esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Jet-Noise Source Model and Propagation
In analogy with similar physical consideration taken from the Stochastic Generation and Radiation model for turbulent Noise (SNGR) [16], the energy peak of the universal energy spectrum can be related to the turbulent length scale through a free mode parameter, according to  
\[L_{T}=\frac{s}{1.5}\cdot\frac{1}{\alpha} \tag{6}\]  
This equation puts in relation the turbulent scale \(L_{T}\) with the value of the curvilinear abscissa \(s\), and takes into account the experimental evidence according to which the energy peak has its maximum at a value 1.5 of the modified Strouhal number [14]. Equation (6) states that the turbulent scale grows up gradually increasing the distance from the jet nozzle, with sources emitting at lower frequencies at a greater distance from it. On the other hand, a classical result [17] relates the integral length scale \(L_{int}\) for homogeneous, isotropic turbulence with the correlation length \(L_{0}=\frac{2\pi}{k_{0}}\) of a vortex structure at a single wavenumber \(k_{0}\), according to the following equation:  
\[L_{int}=\frac{3}{8}L_{0} \tag{7}\]  
This equation implies then that the correlation length at each frequency is proportional to the wavelength. In the revised Eldred-based model, it would means that each single source is correlated with all the others with a correlation length that depends on the frequency considered. In order to enhance the capabilities of the revised model and in order to tune the model exploiting the experimental data, the parameter \(\beta\) is then added to the equation:  
\[L_{int}=\frac{3}{8}L_{0}\cdot\frac{1}{\beta} \tag{8}\]  
A comprehensive analysis concerning the role of \(\beta\) is provided in [14]. Both Eldred-based uncorrelated and correlated source models can be used for the hybrid empirical/BEM computation. The hybrid approach's implementation has firstly been tested to check that the total pressure, \(p\), is equal to the Eldred incident field, \(p_{inc}\), when a vanishing scattering BEM surface is considered (\(p_{sc}=0\)).
## 4 Results and Discussion
### Determination of the Optimal Operative Profiles
On the contrary, full-load operation remains the best operating mode for the vast majority of situations, in which larger amounts of thermal energy are requested rarely (in summer) or with a middle frequency from large multi-family buildings. In any case, full-load operation remains the safest choice for every scenario, since it guarantees the energy demands' fulfillment, providing basically always the smallest costs. Figure 14: Cost comparison between full-load (left column, purple and green) and partial-load operation (right column, blue and yellow) for different climate zones and building types, for a WWB typical day (winter, working day, cloudy). Figure 13: Cost comparison between full-load (left column, purple and green) and partial-load operation (right column, blue and yellow) for different climate zones and building types, for an SWX typical day (summer, working day, neutral weather). As a final example, in Figure 16, the complete operative profile of two typical days in two climate zones is reported. Figure 16a,b show the most economical solution to fulfill the demand of a two-living-unit building, in an intermediate season, working, cloudy day in Climate Zone 15, in full-load and partial-load mode, respectively. A similar comparison is shown in Figure 16c,d for a winter, weekend, cloudy day in Climate Zone 13. Together with the SP profile, shown with columns, the status of the thermal storage is also plotted for every time step. In both scenarios, it can be seen that full-load mode fulfills the thermal and electrical demands, indicated with \(P_{th}\) and \(P_{el}\), respectively, with a lower cost (10-15% less) and a lower number of starts/stops. This confirms the fact that it is generally not convenient to operate a CHP, MGT-based system in partial-load mode. Furthermore, a lower number of starts and stops might have the potential to spare some of the system components from alternating, thermo-mechanical fatigue. This aspect will be treated in a separate future work. Figure 16: _Cont._  
Figure 15: Cost comparison between full-load (left column, purple and green) and partial-load operation (right column, blue and yellow) for different climate zones and building types, for a WSB typical day (winter, weekend day, cloudy).
# TSCH Multiflow Scheduling with QoS Guarantees:
## 1 Introduction
### TSCH Schedulers and Protocols
#### 1.2.1 Autonomous  
One of the characteristics of autonomous TSCH schedulers is that they reuse the topology and routing information stored by the nodes to perform the scheduling. This information is obtained through the exchange of RPL control messages with their neighbours, Figure 2a. Therefore, no additional signalling is required for the formation of the different schedules and only the MAC address and updated topology information is needed. The most commonly used autonomous scheduler is Orchestra [5], due to its ease of configuration and high performance.  
