esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### Effect of Acoustic Damping on the Interior Noise  
The acoustic damping inside the cavity is an important parameter that affects the noise level at the targets. We investigated the effect of the acoustic damping loss factor on the noise level received at Target 1.  
Figure 12 displays the sound pressure level at the position of Target 1 with different acoustic DLFs.  
The depicted figure highlights the substantial impact of acoustic damping on the received noise at a receiver. Furthermore, the figure indicates that for frequencies exceeding  
Figure 11: Sound pressure level prediction at higher frequencies using ACTRAN extended extrapolation solution.  
Figure 12: Sound pressure level with different acoustic DLFs. The DLF for the curve with a solid line and square marker is 0.02 up to 125 Hz, and it is 0.06 from 160 Hz to 1250 Hz. For the rest of the SEA result curves, the DLF is constant over the whole frequency range.  
200 Hz, employing an acoustic damping factor (DLF) of 0.06 yields results that closely approximate those obtained using ATPA. The average difference between the results of ATPA and SEA in one-third of octave bands (in the frequency range of 63 Hz to 1250 Hz) is 9.4 dB, but it can be significantly improved with an adjustment of the acoustic DLF, resulting in an average of 5.4 dB (see Figure 12). On the other hand, the differences between the overall sound pressure levels calculated using the ATPA and SEA methods with an adjusted DLF (in the same frequency range) are 4.1 dB for Target 1 and 5.4 dB for Target 2.  
It is worth noting that the acoustic DLF values were computed from impact tests on panels as opposed to acoustic cavity excitation, which might have led to a more precise determination of this parameter.
## 3 Experimental Results  
In this section, the AOA is implemented in the IEEE 33-bus and IEEE 69-bus systems to specify optimum placements and capacities of DGs and CBs by minimizing total power loss and voltage deviation. In all scenarios, the minimum and maximum limits of each DG are determined as 0 MW and 3 MW, respectively, while the capacity of each CB is determined as \(\pm\)3000 kVAR. In addition, the optimum power factor of DGs is determined with AOA, and the obtained results are compared with other algorithms and scenarios. Power losses and voltage profiles of distribution systems are investigated in all scenarios. The results obtained in this study are compared with the results of other optimization algorithms. In this study, the distribution systems are analyzed in four scenarios and each scenario is explained in Table 1.
# Quality Assessment of HDR/WCG Images Using HDR Uniform Color Spaces
## 2 From State-of-the-Art SDR Quality Assessment Metrics to HDR/WCG Quality Assessment Metrics
### Adapting SDR Metrics to HDR/WCG Images
#### 2.4.2 Remapping Function  
The six aforementioned color spaces, i.e., \(XYZ\), \(Y^{\prime}Cr^{\prime}Cb^{\prime}\), \(HDR\)-\(Lab_{100}\), \(HDR\)-\(Lab_{1000}\), \(ICtCp\) and \(J_{2}a_{2}b_{2}\), have a different range of values. As most of SDR metrics have constant values defined for pixel values between 0 and 255, it is required to adapt the color spaces. We remap them in a way that their respective perceptually linear luminances fit a similar range as the luminances encoded with the PU transfer function between 0 and 100 cd/m\({}^{2}\). We choose 100 cd/m\({}^{2}\) as a normalization point because it roughly corresponds to the peak brightness of an SDR screen. Moreover, the PU-encoding is used as a reference to remap the color spaces because it is already adapted to SDR metrics. The goal of this process is to obtain HDR images with the same luminance scale than SDR images in the range 0 to 100 cd/m\({}^{2}\) while preserving the perceptual uniformity of the color spaces. The remapping of the perceptual color spaces is done as follows:  
Figure 1: Diagram of the proposed method to adapt SDR metrics to HDR/WCG contents.  
