esa-sceva/satcom-chunk-collection huggingface dataset:
# Design, Challenges and Developments for 5G Massive MIMO Antenna Systems at Sub 6-GHz Band: A Review
## 3 5G Antennas as a Candidate for Massive MIMO Technique at Sub-6 GHz
### Single Elements
#### 3.1.1 3D-Model Antennas
For the demonstration, a wideband dipole antenna with dual polarization was implemented. According to the findings of the measurements, the proposed antenna was 48.7% (1.66-2) [92]. A radiating and feeding patch created a new low-profile dual-polarized patch antenna for 5G base stations. The radiation field is excited into dual polarization when a coupled feed field is present. In order to achieve more accurate impedance matching, the edges and corners of these two patches are routinely cut. The processing cost is reduced, and the structure is greatly simplified because only one substrate stands between the two patches. The patch antenna is only 8.7 mm in thickness and measures 32 mm on each  
Figure 20: Dual-polarized cross-dipole antenna with fan-shaped slots and \(\Gamma\)-shaped feeders on the front and rectangular patches on the back [89]. side. Its operating frequency range is from 3.3 to 3.7 GHz. In the operating band, the connection isolation coefficients \(|\)S11\(|\) and \(|\)S22\(|\) are less than 15 dB, the connection isolation coefficients \(|\)S21\(|\) are less than 19 dB, and the cross polarization discrimination (XPD) is greater than 27 dB. In addition, a (1 \(\times\) 3) antenna array was designed using this antenna element as the basis for the design. The antenna array could attain the reflection coefficients of \(|\)S11\(|\) and \(|\)S22\(|\). In the frequency range of 3.3 to 3.7 GHz, the S11 return loss was below \(-\)10 dB and port isolation was less than \(-\)20 dB. The radiation pattern was both symmetrical and stable, and the H-plane had a half width that ranged from 65 degrees to 76 degrees. The use of this antenna in 5G massive multiple-input multiple-output (MIMO) applications has been evaluated and found to have promising potential [93].
###### Abstract
Moreover, based on measured activities and environmental data, the system must inform the user possible dangers and record the forgetfulness of leaving a specific space without completing the work they were doing. Furthermore, these processes in the system should be performed without user intervention. Many studies on smart home solutions are being conducted [6], but most of these have yet to address these problems accurately. Accordingly, to address these problems, we propose a self-organized device network structure based on edge computing that can recognize the user and location in real-time and determine whether the user has performed activities successfully or abnormally. Furthermore, the proposed system can notify the user when he or she is in a dangerous situation or forgets to do something. We developed three types of smart devices (wearable, tag, and stationary) that can be quickly equipped by users and home appliances, such that these devices have the advantage of being seamlessly applicable to typical existing houses. All devices in this system use a Bluetooth Low Energy (BLE) protocol, which enables the expansion of the wireless sensor network (WSN). Although other useful communication technologies for building IoT systems exist, such as ZigBee and LoRa, we used BLE because of its high data rate and transmission distance covering one indoor space. Furthermore, BLE has the advantage of high accessibility because most smartphones support BLE communication. The smart devices developed in this study can be configured through the smartphone BLE app according to the user's requirements. For example, a user can quickly set the appliance type of a tag device using a smartphone. By exploiting edge computing, it is possible to quickly analyze user activities by unit space and determine whether the activities were finished successfully or abnormally. The remainder of this paper is organized as follows. In Section 2, we review previous related studies. In Section 3, we propose a conceptual model for the proposed device network. In Section 4, we present the design details, and, in Section 5, we present a service scenario. In Section 6, we evaluate the implementation and performance for the validity and use of the system. Finally, in Section 7, we offer concluding remarks and explain the direction of future work.