In Orchestra, each node creates and maintains its own TSCH scheduling and updates it as changes occur in the network. In fact, they only use the RPL information received from neighbours and parents. An Orchestra schedule is composed of several Slotframes, each for a different type of traffic: Beacon messages (broadcast), RPL messages or Unicast messages (application traffic).  
Four types of slots are defined in Orchestra:  
* Common Shared Slot (CS): a common cell in all the nodes used for transmission and/or reception.
* Receiver-based Slot (RBS): there is one cell in reception and one cell in transmission for each neighbouring node, the position of the cell is calculated with the information from the Receiver.
* Sender-based Slot (SBS): there is one slot in transmission for each node and one in reception for the neighbours. The difference with the previous one is that the position of the cell is calculated with the information of the Sender.
* Sender-based Dedicated Orchestra Slots (SBD): this is a specific transmission slot for each neighbour, but in this case instead of using shared cells, a dedicated cell is used.  
On the other hand, the different slotframes that can make up the scheduling with Orchestra are:  
* EB Slotframe: this is a slotframe consisting of dedicated Sender-based Slots, and is for the transmission of EB messages.
* Broadcast Slotframe: this is a slotframe consisting of Common Shared Slots (shared cells), mainly for RPL traffic.
* Receiver-based Unicast Slotframe: for unicast traffic consisting of Receiver-based cells.
* Sender-based Unicast Slotframe: for unicast traffic consisting of Sender-based cells.  
Orchestra scheduling can be carried out in two different configurations, Receiver-based or Sender-based:  
1. Configuration Receiver-based: this consists of an EB Slotframe, a Broadcast Slotframe and Receiver-based Unicast Slotframe.
2. Configuration Sender-based: this consists of an EB Slotframe, a Broadcast Slotframe and a Sender-based Unicast Slotframe.  
Figure 2: Control plane distribution between the main approaches of TSCH schedulers. (**a**) Autonomous, (**b**) Distributed, (**c**) Centralized (SDN), (**d**) Centralized.
## 3 Results  
During the whole observation period, untreated colonies showed a peak of AFB cases in March (10 outbreaks) and April (9 outbreaks) while EFB showed the largest number of cases in the untreated group in April (Figure 2).  
The overall prevalence of clinical cases in the untreated group was of 5.1% for AFB and 4.5% for EFB. The administration of _L. plantarum_ for four weeks determined a reduced EFB prevalence of 2.0%. On the contrary, the prevalence of AFB in the treated group was higher than the untreated group of 0.2% (Figures 3 and 4, Table 2). Moreover, during the whole observation period, the group treated with _L. plantarum_ showed lower cases of EFB, except during June and July (Figure 3), after 4 months since the end of the treatment.  
Figure 1: Apiaries distribution.  
Figure 4: EFB cases in the two groups.  
Figure 3: AFB cases in the two groups.  
Figure 2: Number of symptomatic cases of AFB and EFB in the untreated groups.  
\(p\)-value of Log-Rank test was 0.906 (obs. value 0.014) showing no statistical differences between the survival curve of the treated and untreated groups compared to the number of symptomatic AFB cases.  
The Kaplan-Meier estimator of the survival function applied to the AFB cases is reported in Figure 6.  
\(p\)-value of Log-Rank test applied to EFB cases (Figure 6) resulted in 0.083 (obs. value 3.005) showing a higher difference compared to the survival curves of AFB.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multirow{2}{*}{**Number of Colonies**} & \multicolumn{2}{c}{**Number of Clinical Symptomatic Cases**} & \multicolumn{2}{c}{**Disease Prevalence**} \\ \cline{3-6}  & & **AFB** & **EFB** & **AFB** & **EFB** \\ \hline Control group & 491 & 25 & 22 & 5.1\% & 4.5\% \\ _L. plantarum_ group & 488 & 26 & 12 & 5.3\% & 2.5\% \\ Total number of colonies & 979 & 51 & 34 & 5.2\% & 3.5\% \\ \hline \hline \end{tabular}  
Kaplan–Meier estimator of the survival function applied to the AFB cases is reported in Figure 5.  
