esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
For this reason, the processing of this type of information with specific objectives becomes an important challenge [12], in which numerous algorithmic techniques will concur, to form a methodology capable of extracting the valuable knowledge. Most studies on land use are mainly oriented to classification tasks. When we use a data source such as the expressions of a language, it is necessary to propose a methodology that starts with data collection, followed by pre-processing and, finally, classification. The fundamental reason is that the expressions in each language and country--and even region--are different. For example, in order to carry out the present study, it was necessary to create a corpus and a dictionary for the Spanish language and for expressions specific to Peru. This paper proposes a methodology for the identification of land uses using Natural Language Processing (NLP) from the contents of the popular social network Twitter. It will be approached by identifying keywords with linguistic patterns from the text, and the geographical coordinates associated with the publication. The methodology is composed of the following stages: data collection, corpus and dictionary creation, pre-processing, feature extraction, learning, prediction, and representation. At each stage, context-specific innovations are introduced to deal with data from South America and, in particular, the city of Arequipa, Peru. The objective is to identify the five main land uses: residential, commercial, institutional-governmental, industrial-offices, and unbuilt land. Within the framework of urban planning and sustainable urban management, the methodology proposed in this study contributes to the optimization of the identification techniques applied for the updating of land use cadastres, since the results achieved an accuracy of about 90%, which motivates its application in the real context. In addition, this model would allow the identification of land use categories at a more detailed level,in situations such as a complex/mixed distribution building based on the amount of data collected. Finally, the methodology makes land use information available in a more up-to-date and, above all, much less costly way. The document is organized as follows: first, a review of the recent scientific literature related to the use of machine learning techniques applied to the categorization of land use, and specifically involving data from social networks, followed by an overview of the NLP-based methodology; next, a description of the application context in the city of Arequipa, containing examples of the results provided by the main phases of the methodology, together with the results achieved in comparison to several models; then, visual examples are presented to illustrate the contrast between results from our approach and what is registered in the cadastre; finally, conclusions and future work.
# A Low-Profile Dielectric Resonator Antenna Array for OAM Waves Generation at 5G NR Bands
## 2 Design Methodology
The dimensions of all parameters are well optimized to resonate at 3.2 to 3.66 GHz, and the details of the parameters are shown in Table 1.  
Figure 1: Proposed DRA. (**a**) Perspective view; (**b**) back view; (**c**) ground plane. Figure 2: Feeding network. (**a**) Structure of the feeding network; (**b**) location of the ports. The fabrication and measurement processes took place once all the simulation results displayed good agreement with the design goal and the antenna's requirements. The proposed DRA was fed with a 50 \(\Omega\) SMA connector. An SMA connector was used because it offers the best impedance matching, given that it can be adjusted to any position. The fabricated prototype of the proposed DRA is shown in Figure 4.
# Performance Analysis of Thread Block Schedulers in GPGPU and Its Implications
## 2 GPGPU and Thread Block Model  
As SM adopts the SIMT (Single Instruction Multiple Thread) model, it executes the same instruction for multiple threads simultaneously [13]. The maximum number of threads that can be executed per SM is generally 2048. The thread block scheduler should manage the allowable number of threads per SM not to exceed this limit while allocating thread blocks to SM [14]. Meanwhile, a series of threads that should be executed simultaneously within the same hardware unit are called _Warp_, which can execute up to 32 threads simultaneously. Each thread block consists of at least one Warp, and whenever the number of threads constituting a Warp exceeds 32, the number of Warps increases by 1. The number of Warps that can be executed at the same time depends on the number of each computing unit in SM [15]. From this point of view, SM does not execute all threads in the thread blocks simultaneously, but Warps for the given thread block are allocated to the computing units and then the threads in that Warp are executed simultaneously.  
When scheduling thread blocks, not only the number of threads per SM but also the number of Warps should be considered. That is, when allocating thread blocks to SM, the thread block scheduler should monitor the utilization of each type of resources in SM and manage them so that SM does not exceed allowable resource usage. Specifically, when performing thread block scheduling, the following types of resources should be monitored.  
