esa-sceva/satcom-chunk-collection huggingface dataset:
# Angle-of-Arrival Estimation Using Difference Beams in Localized Hybrid Arrays
## 3 Proposed AOA Estimation Approach
### Phase Shift Design  
Let the \(n\)th phase shift of the \(m\)th subarray at the \(t\)th (\(t=0,...,T-1\)) symbol be \(\alpha_{m}^{n}(t)\) given by  
\[\alpha_{m}^{n}(t)=\begin{cases}n\alpha_{m}(t),&n=0,...,\frac{N}{2}-1\\ \pi+n\alpha_{m}(t),&n=\frac{N}{2},...,N-1\end{cases} \tag{4}\]  
Figure 1: Illustration of a localized array with \(M\) subarrays, where the RF and down conversion components are omitted for simplicity.  
where \(\alpha_{m}(t)=-2\pi(\mod\{m,K\}T+t)/L,\mod\{\cdot,\cdot\}\) represents the modulo operation, and thus \(\mod\{m,K\}\) indicates that \(\alpha_{m}^{n}(t)\) varies periodically every \(K\) subarrays in one symbol; \(K\) takes a value from \((2,M]\) and \(N=QK\), where \(N\) is assumed to be an even number and \(Q\) is an integer; \(T\) is the number of training symbols; \(L=TK\) is the total number of different phase shifts used in the system. The setting given by (4) is able to make the array scan potential \(2L\) directions within \([-\pi,\pi)\) across \(T\) symbols, ensuring that the AoA is acquired by at least one of the \(L\) beams with high gain. Note that it is necessary to have the mainlobes of two difference beams to cover the AoA so that sufficient energy can be obtained when computing the cross-correlation to estimate the AoA. According to the sampling theorem, at least \(N\) scanning beams are required to cover the AoA range of \([-\pi,\pi)\) given the number of antennas per subarray \(N\). Generally, the proper values of \(N\), \(K\) and \(T\) are supposed to be set to satisfy \(N\leq TK\) for good AoA coverage with beamforming gains.  
Unlike the phase shift design in [5] that leverages multiple sum beams to steer multiple evenly distributed directions within \([-\pi,\pi)\), the proposed one can steer double directions using each subarray by exploiting the difference beams [12]. Each difference beam steers a null towards the boresight of the corresponding sum beam. An example of normalized beam patterns within the first symbol period are shown in Figure 2, where the red solid and black dotted curves represent the synthesized difference beams and sum beams, respectively. In this example, we adopt \(K=M=8\) and \(N=24\), and therefore the phase shifter values of subarray \(m\) for difference beams are set to be \(-\pi mn/4\) for \(n=0,...,11\) and \(\pi(1-mn/4)\) for \(n=12,...,23\), while for sum beams, \(-\pi mn/4\) for \(n=0,...,23\). When multiple training symbols are used, both null-steering direction and phase shifts are rotated by \(\frac{2\pi}{L}\) between every two consecutive symbols. Although the maximal beamforming gain of a difference beam is 3 dB lower than that of a sum beam, multiple difference beams across multiple training symbols overlap in some directions of interest, which can make up for the beamforming gain loss.