\end{table}
Table 2: Disease prevalence in _L. plantarum_ and control groups.  
Figure 5: Survival estimation curves—AFB cases.  
Figure 6: Survival estimation curves—EFB cases.  
In order to establish if the use of \(L\). _plantarum_ could have a preventive effect in reducing the number of cases of EFB based on the time elapsed from the end of the treatment period, the Log-Rank test has been applied to different time-frames, excluding from the analysis all the other cases that emerged during the trial. The \(p\)-values are reported in Table 3.  
The \(p\)-value of Log-Rank test applied to survival curves of EFB cases in the treated and untreated groups showed a statistical difference at day 34.
## 3. Numerical Examples
### Properties of the Hysteretic Dampers  
The properties of the steel hysteretic dampers are assumed to be proportional to those of the bare frame. The stiffness ratio \(\kappa\)[10] that corresponds to the ratio of the damper stiffness to the story stiffness of the frame is used to determine the stiffness of the damper at the \(i\)th story, as follows:  
\[k_{d,i}=\kappa\cdot k_{f,i} \tag{18}\]  
where \(k_{f,i}\) is the lateral story stiffness of the bare frame that is determined by using the NSPA results. The lateral stiffness ratio \(\alpha\) (\(=k_{f}/k_{total}\) where \(k_{total}=k_{f}+k_{d}\)) of reference [5; 13] is equivalent to \(1/(\kappa+1)\).  
The yield drift ratio \(\upsilon\)[27] that corresponds to the ratio of the yield deformation of the damper to the story yield drift of the bare frame is used to determine the yield deformation of the damper at the \(i\)th story, as follows:  
\[dy_{d,i}=\upsilon\cdot dy_{f,i} \tag{19}\]  
where \(dy_{f,i}\) is the yield story drift of the bare frame that is determined using the NSPA results.  
In this paper, the stiffness ratio, the drift ratio, and the post-elastic stiffness ratio of the dampers correspond to two values (1.5 and 3), two values (0.4 and 0.6), and one value (0.03), respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Element** & **Label** & **Section** & **Mp (kN\(\cdot\)m)** \\ \hline \multirow{4}{*}{Columns} & C4 & \(\odot\)-550\(\times\)22 & 2991.7 \\ \cline{2-4}  & C3 & \(\odot\)-550\(\times\)25 & 3361.7 \\ \cline{2-4}  & C2 & \(\odot\)-550\(\times\)28 & 3723.0 \\ \cline{2-4}  & C1 & \(\odot\)-550\(\times\)32 & 4191.2 \\ \hline \multirow{4}{*}{Beams} & B4 & H-550\(\times\)250\(\times\)12\(\times\)22 & 1204.3 \\ \cline{2-4}  & B3 & H-550\(\times\)250\(\times\)12\(\times\)25 & 1322.3 \\ \cline{2-4}  & B2 & H-550\(\times\)250\(\times\)12\(\times\)28 & 1439.0 \\ \cline{1-1} \cline{2-4}  & B1 & H-550\(\times\)300\(\times\)14\(\times\)28 & 1718.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sections and plastic moments of the columns and beams of a twelve-story steel moment resisting frame.
# Optimization of Blended Biochar Pellet by the Use of Nutrient Releasing Model
## 2 Materials and Methods
### Batch Experiment for Nutrient Leaching Test  
The treatments consisted of pig manure compost (PMC) as a control, pig manure compost pellet (PMCP), and various ratios of biochar pellets (BCP) blended with 2:8, 4:6, 8:2, and 9:1 of biochar/pig manure compost (w/w) in order to test the feasibility of developing a slow-release fertilizer.  
For the nutrient releasing experiment, the size of the glass column was \(\mathcal{O}\) 24 mm \(\times\) 40 cm with each column filled with 5 g of PMC, PMCP and different blended BCP, respectively. The column was poured into 50 mL of deionized water, completely drained, and then immediately refilled after certain retention time. The water samples were collected at 50 mL of drained water through the column until 84 days of leaching periods.