\[\widehat{j_{z}a_{z}b_{z}}(i,j)=\frac{\alpha_{PU}}{\beta_{I_{z}}}\times J_{z}a_{ z}b_{z}(i,j) \tag{23}\]  
where \(J_{z}a_{z}b_{z}(i,j)\) corresponds to the value in the \(J_{z}a_{z}b_{z}\) domain of the pixel with the spatial coordinates \(i\) and \(j\). \(\widehat{J_{z}a_{z}b_{z}}(i,j)\) corresponds to the same pixel value after the remapping. \(\alpha_{PU}\) is the luminance value in the PU space when linear luminance value is \(100\) cd/m\({}^{2}\). \(\beta_{I_{z}}\) is the same value but for the luminance component \(J_{z}\) of the \(J_{z}a_{z}b_{z}\) color space. A similar operation is applied to \(ICICp\) and \(HDR\)-\(Lab\), \(XYZ\) and \(Y^{\prime}C^{\prime}Cb^{\prime}\) color spaces. The resulting luminances for the aforementioned color-space as well as the PU-encoding luminance are plot on Figure 2. For these figures, we chose a surround luminance of \(20\) cd/m\({}^{2}\) for the two \(HDR\)-\(Lab\) color spaces.  
**Remark 1**.:
* _Note that, to adapt the_ \(\overline{\Delta E^{5}}\) _metrics, the blurring model used in this metrics is first applied to the_ \(XYZ\) _color space of the images and then the different color difference metrics are calculated. In the case of the_ \(J_{z}a_{z}b_{z}\) _color space instead of the color difference metrics presented in Equation (_15_), we use a simpler Euclidean distance between the pixel values._
* _In the following sections, the naming convention used for all metrics is_ Metrics__ColorSpace__. For example, the PSNR metrics used with the_ \(ICTCp\) _color space is called_ PSNR__ICICp__.
## 4 Results
### Mobility in Private Vehicles
#### 4.1.6 Parking: Problems and Solutions  
The high percentage of journeys made in private vehicles in the RMSC makes it very important to analyse the parking of these vehicles, both at the origin and at the destination of their journeys. This analysis allows for the detection of the shortcomings or problems related to this aspect, thus making it possible to propose alternatives and solutions in the cases that require them. Figure 9 below shows the parking situation in the city of Caceres.  
With regard to parking at the place of destination, there is clear preference among those surveyed for parking on the street, especially where there is no blue zone pricing (63% of those surveyed). The second most used option is parking, with 26%. As discussed  
Figure 8: Problems associated with private vehicles in RMSC.  
Figure 9: Parking situation in Cáceres. (**a**) Parking place at destination of journeys; (**b**) Parking facilities in Cáceres.  
above, Caceres is the main point of attraction for journeys from the rest of the municipalities that make up the RMSC. Since private vehicles are the main means of transport chosen to travel to the capital of Caceres, one of the main drawbacks in accessing the city is finding a parking space in the city. When asked about parking facilities within the city centre of Caceres, 87% of those surveyed considered them to be unsatisfactory or very unsatisfactory, making it clear that there is a parking problem in the city.  
Related to the above, Figure 10 shows the respondents' opinions on the conditioning factors for the use of park-and-ride facilities located at the entrances to the city of Caceres and connected by public transport to the city centre and various points in the city, also known as park-and-ride facilities. A total of 62% of respondents stated that they would be willing to use this type of parking. When it comes to using this type of car park, the factors that would influence their choice of car park would be the frequency of the connecting bus service (30%), the price of parking (27%) and the variety of destinations (19%).  
When analysing the on-street parking situation in the different municipalities that make up the RMSC, no significant difficulties were found. In certain municipalities, the characteristics of their urban centres and the size of their streets can make parking difficult, causing problems for traffic circulation, as is the case in Montanchez and Brozas. These situations mainly occur in the summer, when there is a greater influx of vehicles. Given the possibility of creating new parking areas in these municipalities, the lack of available space makes them unviable alternatives.