# PPDC: A Privacy-Preserving Distinct Counting Scheme for Mobile Sensing
## 3 Privacy-Preserving Distinct Counting Computation
### Privacy-Preserving Distinct Counting Scheme
The security of PPDC will later be formally proved in Theorem 2. Figure 3: An example of determining the 5 th bit in FM sketch \(S\) with the bitwise XOR homomorphic encryption, where the coding scheme defined the length of the bit string \(T(r_{i_{j}})\) is 5. The result shows that the aggregation result is not influenced by the operations of user’s privacy protection. ```
1:\(\{x_{i}^{l}\}\): User \(i\)'s dataset with various elements, \(l\in[0,L_{i}],x_{i}^{l}\in[0,N-1],\;i=1,...,n\);
2:\(h\): A hash function with a \(w\)-bit string output;
3:\(d\): the number of FM sketches;
4:\(M_{a}^{i}\) and \(M_{b}^{i}\): two secret seeds of User \(i\);
5:\(t\in[0,2^{v}-1]\): A public known nonce number;
6:\(q\in\mathbb{N}\): An accuracy controlling parameter. 7:The number of distinct elements in \(\{x_{i}\}\), \(i=1,...,n\). 8:for\(k=1\) to \(d\)do
9:for\(i=1\) to \(n\)do
10: User \(i\): \(\Lambda_{i}\gets h(x_{i})\), \(len(\Lambda_{i})=w\);
11: User \(i\): \(k_{i}=F_{M_{a}^{i},v,q}(t)\oplus F_{M_{b}^{i},v,q}(t)\);
12:endfor
13:for\(j=w\) to \(1\)do
14:for\(i=1\) to \(n\)do
15: User \(i\): \(T(r_{i_{j}})\gets r_{i_{j}}\) in \(\Lambda_{i}\), \(len(T(r_{i_{j}}))=q\);
16: User \(i\): \(\overline{T(r_{i_{j}})}=T(r_{i_{j}})\oplus k_{i}\);
17:endfor
18: Aggregator \(P\): \(\overline{G(j)}=\overline{T(r_{1_{j}})}\oplus...\oplus\overline{T(r_{n_{j}})}\). 19:if\(\overline{G(j)}=\{0\}^{q}\)then
20: continue;
21:else
22: break;
23:endif
24:endfor
25:endfor
26:return\(C(S)=d\cdot\frac{2^{\sum_{k=1}^{d}\frac{Z(S_{k})}{d}-2^{-k}\cdot\sum_{k=1}^{d} \frac{Z(S_{k})}{d}}}{\varphi}\);
```  
**Algorithm 1** Privacy-preserving Distinct Counting Scheme
# A New Compact Octagonal Shape Perfect Metamaterial Absorber for Microwave Applications  
[PERSON] 1,*  
[PERSON] 1,*  
[PERSON] 2,*  
[PERSON] 1,*  
[PERSON] 2,*  
[PERSON] 1,*  
[PERSON] 2,*  
99.99% at 5.48 GHz and 99.92% at 0.865 THz. However, the dimension of the unit cell structure was 36 \(\times\) 36 mm\({}^{2}\). [PERSON] et al. recommended a design structure of 12 \(\times\) 12 mm\({}^{2}\) composite double negative metamaterial for multi-band operation and reported effective medium ratio was 7.44 [16]. On the other hand, the above author analysed double negative characteristics and compactness, but absorption properties were not analysed in their study. [PERSON] et al. [17] proposed multi-band split S-shaped metamaterial structure for absorption analysis, whereas authors obtained maximum 55% absorption. [PERSON] et al. proposed a dual-band multilayer metamaterial absorber in the megahertz region, which were absorptions of 96% in the 4.0-6.0 GHz range because of the irregular thickness of the resistive sheets [18]. A multi-band perfect metamaterial absorber based on spiral showed the absorption of 99.4%, 96.7%, and 99.1% at three resonant frequencies 9.86 GHz, 12.24 GHz, and 15.34 GHz, simultaneously [5]. [PERSON] et al. suggested a dual-band metamaterial absorber in the terahertz area, which was two discrete absorptions of 80.8% and 63.4% near 0.45 and 0.92 THz [19]. A dual-band MA design, fabrication, and characterization was offered by [PERSON] et al. [20]. Their MA exhibited absorption peaks of 85% and 94% at 1.4 THz and 3.0 THz, respectively. Polarization-independent MAs proposed by [PERSON] et al. that performed in the microwave regime. The maximum absorption value was 95.81% at 10.31 GHz [21].  