## 2 Background
### Multi-Channel Wireless Mesh Network (M-WMN)  
In a conventional wireless networking device, the operation is only enabled on 1 channel. There are two methods that can be used to implement the multi-channel wireless mesh network; multiple radios on the physical layer (PHY) or using the channel switching capability of the device [4]. In this embedded system implementation, the former method in which two wireless network devices with a single radio PHY each were used. However, the two channels of the wireless network devices in this embedded system solution will be used for different purposes.
# Mechanical Analysis of DS in Horizontal Directional Drilling
## 5 Engineering Application  
During the reaming stage of the HDD engineering of YangBa mountain, when the 147 th DS (6-5/8 in) is drilling 2 m, the mud pressure and torque suddenly dropped to zero. It is speculated that the DS is broken, and the broken DS was found when the DS was pulled back to the 137 th DS (see Figure 1). According to the field analysis, there are two main reasons for DS fracture:  
1. The size of DS is small, and its torsion resistance is low.  
2. The drag torque between DS and borehole wall is large.  
In the dynamic simulation results, it is found that in the ultra-long span HDD project, the drill string is prone to buckling deformation, which is caused by the small size of the drill string and its poor torsional resistance. The large drag torque mentioned in the case corresponds to the conclusion in the previous article: the buckling deformation of the drill string leads to the above contact friction and even self-locking. Therefore, based on the rules and conclusions obtained in this paper, the DS after the 130 th is replaced by a 7-5/8-inch DS on site, and some stabilizers are installed in the bending section to reduce the contact drag torque of the DS. Finally, the HDD project with a horizontal length of 1705.4 m (the total length is 1718.2 m) was successfully completed.
## 3 Results
### Statistical Tests
#### 3.2.2 McNemar's Test  
[PERSON]'s test, a non-parametric statistical method, is employed to evaluate and compare the error rates of two binary classification models applied to an identical dataset. It determines if the discrepancies in their performance are statistically significant. Given a binary classification task, let \(CM_{1}\) and \(CM_{2}\) be two classification models evaluated on the same set of \(n\) instances. Each instance is classified as either positive (1) or negative (0). [PERSON]'s test analyzes the disagreement between the two models' predictions, focusing on instances where the models differ in their classification. The contingency table for [PERSON]'s test is as follows:  
According to Table 1, \(n_{11}\), \(n_{10}\), \(n_{01}\), and \(n_{00}\) represent the number of instances correctly classified as positive by both models, classified as positive by \(CM_{1}\) but negative by \(CM_{2}\), classified as negative by \(CM_{1}\) but positive by \(CM_{2}\), and correctly classified as negative by both classification models, respectively. [PERSON]'s test statistic \(\chi^{2}\) is based on the \(n_{10}\) and \(n_{01}\) and is calculated as follows:  
\[\chi^{2}=\frac{(|n_{10}-n_{01}|-1)^{2}}{n_{10}+n_{01}} \tag{8}\]  
The test statistic in question adheres to a chi-square distribution with one degree of freedom when the null hypothesis presumes identical error rates for the models. To evaluate the significance of the observed variance, the test statistic is adjoined with the critical value at a significance level \(\alpha\), as listed in chi-square distribution tables. Should the test statistic exceed the critical value, the null hypothesis is dismissed, signifying a statistically meaningful divergence in the error rates of the two classification models. Another approach to determining the statistical significance is to compute the \(p\)-value.
# Efficient Intrusion Detection Algorithms for Smart Cities-Based Wireless Sensing Technologies
## 4 Proposed Intrusion Detection Algorithms for Smart Cities Network
### TBIDS Detection Model  
In this section, the detection model is described in which two cases are considered, a single sink node station and multiple sink nodes. A sensor network consists of \(S\) sensing devices that could be homogenous or heterogeneous. The heterogeneous sensor, in this context, is a sensor with different settings or capabilities. For instance, sensors might differ in their sensing, communication, and/or initial energy. In the setup phase, AODV and LEACH models are uploaded to all of the sensor nodes. Sensors are deployed either randomly or according to one of the deployment algorithms [42]. After deployment, the LEACH clustering algorithm is applied to form connected cluster heads (CHs). Once the cluster heads are chosen, the network is now ready to operate.  
In smart cities, the concept of using a single sink node might not be applicable. Therefore, in this paper, the detection model for two cases--a single sink node and multiple sink nodes--are considered.  