## 2 Related Method and Work
### Dual-Receiving Acoustic Signal Processing
It may lead to each localization loop becoming discontinuous in the NLOS scenario with a transitory duration, which will lessen the user experience sharply. After the selection and cutting process from [22], the NLOS identification part can be undertaken. Then, the NLOS identification results in one specific localization loop can be worked out. Before compiling the Android APP, users need to measure the indoor temperature and then calculate the indoor acoustic signal propagation velocity precisely. The main anchor and other acoustic anchors are processed by the effective composite window function in the system. At this time, the core process at each dual-receiving acoustic anchor is as shown in Figure 2 below. To make it interact with the indoor sensor network or cognitive IoT networks more smoothly, convenient D/A or A/D conversion modules are necessary. We made our own acoustic anchors based on the literature mentioned above; their external core parts can be seen in Figure 3 below. For each acoustic anchor, the chirp acoustic signal originally sent by the source may produce harsh noise after being processed by an external circuit, which greatly brings the user an awful experience. Therefore, the direct digital synthesis  
Figure 2: Main process of RSS selection mechanism from [22] of dual-receiving acoustic anchor. (DDS) module is required to be used in the acoustic anchor, and an analog type A amplifier will be used for the audio power amplifier to optimize the communication effect. The acoustic anchor will take STM32 as the mainstay and combine it with the feedback of the DDS module and other dual-receiving acoustic anchors. It will also complete the chirp acoustic signal and the necessary interaction process with the main anchor. In the end, the displayed results are the coordinates of this main anchor. As for the indoor localization system, the Arduino modules or LEGO components should be used for transformations (refer to [21,22]). If there are severe limitations and the NLOS scenario is not so complex, a single receiving acoustic anchor also can be used under that circumstance. We used a dual-receiving acoustic anchor based on a PCB circuit designed in [21]. The two kinds of self-made acoustic anchors are as shown in Figure 3a,b below. The independent receiving acoustic signal is supposed to be considered as an NLOS acoustic signal only when all of the chirp acoustic signals sent by the main anchor are blocked between the main anchor and acoustic anchors. In other words, if no less than one transmission path is not blocked or shielded, the acoustic localization signal will still be regarded as an LOS acoustic signal for each anchor. Users also can be allowed to choose to apply this mechanism in the system or not, according to their actual demands.
## 4 MS-FARIMA Algorithm and Orbit Extrapolation Improvement with MSEC  
After the decomposition of the extrapolation errors based on the ephemeris of one satellite using TSFs, we can effectively measure and decrease the PE \(S(t)\) and TE \(R(t)\). However, due to the inevitable randomness, RE \(U(t)\) needs to be further diminished via precise methods. As the errors of the satellite and its adjacent satellites can suffer from similar degradation, extrapolating the orbit errors of adjacent satellites can contribute to diminishing their common errors. In addition, the adjustment of the extrapolation errors of one satellite at different observation times can be performed for error improvement. Therefore, we propose a novel and accurate orbit extrapolation framework that involves the ephemerides of multi-satellite and multi-stream data TSFs. An MS-FARIMA algorithm is designed, which supports data from the same attribute information at different times and for different objects. By applying MS-FARIMA to handle multi-satellite ephemeris coordination, the accuracy of the orbit extrapolation error is enhanced.
## 4 Improvement Proposals
### DDS for Stock Control
#### 4.1.1 Weekly Control  
\"Weekly Control\", whose interface is shown in Figure 2, is associated with the implementation of a Control Sheet (CS) in every storage zone that the SE has.  
The proposed CS contains the following information: \"Date\" (dd/mm/yyyy); \"Worker\" (signature); \"Category\"; \"Reference Code\"; and \"Output\", which should be filled in each time a worker removes any quantity that is in storage; and a column named \"Check\" to be filled by the person responsible for stock control when adding this information in the DDS, in the \"Weekly Control\". To streamline the use of the CS by workers, a map of the storage areas of the different categories was created, which is presented in Appendix A. This map should be placed in each work area to allow each worker to have a quick source of information regarding where the material they are looking for is located according to the material's category. Weekly, a named, responsible individual should survey the outputs  
Figure 1: Proposed label.  