* Registers and shared memory
* Threads and thread block Information
* Hardware Warp information  
Thread blocks belonging to the same GPGPU task have the same resource demands, but the resource requirements are varied as the thread blocks are from different tasks. Due to this reason, the thread block scheduler should monitor and manage the usage of each resource type in each SM. For example, even though the resource utilization of SM is not high, if the resource type to be used is already in use, Warps that have not yet been assigned the resource should wait. This is called _Stalled Warp_, which is the main reason of GPGPU's performance degradations. In addition to stalls by computing units, stalls due to memory references also cause serious performance degradation.  
When the shared memory is used between threads belonging to the same thread block, the delay is up to 100 cycles as the number of threads increases. In the case of global memory sharing across threads in different SMs, the delay becomes hundreds of cycles or more [16; 17; 18]. The two types of memory have a tradeoff between latency and capacity so those should be deliberately utilized considering data input size and copy overhead [19; 20].
# High-Accuracy Calibration Based on Linearity Adjustment for Eddy Current Displacement Sensor
## 3 Calibration Method Based on Linearity Adjustment Model
### Zero-Scale and Full-Scale Adjustment Function  
After mid-scale adjustment, \(u_{m}\) will be adjusted at the zero-scale, then at the full-scale, which are similar to the mid-scale adjustment process except the weight function.  
In the process of zero-scale adjustment, the peak of adjustment amplitude is set at \(\tilde{x}=0\). Thus, the standard deviation \(\sigma_{x}=5.33\) can be calculated of the displacement series \(x=0,0.1,0.2,\cdots,5.9,6\) mm in the case of half-Gaussian. Then, the weight coefficient \(\mu_{i}\) can be expressed as:  
\[\mu(x)=0.07\cdot e^{-0.018x^{2}}. \tag{15}\]  
The zero-scale adjustment function \(g_{z}\) can be expressed as:  
\[g_{z}:u_{z}=sgn(\sum_{i=1}^{n}U_{i}\alpha_{i}K(u_{mi},u_{mj})+b), \tag{16}\]  
where \(u_{z}\) is the output voltage, which is adjusted by the zero-scale adjustment function.  
Then, in the process of full-scale adjustment, the peak of adjustment amplitude is set at \(\tilde{x}=6\). Thus, the standard deviation \(\sigma_{x}=5.33\) can be calculated of the displacement series \(x=0,0.1,0.2,\cdots,5.9,6\) mm in the case of half-Gaussian. Then, the weight coefficient \(\mu_{i}\) can be expressed as:  
\[\mu(x)=0.07\cdot e^{-0.018(x-6)^{2}}. \tag{17}\]  
The full-scale adjustment function \(g_{f}\) can be expressed as:  
\[g_{f}:u_{f}=sgn(\sum_{i=1}^{n}U_{i}\alpha_{i}K(u_{zi},u_{zj})+b), \tag{18}\]  
where \(u_{f}\) is the output voltage that is adjusted by the full-scale adjustment function.  
Finally, Figure 8 shows the first loop results of the serial adjustments including sensitivity adjustment, mid-scale adjustment, zero-scale adjustment and full-scale adjustment based on the original curve of sensor 1, and as well the output voltage deviation between the actual characteristic curve and the ideal characteristic curve, which is obviously reduced at the mid-scale, zero-scale and full-scale, respectively.  
Figure 8: First results of zero-scale and full-scale adjustment based on mid-scale adjusted curve for Sensor 1.
## 2 Measuring System, Materials and Methods
### Measuring System and Samples
#### 2.2.2 Excitation  
The proposed magnetic method modifies Magnetic Flux Leakage (MFL) technology. The MFL technique's fundamental principle involves detecting changes in the magnetic field distribution generated through excitation. The typical configuration is presented in Figure 9. Variations in the magnetic field distribution are induced by inhomogeneities within the examined material. Corrosion, cracks, or other forms of structural damage may cause such inhomogeneities.  
Figure 8: Schematic picture of the tested RC structure with parameters.  
In this work, the classic MFL method was adapted to assess reinforcement within reinforced concrete structures. To significantly enhance its effectiveness, three modifications were introduced:  
* The excitation system comprises two neodymium magnets. This modification enables the use of the hardware setting where the magnets are orientated to the sample with the same poles (referred to as Same Pole Magnetization, or SPM) and the adjustment of the distance between the magnets.
* The magnets are positioned directly above the reinforcing bar under inspection, thereby minimizing the influence of adjacent bars within the reinforcement grid on measurement outcomes.