In this paper, compact octagonal shape perfect metamaterial absorber exhibits dual resonance at X-band. The absorption has been changed by altering the polarization angle of unit cell, thickness of substrate and metallic materials, number of rings, and resistive load. Moreover, the size of the proposed unit cell is 10 \(\times\) 10 mm\({}^{2}\), which is physically smaller than the stated metamaterials unit cells in literature [12; 13; 15; 17; 22; 23; 24]. Moreover, the design structure provides high absorption peaks of 99.64% at 8.08 GHz and 99.95% at 11.41 GHz, respectively. It is observed that the absorption of the proposed design is better than the suggested metamaterials unit cells in [12; 13; 14; 17; 18; 19; 20; 21; 22; 23; 24; 25]. To determine the absorption parameters, the Computer Simulation Technology (CST) microwave studio suite simulator 2015 is used.
# Generation of Ultrafast Optical Pulses via Molecular Modulation in Ambient Air
## 2 Experimental Setup
DM: Dichroic mirror, L1–4: Convex lenses, HM: 532 nm high reflection mirror, BK7: 12 mm thick BK7 plates, ND: Neutral density filters, FWM1,2: The interaction points. **(B)** Energy level diagram for the cascaded Raman sideband generation, \(\omega_{\text{(AS1-2)}}\): anti-Stokes, \(\omega_{\text{p}}\): pump, \(\omega_{\text{(S1-2)}}\): Stokes sidebands. Dashed lines show virtual energy levels.
## 2 Materials and Methods  
### Study Site  
The study area was located near Ketapang, West Kalimantan, Indonesia (Figure 1). The sampled peat dome (34,651 ha) was a deep coastal peatland, which the Pawan River borders on the north, Pesaguhan River on the south, hills on the east, and alluvial soils on the west. The two main rivers flow to the Karimata Strait in the Java Sea. Historical data on rainfall near Ketapang averages 2892 mm per year [27], while the annual temperature averages 27.3 \({}^{\circ}\)C (from 1982 to 2012). The study area has a range of elevation above mean sea level from 10 to 24 m [28].  
The peat forests on this dome had been partly disturbed and exploited for timber (subsistence use) since about 1988. At that time, the access to the dome was opened through road building and started the timber exploitation and forest conversion [29]. The forests are composed of typical tree species in peat swamps, including _Aglaia rubiginosa (Hirn.) Pantel, Dactylocladus stenostachys (Oliv.)_, and _Dyera costulata (Hook.f.)_., _Palauium spp_. [23]. Three relatively undisturbed peat swamp forests/PSF and three logged peat swamp forests/LPSF) were selected in this study. Tree canopy of 30 m heights dominates PSF, and that of 15 m dominates the LPSF sites.  
In addition to the forests, three early seral dominated by grasses and ferns (ES) and three smallholder oil palm estates (OP) were sampled. The OP estates were in close proximity to the other land cover types. The early seral sites had been logged in the past. They had been burnt several times, enabling ferns (e.g., _Stenchlaena plaustris_ and _Blechnum indicium_) and grasses (e.g., _Themeda triandra_ and _Andropogon gerardii_) [31; 32] to dominate. Early seral sites were first formed from the logged forests that were initially logged and burned in 1994 (Table 1). The three sampled oil palm (_Elaeis guinensis_) estates were three, four, and five years old. These estates were established on previously early seral sites that  
Figure 1: Study sites (white markers) within a deep peat dome near Ketapang, West Kalimantan, Indonesia. Roads (black line) cross-cutting the deep peat dome (dark brown). The peat area was delineated by [30] to represent a dome-shaped peatland between Pawan River on the north and Pesaguhan River on the south. Grey areas represent the non-peat areas. Adapted by permission from [Springer Nature Customer Service Centre GmbH]: [Springer Nature] [Mitigation & Adaptation Strategies for Global Change] (Land cover changes reduce net primary production in tropical coastal peatlands of West Kalimantan, Indonesia, [PERSON] et al.), [COPYRIGHT] (2018) [23].  
had been cleared and canalized around their boundaries. All sites occurred near the center of the peat dome.