Figure 2: Weekly control interface.  
registered in the CS, and enter this value associated with the date in the DDS as well as any entry that has existed for that product. This way, a \"History\" sheet will also be created and regularly updated.  
The first step was the elaboration of the interface shown in Figure 2; afterwards, functions were assigned to each cell. In the \"Reference\" cell of Figure 2, the reference code corresponding to the withdrawn or added product should be selected. For this purpose, a searchable list was created (with a combo box, \"ActiveX Control\"). The \"Product Description\" (in Figure 2) cell has the function of automatically displaying the product description associated with the code entered in the \"Reference\" cell using the XLOOKUP command. The purpose of this cell is to minimize errors, for example, entering a quantity for the wrong product. The \"Input\" and \"Output\" cells (from Figure 2) are meant to be filled with the quantities consumed and/or acquired during the week. The \"Quantity in Stock\" cell in Figure 2 will allow the visualization of the quantity in stock through the sum of the quantities, with reference to the respective code in the \"History\" sheet, using a SUMIF command. The buttons \"CLEAR\" and \"ADD\" were created with the Excel Macros option.  
To use this control, the user must follow the following order of application:  
1. Select the reference code of the product being controlled;
2. Select the date;
3. Enter the input value if any quantity was purchased, or the value \"0\";
4. Enter the output value, i.e., the consumption of the week;
5. Click on the \"Add\" button;
6. Automatically, an updated stock quantity will appear (and the information will be added to a \"History\" of inputs and outputs sheet);
7. Click on the \"CLEAR\" button;
8. Insert new reference code;
9. Repeat steps 1 to 7.
## 6 Experimental Evaluation  
Considering the expense and time incurred in the evaluation of large-scale experiments in real-time, Matlab is used to simulate the environment. Each reservation cycle is assumed to have a duration of 300 s (rc = 300 s). An input request is accepted at the beginning of each reservation cycle.  
The VM's resource requirements are assumed constant throughout the interval. A single datacenter with heterogeneous systems capable of provisioning multiple VMs is considered. As the size of the requested virtual resource is not known, with no limitation for VM request, only the active state execution time of the VM is considered. As the CPU is considered to consume a significant fraction of energy, the tasks are considered CPU-intensive. In this work, CPU utilization and power consumption are considered linearly proportional. All the machines are assumed idle when not in use consuming static power. The datacenter assumed to be powered only by grid energy source. The peak IT load (only server) power estimation of a datacenter is estimated \(\approx\) as 44 kW for the physical machine specification given in Table 2[47]. A datacenter is assumed to have a floor space of \(\approx\) 500 square feet. Total electricitypower requirement is calculated as \(\approx\)136.8 kW (including cooling load, lighting and others). The CPU power consumption of all the servers should not exceed 23 kW [48].  