* The magnets are separated from one another by a distance of 1000 mm and from the detector by approximately 500 mm. This configuration ensures the adequate magnetization of the reinforcing bar and minimizes the magnets' direct influence on the detector. Furthermore, this arrangement supports the development of an area testing transducer with a large measurement area (array of the sensors [8]).  
Three magnetization methods were tested (Figure 10). In the first case, a passive variant was analyzed (NoM, or No Magnetization); in the second case, the magnets were oriented with opposite poles directed toward the reinforcing bar (OPM, or Opposite Pole Magnetization); and in the final case, the magnets were faced with the same poles toward the reinforcing bar (SPM, or Same Pole Magnetization) [6].
## 3 Proposed System Model
### Architecture Overview  
Being motivated from the system model proposed in [7], we proposed our system model for VN. Our proposed system model allows the development of the distributed VN in an efficient manner. Figure 2 illustrates the proposed system model designed to meet the requirements and challenges of VN. In the model, RSUs are static nodes, which are placed in a distributed manner to ensure efficient service provisioning. In the figure, three different types of nodes exist, which are: ordinary nodes, edge nodes and RSUs. Smart vehicles are considered as ordinary nodes that perform V2V communication. Ordinary nodes are resource constraint nodes having low storage capability, less computational power and low battery life. Edge nodes have more computational power than ordinary nodes and are shown in a green circle. Whereas, RSUs act as static nodes. Edge nodes handle service requests/responses. An ordinary node requests a service from edge nodes in a distributed manner. Due to the distributed placement of the edge nodes and RSUs, scalability and availability of the system are achieved. In the proposed model, two types of communication take place, which are: V2V and V2R. For efficient performance, all nodes need to be in active states at all times. For more reliable communication, the cryptographic mechanism is proposed, which ensures security and privacy.  
Whenever a new unrecognized vehicle joins the network, a distinct crypto ID is issued to the vehicle by an IVTP organization. Each vehicle in a VN has its unique crypto ID, which is used to establish trust between vehicles in a communication network. IVTP calculates the trust value that ensures the reputation of a vehicle. Greater the IVTP value, higher will be its reputation and honor. All the RSUs primarily contain sensory information, which is required by the ordinary nodes. All RSUs are connected in a P2P manner.
## 4 Experimental Evaluations for Action Recognition
### Ablation Analysis of Pedestrian-Action Recognition
#### 4.2.2 Evaluation of the Position-Information Feature-Image Added Two-Stream CNN  
Table 7 compares the accuracy of the binary descriptor dense SIFT flow-based two-stream CNN with and without the position-information feature-image added stream. From the experimental results, the position-information feature-image added stream is adopted to improve the performance of cross-attribute recognition while maintaining the performance of action-attribute recognition.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Action**} & \multicolumn{3}{c}{**Cross**} \\ \cline{2-6}  & **Walking** & **Standing** & **Average** & **Cross** & **Not Cross** & **Average** \\ \hline Optical Flow & 86.90\% & 61.59\% & 84.68\% & 75.73\% & 63.22\% & 71.69\% \\ \hline Binary Descriptor & \multirow{2}{*}{91.01\%} & \multirow{2}{*}{72.38\%} & \multirow{2}{*}{88.70\%} & \multirow{2}{*}{86.12\%} & \multirow{2}{*}{65.64\%} & \multirow{2}{*}{76.44\%} \\ Dense SIFT Flow & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Test accuracy of the position-information feature-image added two-stream CNN with the detector and tracker between the optical flow and binary descriptor dense SIFT flow.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Walking Accuracy** & **Standing Accuracy** & **Average Accuracy** \\ \hline JAAD & 92.44\% & 52.80\% & 82.91\% \\ \hline JAAD and PIE & 91.01\% & 72.38\% & 88.70\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Test accuracy of pedestrian-action recognition using the conventional dense SIFT flow two-stream CNN.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Action Attribute**} & \multicolumn{2}{c}{**Cross Attribute**} \\ \cline{2-5}  & **Walking** & **Standing** & **Average** & **Cross** & **Not Cross** & **Average** \\ \hline Two-Stream CNN & 91.01\% & 72.38\% & 88.70\% & 86.12\% & 65.64\% & 76.44\% \\ \hline Two-Stream CNN & \multirow{2}{*}{91.49\%} & \multirow{2}{*}{72.35\%} & \multirow{2}{*}{88.75\%} & \multirow{2}{*}{86.43\%} & \multirow{2}{*}{71.04\%} & \multirow{2}{*}{79.13\%} \\ with Position stream & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison before and after adding the position-information feature-image stream.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Walking Accuracy** & **Standing Accuracy** & **Average Accuracy** \\ \hline JAAD & 92.44\% & 52.80\% & 82.91\% \\ \hline JAAD and PIE & 91.01\% & 72.38\% & 88.70\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Test accuracy of pedestrian cross-attribute recognition using the conventional dense SIFT flow two-stream CNN.