# Study and Investigation on 5G Technology: A Systematic Review
## 5 Description of Novel 5G Features over 4G
### Mobile Edge Computing  
Mobile Edge Computing (MEC) [24]: MEC is an extended version of cloud computing that brings cloud resources closer to the end-user. When we talk about computing, the very first thing that comes to our mind is cloud computing. Cloud computing is a very famous technology that offers many services to end-user. Still, cloud computing has many drawbacks. The services available in the cloud are too far from end-users that create latency, and cloud user needs to download the complete application before use, which also increases the burden to the device [86]. MEC creates an edge between the end-user and cloud server, bringing cloud computing closer to the end-user. Now, all the services, namely, video conferencing, virtual software, etc., are offered by this edge that improves cloud computing performance. Another essential feature of MEC is that the application is split into two parts, which, first one is available at cloud server, and the second is at the user's device. Therefore, the user need not download the complete application on his device that increases the performance of the end user's device. Furthermore, MEC provides cloud services at very low latency and less bandwidth. In [23,87], the author's investigation proved that successful deployment of MEC in 5G network increases the overall performance of 5G architecture. Graphical differentiation between cloud computing and mobile edge computing is presented in Figure 9.  
Figure 8: Pictorial Representation of communication with and without using beamforming.  
Figure 9: Pictorial representation of cloud computing vs. mobile edge computing.
# Handover-Driven Interference Management for Co-Channel Deployment of Femto- and Macro-Cells
## 6 Performance Evaluation
### Evaluation Model
#### 6.1.2 Channel Model  
The path losses of a user to BSs are determined based on the ITU and COST231, models which are described as [31; 32]:  
* Macrocell user to macro BS (outdoor link): \[h=10^{4.9}\left(\frac{r}{1000}\right)^{4}f^{3}10^{S/10}\] (13)
* Macrocell user to femto BS, femtocell user to macro BS (outdoor-to-indoor or indoor-to-outdoor link): \[h=10^{4.9}\left(\frac{r}{1000}\right)^{4}f^{3}10^{S/10}10^{(L_{i}+L_{e})/10}\] (14)
* Femtocell user to femto BS (indoor link): \[h=10^{3}r^{3.7}10^{S/10}10^{L_{i}/10}\] (15)  
where \(r\) is the transmitter-receiver separation distance in meters; \(f\) is the carrier frequency in MHz; \(S\) is the log-normal shadowing factor with a standard deviation of 8 dB; and \(L_{i}\) and \(L_{e}\) are internal and external wall losses that are set to 2 and 7 dB, respectively, in the simulation. The frequency selective multipath channel is modeled to consist of six independent Rayleigh multipaths, with an exponentially decaying profile. A maximum doppler spread of 30 Hz is assumed.
## 4 Results and Discussions  
Method to Measure and Compare Packet Processing Time of Software Router with Prototype Hardware Router CISCO 2801  
The most complicated and interesting component of nodal delay is the queuing delay, \(d_{queue}\). In fact, queuing delay is important and interesting in computer networking Another interesting scientific task is to develop a method of estimating the packets processing delay by only router (\(d_{proc.}\) + \(d_{queue}\)) without taking into account IP Fragmentation, transmission and propagation delays. However, at present there are no practical methods of measuring the delay of packets introduced only by the router (\(d_{proc.}\) + \(d_{queue}\)). Existing router delay testing methods are based on formula 1 and are defined as the sum of all types of delays (\(d_{\textit{frng.}}\) + \(d_{\textit{proc.}}\) + \(d_{queue}\) + \(d_{\textit{trans.}}\) + \(d_{\textit{prop.}}\)). These methods do not make it possible to accurately assess the router's performance in terms of average packet processing time. Moreover, this method will be a good practical tool for comparing the performance of software and hardware routers.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Celeron J1800[2 Cores[2.41 GHz]RAM 4 GB** \\ \hline
**Series** & **Packet Size [byte]** & **Average delay [ms]** \\ \hline  
\hline \end{tabular}
\end{table}
Table 1: Investigation of the average time of the total nodal delay by the software router on the servers of different performance.  
