNN)  
K-Nearest Neighbors (KNN) represents a nonparametric classification method used in classification and regression. This algorithm performs classification based on distance, thus its function is only locally approximated and computation is postponed until function evaluation. If the analyzed units have different physical characteristics, it is recommended to normalize the data in order to improve its accuracy [43].  
In our study, the classification is computed, and a new object is classified according to the votes of its neighbors. The 20 mean MFCCs extracted are used in this approach, and for this reason, it is employed as a 20-dimension classifier. This algorithm provides high accuracy for problems with unknown distributions [44].  
The Principal Component Analysis (PCA) is computed to evaluate whether the noise is introduced and determine if it can be reduced. For model generation, a Grid Search is performed. For calculating a model with KNN, Scikit learn, an easy-to-use and efficient library from python, was selected.  
The dataset was split into two parts for all three methods: the train set (80%) and the test set (20%).
## 2 Quality Assessment of COSMIC-2 GPS Observations
### Multipath Effect  
In the process of satellite signal propagation, due to the influence of the surrounding environment, the signal of the surrounding reflectors will enter the receiver with the satellite signal, resulting in a deviation between the observed value and the true value, this phenomenon is called multipath effect [30; 31; 32]. The multipath error is mainly due to pseudo-ranges and can be determined by the linear combination of pseudo-range and phase observations, and its calculation formula is:  
\[MP1=P_{1}-(1+\frac{2}{\alpha-1})\varphi_{1}+(\frac{2}{\alpha-1})\varphi_{2} \tag{2}\]  
\[MP2=P_{2}-(\frac{2\alpha}{\alpha-1})\varphi_{1}+(\frac{2}{\alpha-1}-1)\varphi_{2} \tag{3}\]  
where \(MP1\) and \(MP2\) are multipath errors on L1 and L2 bands respectively, \(\alpha=\frac{f_{2}^{2}}{f_{2}^{2}}\) (\(f_{1}\) and \(f_{2}\) are the frequency values of L1 and L2 bands respectively), \(P_{1}\), \(P_{2}\) are pseudo-range observations and \(\varphi_{1}\), \(\varphi_{2}\) are the phase observations on both frequencies, respectively.  
Figure 4: Mean SNR of six COSMIC-2 satellites during DOY 138–142, 2021.  
Figure 3: Mean SNR of each COSMIC-2 satellite during DOY 138–142, 2021.  
The thresholds of RMS for \(MP1\) and \(MP2\) in TEQC are set as 0.5 m and 0.75 m respectively, and the smaller RMS indicates that the observed data are less affected by multipath effects [28; 33]. The mean multipath errors of every COSMIC-2 satellite are plotted every day in Figure 5, and the mean multipath error of six COSMIC-2 satellites every day over 5 days is shown in Figure 6.  
As can be seen from Figures 5 and 6, the RMS of \(MP1\) and \(MP2\) of these satellites are within the index value range. Among them, the RMS of \(MP1\) is generally less than \(MP2\), except for Sat-5 in DOY 141, but it is still within the limit, so it has little impact on data quality.
## 3 Simulation Analysis of the Formation Mechanism and Evolution Process of Frost-Heave Force  
### Effect of Excretion Conditions on Frost-Heave Force  
In order to explore the freezing process of the void water behind the lining and the formation mechanism of frost-heave force, a frost-heave test of void water behind the lining was carried out. The test model was composed of two concrete specimens with dimensions of 0.4 m \(\times\) 0.4 m \(\times\) 0.2 m, and the size of the hollow cavity was set to 0.2 m \(\times\) 0.2 m \(\times\) 0.1 m, as shown in Figure 3. A temperature sensor was embedded in the model to monitor whether the water in the cavity was frozen; a film pressure sensor was also installed to test the frost-heave force.  
We set the test conditions according to the tunnel void form as follows: Working condition 1: during the freezing process of type I voids, the water supply and discharge channels do not freeze. Working condition 2: during the freezing process of type II voids, the water supply and drainage channels are frozen first, and the water body in the model freezes later, as shown in Figure 4.  