# Novel Algorithm for Multi-Time Data Implantation in a Special Cyber-Manufacturing Architecture
## 4 Proposed Method of Securing Data in Cyber Device
### Proposed Multi-Time Data Embedment Scheme
#### 4.1.3 Data Extraction Method  
The destination end, i.e., decoder, might collect \(\bar{N}_{r}\) from either cloud, standalone system or through a communication link according to nature of the application. However, our concern issue was to extract message bits from \(\bar{N}_{r}\) and to generate cover DNA/RNA \(N_{r}\). Before proceeding to do that, the decoder next collect the SK and thereafter, it separates \(\bar{N}_{x}\), \(L\), \(n\) and \(S\) from \(SK\). The method converts both the \(\bar{N}_{r}\) and \(\bar{N}_{x}\) into binary values using Table 1, which are, indeed, \(\bar{R}\) and \(R_{x}\), respectively. Concatenate \(\bar{R}\) and \(\bar{R}_{x}\) to form \(\bar{R}_{x,i}\), where \(1\leq i\leq n+1\). Next, it divides the \(\bar{R}_{x}\) into pieces of \(L\) bits. Let the pieces are \(\bar{R}_{x,1}\), \(\bar{R}_{x,2},\ldots,\bar{R}_{x,n+1}\).  
The method executes the following steps of Algorithm 2 and Equations (3)-(6) to extract secrets.  
The flowchart of the extraction process is depicted in Figure 3. If we look back at Equation (1), we see that \(R_{1}\) was produced by performing an exclusive-OR operation among \(R_{2}\), \(S\) and \(M_{i}\). Therefore, \(R_{1}\) XOR \(R_{2}\) XOR \(S\) of Equation (5) will produce \(M_{i}\). Equation (6) appends every extracted message chunk to the lagging of \(M\) as it extracts each most recently implanted chunk, i.e., it extracts the last chunk first, then the second-last chunk, and so on.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**SK**} \\ \hline \(\bar{N}_{x}\) & \(L\) & \(n\) & \(S\) \\ \hline \(L\times(n-1)\) & 16 bits & 4 bits & \(L-\)bits \\ \hline \multicolumn{4}{c}{\(Total:(L\times n+20)\) bits} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Stego key’s elements and its length.  
Figure 3: Message and cover DNA extraction process. This is an inverse method that extracts the secrets from the stego DNA and rebuilds the cover DNA sequence as well.  
After completing the execution of the loop, \(R_{2}\) will hold the value of \(\bar{R}_{x,1}\). That \(\bar{R}_{x,1}\) is mainly \(R\), as presented in step (5) of Figure 1. Hence, \(R_{2}\) is \(R\). The \(R\) is converted to nucleotides \(N_{r}\) according to Table 1. As the scheme has the capability to generate \(N_{r}\), it is a reversible method. The proposed reversible method, thus, extracts \(M\) and \(N_{r}\) at the receiver end.
## 2 Characterisation Test Set-Up
### Data Acquisition and Scripting  
Due to time constraints on the availability of the EIRSAT-1 FM, the ability to execute tests overnight and without an operator always present was paramount. Python scripts were composed to configure the \(\sim\)40 parameters to set up GMOD and then allow for the specified amount of time for spectral integration. The scripts interfaced with the mission control software (MCS v21.2) and commanded the experiment as if it were in flight. The main interface with the spacecraft was via Bright Ascension's MCS [45]. The Python scripts performed the following tasks: turning on GMOD and experiment configuration including firmware version and operational mode. The script then synchronised the time on GMOD's microcontroller to the OBC time, and initiated GMOD generating data and streaming it to the OBC. The script recorded the storage location of each run on the OBC and, once a number of integrations had been completed, a second script could be used to downlink the data. The data were downlinked through the satellite telemetry interface using the MCS connected to the OBSW. This campaign provided the opportunity to test the robustness of the chain of data acquisition that will be followed during the actual mission.