In the following we will enter the designations Tav.router as a packet processing time by the router:  
\[T_{av.router}=d_{proc.}+d_{queue}. \tag{2}\]  
Packet processing time \(T_{av.router}\) of a host (i.e., router) is the time elapsed between the arrival of a packet in the router (input queue of the NIC, i.e., the data-link layer of the TCP/IP protocol stack) and the time the packet is processed at the application layer (\(d_{proc.}+d_{queue}\)).  
The idea of the experiment is to create a software router as a prototype Cisco 2801 hardware router. To confirm or simplify the hypothesis that the developed software router provides a cost-efficient alternative to expensive, special hardware routers. To do this, it is necessary to select a server machine for the software router, the average time of processing packets which should be identical to the average time of processing packets by hardware routers and then to compare their prices. For the experiment the computer on which the traffic generator is developed is chosen. This generator can send and receive traffic at the same time, and thus calculate the difference between sending and receiving times each packet. This allows the generator to calculate the time of packets transit over the network and processing on the router.
# Efficient Data Delivery Scheme for Large-Scale Microservices in Distributed Cloud Environment
## 4 Simulation Scenarios and Results
### Assessment of Varying Event Channel Subscription Correlations Using a Multi-Modal Model  
Inspired by [PERSON] et al. (2019), a multi-modal event channel subscription model was used to examine how the correlations in event subscriptions among brokers affected the efficiency with which data was delivered. From the coordinate pool, 100 brokers were instantiated in this simulation scenario, with their locations chosen at random. Furthermore, a 1000-topic pool was developed to allocate 100 event channel topics for each broker's traffic-forwarding workloads. Specifically, edge brokers' event channel subscription correlations were developed using the multi-modal model to measure their event notification performances as follows:  
* There were \(n\) categories of the event channel space
* Each broker randomly and uniformly chose \(p\) categories from  
Figure 5: Effect of varying data locality rates on the average delay.  
Figure 6: Effect of differing data locality rates on forwarding traffic.  
* There were fixed values for the ratio of \(p/n\), which were 0.1 and 0.2. Various values of \(p\) and \(n\) were used to change the correlation levels of the event channel; in particular, with \(p=1\), \(n=[10,5]\) and with \(p=5\), \(n=[50,25]\)
* From these \(p\) categories, one hundred topics of the event channel were selected for each broker, which were distributed by following [PERSON]'s law  
Table 2 and Figure 7 show the average forwarding traffic among brokers and average delivery latencies of the compared schemes in the multi-modal model scenario, respectively. In general, both ADL and AFT values of the three algorithms decreased in the experiment when event channel correlations among brokers increased (from the case of \([p=5;n=25]\) to \([p=1;n=10]\)). The PubSubCoord-like scheme experienced the highest (worst) ADL and AFT values in this evaluation setting, as this approach does not consider clustering edge brokers with high event channel subscription correlations and proximity geolocations to reduce the delivery latency and forward traffic, as in the proposed approach. Although the proposed scheme achieved the best AFT on average, it was only slightly better than the PSECTO scheme, at 8.12% versus 8.39%, respectively. As expected, the PSECTO approach achieved the lowest ADL values in the simulation setup, as it takes advantage of brokers' event channel subscription correlations and proximity locations and uses direct connections between brokers for transferring data. On the other hand, overlay networks for data distribution require considerable work from the brokers, which could lead to scalability issues.