# Real-Time Method and Implementation of Head-Wave Extraction for Ultrasonic Imaging While Drilling
## 4 Discussion
### Digital Filtering  
A finite impulse response (FIR) filter is used to filter out thermal and quantization noise in the circuit. FIR filtering is an efficient way to calculate the convolution of discrete signals and filter coefficients in the time domain. The calculation expression is shown in Equation (5), where \(x(i)\) is the original discrete signal, \(h(i)\) is the filter coefficient, \(y(i)\) is the filtered discrete signal, and \(N\) is the filter order.  
\[y(i)=h(i)*x(i)=\sum_{k=0}^{N-1}h(k)x(i-k) \tag{5}\]  
In the present work, we used a single-rate type band-pass filter with a cut-off frequency of 180-320 kHz, an order of 46, and filter coefficients after rounding. Equation (5) shows that the implementation of the FIR filter involves the processes of shifting, multiplication, and accumulation, suggesting that its implementation in FPGA can be mainly divided into the three stages of the pipeline. The calculation process is shown in Figure 7, where the arrow represents the direction of the data flow, the black hollow circle represents \(x(i)\), the black solid circle represents \(h(i)\), the red hollow circle reflects the product-term of \(x(i)\) and \(h(i)\), and the red solid circle represents the output of the filter \(y(i)\). Since the filtering coefficient \(h(i)\) is symmetric, the \(y(i)\) calculation can be simplified as follows: first, the input \(x(i)\) is shifted by the shift register group in FPGA, and the original shifted signals are added symmetrically. Then, the calculation result of the adder output is fed into the multiplier, where it is multiplied by \(h(i)\). Finally, the product-term is added into the accumulator to calculate the filtering output.  
Figure 6: Structure of the front-end control program.
## 4 Discussion
### Geometry-Based Model for the Transport Distance  
The geometric model proposed here, as well as predicting rainfall-induced soil erosion, can also incorporate previous rainfall-soil erosion relationships to estimate the maximum transport distance. In order to assess the predictive capability of the model over a wider data range, 147 random instances of cumulative rainfall and maximum transport distance were identified from physical experiments (linear interpolation produces the samples between neighbor cumulative rainfall events and the samples between corresponding maximum transport distance in the meantime). Figure 12a displays the random samples against the model prediction. Figure 12b shows the relationship between the random samples and the model prediction and presents the associated error (RMSE = 4.7).  
In our study, the flexible and straightforward model depends on three inputs--cumulative rainfall, slope angle, and debris angle. The sediment angle may be uncertain and easily affected by rainfall (Figure 13). The histogram of sediment angles is as shown in Figure 14. Most sediment angles were found to range between 18\({}^{\circ}\) and 22\({}^{\circ}\) (very close to the residual friction angle of 21\({}^{\circ}\)), which means that a coefficient, such as residual friction angle, could arguably be used as a substitute. The significance of using a coefficient is that it can reduce the inputs to cumulative rainfall and slope angle, which could be more straightforward.  
Figure 12: Error analysis of model prediction: (**a**) random samples _versus_ model prediction (\(n=147\)); (**b**) linear relationship between model prediction and random samples (RMSE = 4.7) (\(n=147\)).  
Figure 13: Cumulative rainfall with sediment angle. (**a**) Sediment angle with cumulative rainfall (\(n=29\)) based on physical tests; (**b**) sediment angle with cumulative rainfall (\(n=147\)) based on random field.
# eHealth Integrity Model Based on Permissioned Blockchain +
## 3 Discussion
### Privacy  
In the BEIM, a blockchain is permissioned, i.e., it is not available publicly to everyone, and most of it is encrypted. The two privacy requirements, i.e., Anonymity to Third Parties and Confidentiality of Transaction Content, must be met. These requirements are met by usage transaction content encryption and the usage of covert identifiers.  
Integrity verification requires access to a complete blockchain. More precisely, access is required to prove that a document or a log entry is missing. The BEIM uses an asymmetric key pair \((Pk_{ID},Dk_{ID})\) for the encryption of transactions related to one user. Public key \(Pk_{ID}\) is widely available for everyone, but \(Dk_{ID}\) is only given to verifiers (and to entities that add Subtype \(b\)-_remove_ transactions, which also verify the existence of records they remove). A verifier must have access rights to the given EHR or one of its parts to verify it. Additionally, the verifier is given blockchain access rights that include a right to access a service that translates a public ID to a covert ID, and a right to access decryption key \(Dk_{ID}\). These additional rights enable the verifier to only obtain information about the number of user records and their hashes, which are a subset of information that is already available to the verifier.  