Input: _SourceHost_, _DestHost_, _threshold_  
Output: _TargetVMQ_  
1 For _shot_ in _SourceHost_ do  
2 \(SB\)--Get the current dynamic power of _shot_;  
3 \(sel\)--_task_--Get _VM_ from _shot_ based on _VM_ selection policy;  
4 \(SR\)--Get the power of _shot_ after removing the _sel-task_;  
5 \(\Delta S\)--\(SB\)--\(SR\);  
6 For _dhost_ in _Desthost_ do  
7 \(dhost\) feasible for _sel-task_ then  
8 \(CP\)--Get current dynamic power of the _dhost_;  
9 \(AP\)--Calculate the dynamic power after placing the _VM_;  
10 \(\Delta D\)--\(AP\)--\(CP\);  
11 \(SelectedHost\).add(_dhost_);  
12 If _SelectedHost_*NULL_ then  
13 \(host\)--Get _SelectedHost_ with \(\Delta D\) less than \(\Delta\)S;  
14 \(TargetVMQ\).add(_sel-task_, _host_);  
15 \(\Delta S\)--Remove the _sel-task_ from the _shot_;  
16 \(\Delta S\)--\(util\)--\(threshold\)  
17 \(\Delta S\)--Remove _shot_ from _SourceHost_;  
18 \(\Delta S\)--\(go\) to line1;  
19 \(\Delta S\)--\(go\) to line2;  
20 \(\Delta S\)--\(go\) to line3;  
21 \(\Delta S\)--\(go\) to line4;  
22 Return _TargetVMQ_;  
**Algorithm 6** Proposed task distribution algorithm (TDA)
## 3 DDoS Attack Detection Methods
### DDoS Attack Detection Studies Using Anomaly-Based Methods
#### 3.3.2 Queue Modelling-Based Detection Methods
In [80], a framework to identify DDoS attacks using the packet flows of particular protocols was presented. In this study, the normal behaviour is estimated using a Gaussian parametrical mixture model, while the attacks are detected using a queue model. The results show the approach is effective with reasonable detection accuracy. [PERSON] and [PERSON] [81] analysed the effects of attacks on variables such as queue growth rate using a standard M/M/1/K queue model with round robin discipline. The given results demonstrate that the queue growth rate linearly increases as the frequency of flooding attacks increases. The authors in [82] presented the use of the queueing model for network router attack detection. In this study, the traffic congestion due to attack packets can be readily noticed at locations near the target rather than the attack sources; consequently, it is anticipated that the technique will have a comparatively higher false negative rate. In [83], a queue scheme was developed for detecting malicious attacks. In this study, the arrival requests are provided with a queue service at a base station that oversees assessing the forwarded packets. Once the traffic is backed up for an extended period, malicious attacks are discovered. In [84; 85], the effectiveness of queuing management mechanisms under DDoS attack detection were evaluated. Five distinct queuing algorithms--drop-tail, RED, deficit round robin (DRR), fair queue (FQ), and stochastic fair queue (SFQ)--were tested for how UDP flooding affected their performance in [84]. The study demonstrates that SFQ outperforms the other queuing mechanisms for UDP traffic. Recently, using NS2 software, [PERSON] et al. [85] evaluated the effectiveness of drop-tail, RED, and REM queue management mechanisms on ad hoc networks under attack. This study evaluated the performance of the three mechanisms under small-, medium-, and large-scale DDoS attacks based on the packet rate and average end-to-end latency. Simulation results revealed that drop-tail was less effective at detecting medium- and small-scale DDoS attacks than REM and RED. However, all three mechanisms showed inadequate detection abilities when subjected to large-scale DDoS attacks.