Figure 2: Zhishan Tunnel entrance temperature fitting.  
During the experiment, the top surface and four sides of the specimen were wrapped with insulation boards, leaving the bottom surface exposed to cold air. For working condition 1, the entire experimental process ensured that the drainage channel did not freeze and only the water in the cavity froze. For working condition 2, the drainage channel was frozen first, and then the water in the cavity was frozen.  
By freezing the bottom of the specimen, it was found that, in working condition 1, the freezing process of the water body in the cavity developed from the bottom upward, and the frost-heave force in the cavity was almost zero; in working condition 2, the freezing process of the water body in the cavity also developed upward from the bottom of the cavity, and a large frost-heave force was generated in the cavity, with the average pressure reaching 0.16 MPa.  
Numerical Simulation of Frost-Heave of Voided Water behind the Lining under Short-Period Temperature Fluctuation Conditions  
We used FLUENT simulation software based on the solidification/melting model to analyze the freezing process of the void water behind the lining under the condition of short-period fluctuations in the lining's surface temperature. Considering that voids often occur in tunnel vaults, a model was established using vault voids as an example. Since voids often occur at the highest point of each mold lining near the end of the formwork, the void cavities formed by concrete pouring are mostly round wedge-shaped and flying-saucer-shaped, and we established circular wedge-shaped and flying-saucer-shaped void models.  
Figure 4: Void water accumulation and frost-heave test conditions.  
Figure 3: Frost-heave test model for water accumulation in voids.
# Analysis and Experimental Validation of Efficient Coded OFDM for an Impulsive Noise Environment
## 5 Performance Analysis
### Parameters Setting
#### 5.1.1 Transmit Power  
With the USRP, it is not possible to set the transmit power directly as a parameter. It can be configured via the usrp hardware driver, \(UHD\ gain\). The output power depends on other parameters such as the frequency, the Total Harmonic Distortion (THD), which corresponds to the level of unwanted harmonics in a sinusoidal waveform. To have an approximate value of the output power, it can be measured by using a spectrum analyzer. For this purpose, in [37], the authors proposed radio frequency measurements on an SBX daughter-board. In this paper, we can notice that the output power decreases with the increasing of the carrier frequency. The UHD gain is an important parameter; nonetheless, its increment has to be done carefully. Since in our test, we use a frequency of 2.4 GHz with a UHD gain of 20 dB, the corresponding output power is equal to 12.05 dBm.
## 2 Materials and Methods
### Evaluation Metrics
#### 2.7.1 Number of Detected Trees  
All inferred trees were validated through the spatial relationship between their limits and the borders of their respective ground truth by the Intersection of Union (IoU) metric (Figure 7). IoU corresponds to the ratio between the areas of the intersection of the corresponding pair (ground truth and inference) by union. The higher the conformity of the tracings of the two masks, the closer to 1 is the IoU value. A sample was considered true positive (TP) when IoU > 0.5, and to guarantee the integrity of the validation, each reference was used only once when comparing with the inferred canopies.  
Hence, three possible results were considered: (1) TP, when the tree was correctly identified; (2) false negatives (FN), when an omission error occurred, i.e., an existing plant was not identified; and (3) false positives (FP), when an inferred mask did not correspond to an existing plant, resulting in a commission error.  
From these results, the metrics precision_det (Pdet), recall_det (Rdet), F1-score (Fdet), and overall accuracy (OA) were computed for each of the two models using the following equations:  
\[\text{Precision\_det}\;(\text{Pdet})=\frac{TP}{TP+FP}*100 \tag{2}\]  
\[\text{Recall\_det}\;(\text{Rdet})=\frac{TP}{TP+FN}*100 \tag{3}\]  
\[\text{F1}-\text{score\_det}\;(\text{Fdet})=\frac{2*Pdet*Rdet}{Pdet+Rdet} \tag{4}\]  
\[\text{Overall Accuracy}\;(\text{OA})=\frac{TP}{TP+FN+FP}*100 \tag{5}\]
## 3 Experimental Results and Discussion
### Influences of Mix Proportions on Slump of the Gangue-Based Shotcrete Material
#### 3.2.3 Influence of the Sand Content  
Table 5 presents the peak compressive strengths of gangue-based shotcrete specimens under schemes C1\(\sim\)C4 with curing periods of 1 d, 3 d, 7 d, 14 d, and 28 d. According to these data, changes in the peak compressive strength of specimens under schemes C1\(\sim\)C4 after various curing periods were plotted, as displayed in Figure 11.  