The transaction's creator obtains the transaction sequence number from an eHealth system service, so they do not require right-to-access decryption key \(Dk_{ID}\). Therefore, the creator does not have knowledge of the transaction content, but only has access to a right to service that translates a public ID to a covert ID. The user that removes the record and adds a Subtype \(b\) transaction must have  
Figure 3: Examples of different cases during integrity verification.  
the right to verify if the removed record was added to the blockchain earlier. However, this step can be omitted when earlier verification was performed by another user.  
When one of the nodes becomes dishonest, it is removed by a consensus mechanism in a blockchain. However, a dishonest node still has access to a historical blockchain after exclusion, and can make it public. In such situations, transactions in a blockchain are viewed as a set of random encrypted transactions with random identification strings. An adversary from a blockchain can only learn how many entries (total medical records, access-rights entries, and log entries) were added and deleted to the EHR with a specific covert ID. However, as long as the covert ID remains secret, it is not possible to link a covert ID with a patient identifier. Optionally, in order to increase security, the covert ID can change over time. This is the same situation as in cryptocurrencies, where only a public key (a unique user-wallet ID) is known, but the relation between a public key and user identity is secret.
# Multiple Sensors Based Hand Motion Recognition Using Adaptive Directed Acyclic Graph
## 2 Hand Motion Recognition Method
### Multiclass Support Vector Machines  
Support Vector Machine (SVM) is a novel large margin classifiers used for classification and regression. SVM maps vectors onto a much higher dimensional space, and sets up a maximum margin hyperplane that divides clusters of vectors. New objects are then located in the same space and were recognized to belong to a class based on which area they fall in. This method can minimize the upper bound of generalization error and provide excellent generalization ability, so it is effective in a high dimensional space and is compatible with different kernel functions specified for the decision function, with common kernels well provided and the freedom to specify custom kernels. More details about the theory of SVM can be referred in [16; 17; 18].  
Most of the problems encountered in reality require the discrimination for more than two categories, and how to effectively extend SVM for multiclass classification is still an ongoing research issue. To solve these problems, a number of classification models were proposed, such as one-versus-rest approach [19], one-versus-one approach [20], direct multiclass SVM [21; 22], and decision directed acyclic graph SVM (DAGSVM) [23]. In addition to these approaches above, some variants such as reordering DAGSVM [24], binary decision tree SVM [25], and error correcting codes SVM [26] have appeared in the literature. These novel methods have a wide range of applications including speech recognition, fault diagnosis, system discrimination, financial engineering and bioinformatics, etc. Table 1 shows the common multiclass SVM methods, and their computational complexity and advantages and disadvantages are compared [27]. In this paper, we use an Adaptive DAG (ADAG) [28]. This method improves the accuracy and reliability of DAGSVM and avoids the error accumulation, especially in the case of data sets with a large number of classes. A more detailed description of DAGSVM and ADAG is summarized as follows.
## 2 Materials and Methods
### Energy Balance Closure  
The relationship between available energy (\(\mathrm{R_{n}-G}\)) and turbulent fluxes (H + LE) is often used as an indicator of the accuracy of estimates by the eddy covariance method ([PERSON], 2018). Failure to close the energy balance is widely documented in the literature, and values of up to 30% are considered reliable ([PERSON], 2018). This study used H and LE fluxes with high-quality (0 and 1 indicators) and unfilled gaps to calculate energy balance closure only when all four components (H, LE, \(\mathrm{R_{n}}\), and G) were available. The slope of the linear regression between (\(\mathrm{R_{n}-G}\)) and (H + LE) for the evaluated period was 0.75 (y = 0.75x + 19, \(\mathrm{R^{2}}\) = 0.88), meaning the non-closure of the energy balance was roughly 25%, as analyzed and discussed by [PERSON] et al. (2018) for the same experimental site.