## 2 Machine Configuration and Operating Principle
### Flux Modulation Principles
where \(N_{t}\) is the number of turns in one phase, \(k_{d}\) is the corresponding distribution factor, and \(y_{ph}\) is the corresponding coil pitch. By substituting (12)-(14) into (15), the \(\psi_{ph}\) components of the three types can be expressed as follows:  
\[ \begin{array}{ll}\psi_{phA}(t)&=r_{g}l_{stk}N_{t}k_{dA}\int\frac{\frac{\pi}{2 }y_{A}}{2}B_{r1}(\theta,t)d\theta\\ &=\sum\limits_{i=1,3,5...}\frac{2r_{g}l_{stk}N_{t}^{k_{dA}}F_{in}\lambda_{0}}{ ip_{r}}\sin\left(ip,\frac{v_{dA}\tau}{Z}\right)\cos(ip_{r}\Omega t)\\ \\ l\psi_{phB1}(t,\phi)&=r_{g}l_{stk}N_{t}k_{dB1}\int\frac{\pi}{2}y_{B1}\ B_{r2}( \theta,t,\phi)d\theta\\ &=\sum\limits_{i=1,3,5...}\sum\limits_{j=1,2,3...}\frac{r_{g}l_{stk}N_{t}k_{ dB1}F_{in}\lambda_{j}}{(jp_{m}+ip_{r})}\sin\left((jp_{m}+ip_{r})\frac{y_{B1}\tau}{Z} \right)\cos(ip_{r}\Omega t+jp_{m}\phi)\\ \\ l\psi_{phB2}(t,\phi)&=r_{g}l_{stk}N_{t}k_{dB2}\int\frac{\pi}{2}y_{B2}\ B_{r3}( \theta,t,\phi)d\theta\\ &=\sum\limits_{i=1,3,5...}\sum\limits_{j=1,2,3...}\frac{r_{g}l_{stk}N_{t}k_{ dB2}F_{in}\lambda_{j}}{(jp_{m}-ip_{r})}\sin\left((jp_{m}-ip_{r})\frac{y_{B2}\tau}{Z} \right)\cos(-ip_{r}\Omega t+jp_{m}\phi)\\ \\ \end{array}  \tag{17}\]  
The back EMF of one phase can be calculated as follows:  
\[E_{ph}(t,\phi)=-\frac{d\psi_{ph}(t,\phi)}{dt}=E_{phA}(t)+E_{phB1}(t,\phi)+E_{ phB2}(t,\phi) \tag{19}\]  
By substituting (16)-(18) into (19), the \(E_{ph}\) components of three types can be expressed as follows:  
\[E_{phA}(t)=\sum\limits_{i=1,3,5...}\ E_{mA(i)}\sin(ip_{r}\Omega t) \tag{20}\]  
\[E_{phB1}(t,\phi)=\sum\limits_{i=1,3,5...}\sum\limits_{j=1,2,3...}\ E_{mB1(i,j )}\sin(ip_{r}\Omega t+jp_{m}\phi) \tag{21}\]  
\[E_{phB2}(t,\phi)=\sum\limits_{i=1,3,5...}\sum\limits_{j=1,2,3...}\ E_{mB2(i,j )}\sin(ip_{r}\Omega t-jp_{m}\phi) \tag{22}\]  
where \(E_{mA(i)}\), \(E_{mB1(i,j)}\), and \(E_{mB2(i,j)}\) are the amplitudes of \(E_{phA}\), \(E_{phB1}\), and \(E_{phB2}\), respectively. All \(E_{m}\) can be given as the following expression:  
\[E_{m}=k_{w}r_{g}l_{stk}N_{t}\omega B_{m}=k_{d}k_{p}r_{g}l_{stk}N_{t}\omega B_{m} \tag{23}\]  
where \(k_{w}\) is the winding factor, \(k_{p}\) is the pitch factor, \(\omega\) is the corresponding angular speed, and \(B_{m}\) is the corresponding amplitude of the flux density. Therefore, \(E_{mA(i)}\), \(E_{mB1(i,j)}\), and \(E_{mB2(i,j)}\) can be expressed as follows:  
\[E_{mA(i)}=k_{dA}k_{pA}r_{g}l_{stk}N_{t}\omega_{A}B_{mA}=k_{dA}\sin\left(ip_{r }\frac{y_{A}\tau}{Z}\right)r_{g}l_{stk}N_{t}\Omega(2F_{ri}\Lambda_{0}) \tag{24}\]  
\[ \begin{array}{ll}E_{mB1(i,j)}&=k_{dB1}k_{pB1}r_{g}l_{stk}N_{t}\omega_{B1}B_{ mB1}\\ &=k_{dB1}\sin\left((jp_{m}+ip_{r})\frac{y_{B1}\tau}{Z}\right)\ r_{g}l_{stk}N_{t} \frac{ip_{r}\Omega}{jp_{m}+ip_{r}}\left(F_{ri}\Lambda_{j}\right)\\ \\ E_{mB2(i,j)}&=k_{dB2}k_{pB2}r_{g}l_{stk}N_{t}\omega_{B2}B_{mB2}\\ &=k_{dB2}\sin\left((jp_{m}-ip_{r})\frac{y_{B2}\tau}{Z}\right)\ r_{g}l_{stk}N_{t} \frac{ip_{r}\Omega}{jp_{m}-ip_{r}}\left(F_{ri}\Lambda_{j}\right)\\ \end{array}  \tag{25}\]