# A Review: Factors Affecting Internal Two-Phase Flow-Induced Vibrations
## 1 Introduction
### Two-Phase External Flow
#### 1.1.2 Cross Flow  
Cross flow is another type of flow, where fluid flows perpendicular to a group of pipes, as shown in Figure 3b. This type of flow involves isolated cylinders as well as bundles of cylinders. It is commonly found in submerged legs of offshore structures, shell and tube heat exchangers, nuclear steam generators, and petrochemical industries [33; 34; 35; 36; 37; 38]. Flow-induced vibrations due to cross flow are generated by mechanisms, such as fluid elastic instabilities, vortex excitation, periodic wave shedding, bubbles and structure interaction, and random excitation caused by turbulence [39; 40; 41; 42]. Flow-induced vibrations can be severe  
Figure 3: Types of two-phase flow. (**a**) Axial flow, (**b**) cross flow, (**c**) internal flow.  
under resonance conditions. However, [PERSON] et al. [20] reported that FIV due to cross flow on a cylinder can be with significantly higher amplitudes even without the presence of coupling with mechanical system, which can cause resonance. These vibrations depend on many factors, such as configuration of cylinders, physical properties of fluid, pitch to diameter ratio, Reynold number, flow velocity, flow regime, and turbulence level [43; 44].
## 4 Experiments
### Comparison of HER and HER-CA
The remaining parameters were the same as in the previous experiment. The experimental results are shown in Figure 8. In the case of 2 obstacles, the average success rate of HER-CA improves by 20.1% compared to HER, and the confidence level of improvement greater than 10% is 82.7%. In the case of 3 obstacles, HER-CA has a maximum success rate of 90.9%, while HER could not be successfully trained, with a maximum success rate of only 29.7% at an epoch number of 9000.
## 6 Performance Evaluation
### Packet Routing  
The packet delivery success ratio was evaluated by simulating the protocols in different network densities using 5 different sizes for the network: 500 \(\times\) 500 m\({}^{2}\), 750 \(\times\) 750 m\({}^{2}\), 1000 \(\times\) 1000 m\({}^{2}\), 1250 \(\times\) 1250 m\({}^{2}\), and 1500 \(\times\) 1500 m\({}^{2}\). As shown in Figure 13(a), ECKN with bridges outperforms ECKN without bridges and GGF. This is because ECKN creates bridges that shorten the distance between the source node and the sink, increasing the probability of delivering a packet. GGF performs better than ECKN without bridges because ECKN considers not only the positions of the nodes but also their remaining energy, which may cause the packet to take a longer path and increase the probability of dropping the packet. It is also important to note that the 1000 \(\times\) 1000 m\({}^{2}\) network size presents the best average delivery ratio. This is because smaller areas present a high likelihood of collisions, which can result in packet loss during routing. On the other hand, larger areas present blind spots, making routing infeasible and causing event packets to be dropped.  
The success of packet delivery is directly related to the number of hops that an event packet takes when traveling from the source node to the sink. Figure 13(b) shows the comparison between ECKN and GGF. ECKN with bridges presents the smallest average number of hops, which can be explained by the creation of bridges that shorten the path between the source node and the sink, reducing the number of hops needed for the event packet to be delivered. GGF and ECKN without bridges show similar performance, with a slight advantage for the former. This advantage can be attributed to the fact that in GGF,  
Figure 14: (a) Avg delivery ratio, and (b) number of hops (for different node densities).  
Figure 13: (a) Average position estimation error for qualified nodes. (b) Largest outlier error.  
the sensor node always chooses the closest neighbor to the sink to forward the packet, while in ECKN, the remaining energy is also taken into account, which sometimes results in a longer path to reach the sink for ECKN without bridges.  
The network overhead was also evaluated in our simulations. As shown in Figure 15a, GGF and ECKN without bridges exhibit the same behavior, while ECKN with bridges has a larger overhead. This can be explained by the larger time-to-live (TTL) that ECKN with bridges uses when creating a bridge. While GGF and ECKN without bridges have the same TTL for every position packet from the sink, ECKN with bridges calculates whether it is possible to shorten the path between an area and the position of the sink. If it is positive, it increases the TTL of the position packet to reach a further region. This causes the position packet to be resent more times, increasing the network overhead.