# Machine Learning Algorithms: An Experimental Evaluation for Decision Support Systems
## 3 Machine Learning Algorithms and Techniques
### Mean Shift  
Mean Shift is a clustering unsupervised algorithm whose purpose is to discover blobs in a smooth density of samples [28]. It is a centroid-based algorithm that works by updating candidates for centroids to be the mean of the points within a given region, also called bandwidth. These candidates are then filtered in a post-processing stage to eliminate near-duplicates to form the final set of centroids. In contrast to K-Means, there is no need to choose the number of clusters.
## 2 Time Series Forecasting for Energy Reduction
### Deep Learning for Time Series
#### 2.4.1 Recurrent Neural Networks  
RNNs are neural networks that contain recurrent layers so they can learn from sequential inputs. RNNs are very flexible and capable of processing all types of sequences. Figure 4 presents a folded and unfolded representation of an RNN. In a simple RNN, the state output \(h\) is just a copy of the output. Given an input sequence \(\{x_{1},x_{2},\ldots,x_{t}\}\), with \(x_{i}\in R^{n}\), an RNN computes \(h_{t}\in R^{m}\) for every time step \(t\) and defines the recurrent function as [19]:  
\[h_{t}=F(h_{t-1},x_{t}), \tag{7}\]  
where \(h_{t}\) is the current hidden state vector, \(h_{t-1}\) is the previous hidden state, and \(x_{t}\) the input vector.  
During backpropagation, RNNs suffer from the vanishing gradient problem. That is, the gradient becomes smaller in some layers as the size of the sequence increases, and therefore, RNN fails in remembering long sequences.
## 7 Results and Analysis  
In this section, results and analysis are presented based on the real measurements using our V2X testbed setup. Considering the spacial limitations of the used 5G licenses, as well as the non-LOS experimental scenario, the 5G range is limited to approximately 350 m. Similarly, using the maximum transmit power in our experimental scenario, the range for DSRC and C-V2X PC5 is approximately 220 m and 400 m, respectively. In order to evaluate the performance of the proposed hybrid V2X technology selection algorithm in a realistic manner, the transmit power values for DSRC and C-V2X PC5 have been reduced in order to have distinct coverage range for the three technologies. More specifically, with10 dBm and 12 dBm transmit power for DSRC and C-V2X PC5, the effective transmission range reduces to approximately 125 m (denoted as \(D_{thr1}\)) for DSRC and 250 m (denoted as \(D_{thr2}\)) for C-V2X PC5. The reliability of the proposed hybrid V2X technology selection algorithm can vary a lot depending of the chosen \(L_{thr}\). \(L_{thr}\) of 20 ms is used during the experimentation. These threshold values are derived considering the spacial limitations of the used 5G licenses, as well as on the basis of studies [5; 30; 31] of typical ranges and latencies. The speed threshold, \(S_{thr}\), is set to 100 Km/h for the tests, however, it can be adapted depending on the traffic dynamics. Similarly, the CAM density threshold, \(Dense_{thr}\), is set to 100 CAMs per second for experimentation as only two V2X nodes are considered. This depends on the available bandwidth and the number of V2X nodes in the vicinity. The time sliding window of five seconds is used, which means that the statistics are observed for the last five seconds before hybrid V2X technology selection decision is made for every CAM/DENM transmission. The lower value of the time sliding window can lead to less accurate analysis of the environment, which can lead to inefficient V2X technology selection decision making. However, using a higher value also comes with a cost. It can lead to a higher computational load and potentially may not represent the accurate and up-to-date traffic dynamics. In the next subsections, the KPIs have been plotted for the individual considered technologies without intelligence and the proposed intelligent hybrid V2X technology selection algorithm. A time accuracy of 1 ms is ensured in accordance with the specifications of the GNSS devices used for time synchronization. The performance of the proposed hybrid V2X technology selection algorithm is analyzed for CAM only as well as joint CAM and DENM transmission scenarios. Experimental statistics are shown in Table 4.