In the case of a single sink node or multi-sink nodes, the sink is responsible for collecting sensors information as well as running the detection model. Besides, the sink node \(SN\) is supposed to compute the following information for each cluster head (\(CH_{j}\)):  
- The packets sent (\(RMj\))  
- The packets received (\(TMj\))  
- The sinkhole ratios \(SR_{j}^{t}\)_and_\(SR_{j}^{b}\)  
\[SR_{j}^{t}=\ \frac{\left|RMj\right|}{\left|TMj\right|}\text{ and }SR_{j}^{b}=\ CH_{j} \tag{2}\]  
Based on the value of ratios \(SR_{j}^{t}\)_and_\(SR_{j}^{b}\), the sink node (\(SN\)) decides on the malicious \(CH_{j}\). The value of \(SR_{j}^{t}\) is a numeric value in which:  
\[SR_{j}^{t}=\ \left\{\begin{array}{l}\infty,\left|CH_{j}\text{is a malicious node}\\ n,\left|n\right.\leq N_{i}\ CH_{j}\text{ is not a malicious node}\end{array}\right. \tag{3}\]  
\(N_{i}\) is learned from the network activities or decided by the network designer. Therefore, the best value of \(N_{i}\) has been exerted based on the trial and error approach. It would be better to have a learning algorithm to deduce such value and other thresholds as well. However, it is considered out of the scope of this paper, and it is part of the future work.
## 3. Numerical Examples
### Results and Discussion
Figure 5: Regressions of \(\theta_{\max}\) on \(\theta_{\max}^{P}\) for a three-story frame with dampers: (**a**) \(\kappa=1.5\) and \(\upsilon=0.4\); (**b**) \(\kappa=1.5\) and \(\upsilon=0.6\); (**c**) \(\kappa=3\) and \(\upsilon=0.4\); and (**d**) \(\kappa=3\) and \(\upsilon=0.6\). Figure 8 summarizes the bias (_a_) and the dispersion (\(\sigma\)) of the simplified method for: (a) a three-story frame; (b) a six-story frame; and (c) a twelve-story frame with dampers that are subjected to all the earthquake records. The biases and dispersions of \(\theta_{i}^{P}\) estimated by the simplified method are in the range of 0.972-1.151 and 0.025-0.179, respectively, for all the frames. In the case where \(\kappa=1.5\), as shown in Figure 8c, the biases of \(\theta_{i}^{P}\) estimated by the simplified method exceed 1.1 for the upper stories (i.e., 9-12 stories) of the twelve-story frame with dampers. However, the biases of \(\theta_{i}^{P}\) for the other stories of all the frames are less than 1.1. Additionally, the dispersions of \(\theta_{i}^{P}\) estimated by the simplified method are less than 0.02 for all the stories of all the frames. Therefore, the accuracy of the simplified method is sufficient for evaluating the seismic demands, irrespective of the difference in the damper parameters. In this paper, the assumption \(E_{d,i}=\overline{E}_{d,i}\) is used to convert the steel frame with dampers into an equivalent SDOF system. To confirm this assumption, Figure 9 shows a one-to-one comparison between the total energy dissipated by the dampers in the elaborate analytical model and the total energy dissipated by the inelastic springs in the equivalent SDOF system. The vertical axis represents the total energy dissipated by the springs of the equivalent SDOF system, and the horizontal axis signifies the total energy dissipated by the dampers of the elaborate analytical model. As shown in Figure 9a, the total energies dissipated by the springs agree well with those dissipated by the dampers. In contrast, the total energy dissipated by the springs fluctuates slightly when compared with the total energy dissipated by the dampers, as shown in Figure 9b,c. However, the total energy dissipated by the springs agrees well with the total energy dissipated by the dampers, irrespective of the difference in the magnitude of an earthquake. Therefore, the accuracy of the equivalent SDOF system is sufficient to evaluate the seismic demands, irrespective of the difference in the damper parameters with respect to the stiffness ratio \(\kappa\) and the drift ratio \(\upsilon\). Figure 8: Bias (_a_) and dispersion (\(\sigma\)) of the simplified method: (**a**) for a three-story frame with dampers; (**b**) for a six-story frame with dampers; and (**c**) for a twelve-story frame with dampers.