# Prediction of Vacant Parking Spaces in Multiple Parking Lots: A DWT-ConvGRU-BRC Model
## 4 Experimental Results  
### Experimental Setup and Evaluation Indicators  
In our experiments, we select 60% of the data as the training set, 20% as the validation set, and the rest as the test set. We normalize the denoised data using Equation (10) and then slice it into the model using single-step moving window data of length 10. For training, the gradient descent optimization algorithm is the [PERSON] [44] algorithm, the learning rate is 0.01, the loss function is the MAE, the epoch size is 32, the batch size is 32, and the number of ConvGRU layers (k) is 3. The numbers of VPSs after 5, 15, 30, 45, and 60 min are predicted accordingly. To avoid contingency, each prediction task was independently repeated 30 times, and the mean values were taken as the results. The DWT-ConvGRU-BRC model is implemented using PyTorch version 1.11.0, and the experimental equipment includes a 12 th Gen Intel(R) Core(TM) i5-12600 KF processor and an NVIDIA GeForce GTX 30600 Ti GPU with 16 GB memory.  
\[x^{*}=\frac{x-\text{min}}{\text{max}-\text{min}} \tag{10}\]  
where max and min represent the maximum and minimum values of the sample data, respectively, and max-min represents the range.  
We use the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) to measure the accuracy of the predicted values. All three evaluation metrics have a range of [0, +\(\infty\)) and are equal to 0 when the predicted value exactly matches the true value, i.e., a perfect model; the larger the error is, the larger the value. The definitions are shown in Equations (11)-(13).  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}|\overset{\wedge}{y_{i}}-y_{i}| \tag{11}\]  
\[MAPE=\frac{100\%}{n}\sum_{i=1}^{n}\frac{|\overset{\wedge}{y_{i}}-y_{i}|}{y_{i}} \tag{12}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(\overset{\wedge}{y_{i}}-y_{i}\right) ^{2}} \tag{13}\]  
where \(y_{i}\) denotes the actual VPSs, \(\overset{\wedge}{y_{i}}\) denotes the predicted VPSs, and \(n\) denotes the time step.
# Influence of Printing Substrate on Quality of Line and Text Reproduction in Flexography
## 4 Results and Discussion
### Analysis of Text Deformation
The analysis of the results shows that the relative raggedness of the text (percentage deviations) on uncoated paper increases as the area of the evaluated element decreases, i.e., with a decrease in text size (from 8 pt to 6 pt) and with the decrease in stroke length of the characters (character \"s\" has a longer stroke length than character \"r\"). The same trend is visible also on the other two investigated printing substrates. This analysis confirms why quality reproduction of a smaller size text is extremely difficult to achieve, particularly in absorbent printing substrates. Additional comparison of percentage deviations between the text and lines will give an insight into the general behavior of fine elements during the printing process on three types of printing substrates. Figure 7 compares the results of percentage deviations and perimeter of 85 um lines and 8 pt text size from the target value. Line diagrams for each parameter, area and perimeter show that percentage deviations of the line and text follow the same trends. There is more data dissemination in the negative shape. In most cases, the deviation is up to 10%, which means that the reproduction of fine elements can be described using common positive and negative deviations for all three types of printing substrates. Therefore, the deformation of fine elements is directly connected to the length of stroke, in both the printouts of line and text. Raggedness (percentage perimeter deviation) is still somewhat bigger in lines than in text characters, especially in the negative shape. This can be connected with characters which have curved strokes, and for that reason compensate ink spreads more easily. Figure 6: Deviation of area and perimeter (%) for text characters “r” and “s” of 8 pt size printed in positive and negative shape on three different printing substrates. The text with the most edge noise along the stroke (edge raggedness) is printed on uncoated paper which is visually noticeable (Figure 8, upper images), and it is directly connected to the results of perimeter measurement. Excessive noise at the edges of the strokes of the characters makes the text blurry and unclear, reducing its visibility and legibility. Figure 8: Visual comparison of edge degradation for text size of 8 pt printed in positive and negative shapes on three different printing substrates. Figure 7: The comparison of area and perimeter deviation (%) for character “s” 8 pt size and line 85 μm thickness printed in positive and negative shape on three different printing substrates.