## Materials and Methods
### Use of Ceramic Waste  
A local sanitary wares production facility provided the waste glazed ceramics used in this study. The ceramics were used to prepare three replacement products: ceramic waste coarse aggregate (CWCA) to partially replace 4.75 mm aggregates, ceramic waste fine aggregate (CWFA) to partially replace sand, as well as ceramic waste powder (CWP) to partially replace cement. Large pieces of ceramics were spun in a Los Angeles (LA) Abrasion apparatus with 12 steel balls for repetitions of 1000 revolutions. The resulting product was sieved through a series of standard sieves with diameters ranging from 9.5 mm to 0.075 mm to obtain the replacement products. Ceramic aggregates passing the 9.5 mm sieve and retained on the 4.75 mm sieve was classified as CWCA. All ceramic aggregates passing the 4.75 mm sieve and retained on any sieve up to and including the 0.075 mm sieve was classified as CWFA. Finally, ceramic aggregates passing the 0.075 mm sieve was classified as CWP. The effect of each ceramic additive was investigated individually prior to their use in the hybrid mixes to get a clear idea of how each of the additives affected the SCC's behavior. The physical properties of the CWCA,  
Figure 2: Photographs showing the tests performed on hardened SCC samples: (**a**) Tested SCC samples; (**b**) Sample being tested for its compressive strength; (**c**) Sulfur-capped samples ready to be tested.  
CWFA, and CWP used in the study are shown in Table 1. The bulk densities, specific gravities, and absorption of CWCA were obtained in accordance with ASTM C127 [21]. The absorption of CWFA and CWP were obtained in accordance with ASTM C128 [22]. The fineness of CWP was obtained in accordance with ASTM C184 [23].
## 3 Time Analytics Model in Photolithography Equipment
### Typical and Atypical Time-Increased Wafers  
We have defined the types of time-increased wafers outside the control range as two types: typical and atypical. Figure 3a shows the wafer loading time of each wafer in chronological sequence. Durations are clustering around 7 s (red dots). The same duration generally indicates the same cause. We have defined clustered time-increased wafers as a typical form. Contrarily, time-increased wafers that do not form clusters have been defined as an atypical form. The atypical form has characteristics of large dispersion and low frequency of occurrence. The ratio of atypical time-increased wafers is 9.1% of the total wafers, and some atypical time-increased wafers also have the same duration as typical time-increased wafers, resulting in a decrease in the accuracy of the classification. The study has assumed that external factors are likely to be atypical. The transport of materials (wafer and photomask) is one of the external factors. The photolithography process requires the transport of 4000 wafers and 100 photomasks per day. There is a possibility that supply will be delayed due to several reasons during transportation. As a result of analyzing ten signals related to wafer transport, it could be defined as a polynomial with three independent variables. Equation (2) shows the delay of the transporting wafer from the outside, and  
Figure 2: Time analytics model flow chart. Time classification is used to construct a new cause classification algorithm.  
Equation (3) shows the delay of transporting the wafer to the outside. The constants T1 and T2 are the minimum times of the difference between the two signal occurrence times of each equation. In cases when the interval between the occurrences of the two signals is greater than the minimum time (T1, T2), the transport delay of the wafer can be calculated. In addition, transportation delays could be classified by analyzing the transport signal of the photomask. Equation (4) shows the delay in receiving the photomask. As a result of the experiment, the time-increased wafer due to the delay in material supply, which is an external factor, had an atypical form, and by classifying it, the ratio of unclassified time-increased wafers could be lowered from 9.1% to 1.4%. The classification of material supply delays reduced 85% of atypical time-increased wafers and was able to increase the accuracy of typical data. In addition, by accumulating data on unclassified atypical time-increased wafers, it was possible to detect clusters with a low frequency of occurrence.  
\[\text{Wafer receive delay}=(\text{Signal A}-\text{Signal B})-\text{ T}_{1} \tag{2}\] \[\text{Wafer give delay}=(\text{Signal C}_{1}-\text{Signal C}_{2})- \text{T}_{2}\] (3) \[\text{Reticle receive delay}=(\text{Signal D}-\text{Signal E})- \text{T}_{3} \tag{4}\]