Table 5 and Figure 11 demonstrate that the compressive strengths of specimens under schemes C1\(\sim\)C4 also increase with prolonged curing, which is consistent with changes under schemes A1\(\sim\)A4 and B1\(\sim\)B4. Additionally, when the curing period remains fixed, the compressive strengths of specimens gradually increase with sand content under schemes C1\(\sim\)C3. However, if the sand content is increased further, as per scheme C4, the compressive strength decreases significantly.  
Specimens cured under scheme C3 exhibit the highest compressive strength across all curing periods. For instance, when the curing period is 28 d, the peak compressive strength of specimens under scheme C3 is 3.89 MPa. This is 1.96, 1.13, and 1.49 times higher than those under schemes C1, C2, and C4, respectively. The fine aggregate, sand, contributes to the bearing capacity of the shotcrete material. Therefore, as increase in sand content results in an increase in the compressive strength of specimens. However, excessive sand use results in loose specimens during the initial stage, leading to a decrease in strength.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Peak Compressive** & **1 d** & **3 d** & **7 d** & **14 d** & **28 d** \\
**Strength/MPa** & & & & & \\ \hline C1 & 0.34 & 0.63 & 0.95 & 1.48 & 1.98 \\ C2 & 0.49 & 0.89 & 1.69 & 2.5 & 3.45 \\ C3 & 0.56 & 1.11 & 2.24 & 3.56 & 3.89 \\ C4 & 0.47 & 0.66 & 1.31 & 2.11 & 2.61 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Peak compressive strengths of gangue-based shotcrete specimens under schemes C1\(\sim\)C4 with different curing periods.  
Additionally, a forms voids in the specimens, causing the cracking and deformation of the specimens, which further reduces their strength.  
In addition, sand alone lacks the ability to form cement, requiring external cementing substances to ensure the strength and ease of use of the shotcrete. The amount of cementing substances required increases with higher sand content. The sand content and cement dosage are closely linked, similarly to the effect of gangue size on the strength of the specimens. When selecting sand size for on-site shotcrete, it is important to note that the sand size is negatively correlated with the cement dosage within the limits of the regulations.
# Resolving Persistent Packet Collisions through Broadcast Feedback in Cellular V2X Communication +
## 3 Broadcast Feedback on Packet Collisions
### Feedback Operation
#### 3.3.1 Feedback Provider Side  
When a vehicle is not transmitting, it monitors every resource to decode the packet therein. It uses Algorithm 1 to check for potential collisions, for which it registers the resource location in the feedback queue. When the vehicle transmits, however, it executesAlgorithm 2 shown below instead of Algorithm 1. Namely, it dequeues the first entry of the feedback queue (line 6) and includes it in the packet to transmit (line 7). Then, the feedback queue is flushed (line 8).  
Figure 5 exemplifies how Algorithms 1 and 2 operate in terms of the feedback queue management. In the example, the ego vehicle has detected and registered seven collision resource locations (Algorithm 1, line 20) from \(A\) to \(G\) since its last transmission (a). While receiving other vehicles' transmissions, it examines the piggybacked collision feedback information and removes the collision resource locations in its queue that are reported by other vehicles first (Algorithm 1, line 14) (b). For instance, resource locations \(A\), \(C\), \(D\), and \(F\) are reported by other vehicles, so they do not have to be reported again by this vehicle. This results in the vehicle knocking them out. Immediately before the transmission by this vehicle, there are three remaining locations, \(B\), \(E\), and \(G\), which have not been feedbacked in other vehicles' transmissions. Among these, it dequeues the first entry and piggybacks the feedback on its own data transmission (c) while flushing the entire feedback queue again (Algorithm 2) (d). The feedback and queue flushing operation repeats at each transmission by the ego vehicle.  
There are three reasons why the transmitter only broadcasts the oldest un-feedback collision event. First, in most cases, the collision feedback queue is short because the feedback operation is performed for every packet transmission by every vehicle. The simulation shows that a single feedback is usually enough to empty the queue after the purging is triggered by the other vehicles (Algorithm 1, line 14). Second, the flushed entries without feedback can have additional chances at other vehicles. For example, \(G\) and \(E\) that were not feedbacked by the ego vehicle can still be feedbacked by other vehicles that also observed these collision events. Finally, it is to minimize the overhead of feedback signaling, as there is inadequate space to piggyback a multiple of them in the transmitted packet. This issue is discussed in Section 3.4.  
Figure 5: Feedback queue management example: only \(B\) is reported as the collision resource.
# PECSA: Practical Edge Computing Service Architecture Applicable to Adaptive IoT-Based Applications
## 4 Materials and Methods
### Three Basic Service Types and the Associated Cost Evaluation Schemes
\[R=R_{T}\cap R_{S}\cap R_{P} \tag{5}\]  
\[S=S_{T}\cap S_{S}\cap S_{P} \tag{6}\]  
Service \(S\) matches requirements \(R\), and this is denoted as \(S\succeq R\), that is to say, the following expressions are valid:  
\[\left\{ \begin{aligned} R_{T}&\geq S_{T}\\ R_{P}&\geq S_{P}\\ R_{S}&\subseteq S_{S}\end{aligned} \right. \tag{7}\]  
The basic needs of security include confidentiality, integrity, availability, controllability, and auditability. For simplification, we only consider three security attributes, i.e., confidentiality, integrity, and non-repudiation, for \(S_{S}\) and \(R_{S}\); thus, \(S_{S}\) and \(R_{S}\) are \begin{table}
\begin{tabular}{c l} \hline \hline
**Symbol** & **Meaning** \\ \hline \(R\) & requirements of user \\ \(S\) & available services of service provider \\ \(R_{T}\) & time requirement of user \\ \(R_{S}\) & security requirement of user \\ \(R_{P}\) & price requirement of user \\ \(S_{T}\) & available service time of service provider \\ \(S_{S}\) & available security service of service provider \\ \(S_{P}\) & available service price of service provider \\ \(E_{t}^{max}\) & total battery energy of node \(i\) \\ \(T_{transmission}\) & data transmission time \\ \(T_{security}\) & security arrangement time, such as time of encryption, signature, etc. \\ \(T_{calculation}\) & calculation time \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of main notations. both subsets of set \(\{\text{Confidentiality, Integrity, and Non - repudiation}\}\). Confidentiality can be guaranteed by data encryption. Integrity and Non-repudiation can be ensured by a digital signature. The edge network's security capacity is required to cover the security demand of users. Let us suppose that \(R_{S}\) is a set of security requirements of a user; then, \(S_{S}\) must contain all the elements in \(R_{S}\) to satisfy the security requirements. For example, if \(R_{S}=\{Confidentiality, Integrity\}\), then \(S_{S}\) can be \(\{Confidentiality, Integrity\}\) or \(\{Confidentiality, Integrity,andNon-repudiation\}\). It should be noted that the validity judgement of the formula \(R_{S}\subseteq S_{S}\) should be ahead of the estimation of \(S_{T}\) and \(S_{P}\). When the formula \(R_{S}\subseteq S_{S}\) is not valid, the service process is stopped. When \(R_{S}\subseteq S_{S}\) is valid, the estimation of \(S_{T}\) and \(S_{P}\) is initiated. Below, we introduce three evaluation schemes for resource allocation.