esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Location and Characterization of the Experimental Area  
The study was carried out at the Federal Rural University of Pernambuco/Academic Unit of Serra Talhada (UFRPE/UAST) in Serra Talhada, Pernambuco, Brazil (Figure 1a). We evaluated, during two periods from 26 February 2021 to 17 June 2021 and 25 March 2022 to 29 June 2022, the development of areas of thinned, lowered, and enriched Caatinga with _Urochlao mosambicensis_ (Hack.) Dandy (signal grass) and _Cenchrus ciliaris_ L. (buffel grass) (\(7^{\circ}57^{\prime}2^{\prime\prime}\) S, \(38^{\circ}17^{\prime}53^{\prime\prime}\) W and average altitude of 512 m).  
The experimental area was 7200 m\({}^{2}\) (80 m \(\times\) 90 m), divided into three blocks containing four 584 m\({}^{2}\) (29.20 m \(\times\) 20 m) plots, comprising four agroforestry systems in each block of the area (Figure 1b). The agroforestry systems relied on an integration between the vegetation of the Caatinga biome (Table 1), forage plants, and crops. Bushveld signal grass (_Urochlao mosambicensis_ (Hack.) Dandy) and buffel grass (_Cenchrus ciliaris_ L.) were used, and the crops were as follows: corn (_Zea mays_ L.) cv. Batite, cowpea (_Vigna unquiculata_ (L.) [PERSON]) cv. CCE-115 and cotton (_Gossyupium hirsutum_ L.) cv. BRS Aroeira. The treatmentsconsisted of four agroforestry systems in the Caatinga biome, characterized as follows: (1) bushveld signal-buffel grass + cowpea + Caatinga; (2) bushveld signal-buffel grass + cotton + Caatinga; (3) bushveld signal-buffel grass + corn + Caatinga; and (4) bushveld signal-buffel grass + Caatinga (Figure 1b).  
In preparing the area, the Caatinga vegetation was thinned and lowered to make the environment more favorable for the development of other crops. The bushveld signal grass and buffel grass were already established in the area before the beginning of the experiment, and a uniform cut was made at a height of 10 cm from the ground with the aid of a backpack brush cutter (Stihl FS160). For planting the crops, 1 m wide and 26 m long planting strips were opened between the grasses, resulting in a gap of 0.5 m between the crops and the grass. The spacing was 0.5 m between plants and 2 m between rows.  
The region is characterized by climate BSwh-type (semiarid climate with dry winters and rainy summers) according to [PERSON] climate classification [30]. During the experimental period, air temperatures of 24.75 and 25.41 \({}^{\circ}\)C, relative humidity of 66.64 and 77.27%, accumulated precipitation of 419.80 and 180.3 mm, accumulated reference evapotranspiration of 483.76 and 292.77 mm, and global solar radiation of 19.02 and 17.01 MJ m\({}^{-2}\) were recorded in 2021 and 2022, respectively, obtained from the meteorological station of the National Institute of Meteorology (INMET), located 548 m from the experimental area (Figure 2).  
Figure 1: (a) Location of the experiment, and (b) experimental design.
# A Fast Calibration Method for Sensors of Atmospheric Detection System
## 4 Evaluation
### VPSSD Method and Temperature Calibration Time Evaluation
The total calibration time of the original system is 2574.6 s, of the PID control method is 1467.2 s, and of the MAC PID controller design method is 1227.8 s. The total calibration time of MCA PID control is reduced to 47.7% of the original system, and the calibration time is reduced by 239.4 s on the basis of PID control while ensuring the service life of the temperature chamber. Using the VPSSD method proposed in this paper, the total calibration time of the original system is reduced from 2574.6 s to 2348.9 s, a reduction of 225.7 s. The PID control method is reduced from 1467.2 s to 1318.0 s, a reduction of 149.2 s. The MAC PID controller design method is reduced from 1227.8 s to 1189.1 s, a reduction of 38.7 s. Therefore, for different control methods, the variable-precision steady-state discriminant method can significantly reduce the total system calibration time. Combined with the MAC PID controller design method, the total calibration time can be reduced to 46.2% of the original system. While ensuring the service life of the temperature chamber, the total calibration time is greatly improved.
# A Novel ASIC Implementation of Two-Dimensional Image Compression Using Improved B.G. Lee Algorithm
## 4 Results and Discussion  
The proposed design is evaluated and assessed in terms of power, delay, and area using the hardware description language Verilog in this section. The image compression algorithm's performance using the error-tolerant adder, proposed fixed-point multiplier, and the concept of pipelining was also assessed by analyzing the quality of the processed image.
# Formulating Cybersecurity Requirements for Autonomous Ships Using the SQUARE Methodology
## 2 Background
### Artificial Intelligence Technology in Autonomous Ships
#### 2.2.3 Automatic Berthing System  
Berthing refers to the mooring of ships in ports. Existing large ships are supported by tugboats to help them berth. A tugboat is a marine vessel that pushes or pulls other ships with a strong driving force relative to its size. For this reason, research was previously conducted in synchronizing the movements of several tugboats. Recently, however, research has shifted to finding ways to automatically dock a ship without using additional support vessels such as tugboats.  
One approach to solving the berthing problem is to use ANNs. ANN is a statistical learning algorithm inspired by biological neural networks and used in machine learning and cognitive science. The automated berthing system, using ANN, manually controls the ship to generate training data for neural network training. The ship's status, such as the ship coordinates, direction, and distance to berthing, which is recorded during the ship steering process, is used as training data. The neural network trained from this data is used as the main controller of the berthing system. The existing ANN controllers could only dock a ship at ports where training data was collected, but recent research has suggested a controller that can automatically dock a ship in new ports without relearning the ANN controller [18; 19; 20]. Berthing comprises three stages: the ship's course is changed to the optimal direction of approach for berthing, the ship's speed decreases, and finally, the main engine stops.  
The more recent learning-based methods employ deep learning. Techniques such as the deep neural network (DNN) technique, better than the conventional techniques, have been applied to improve the results of learning by increasing the hidden layers in the model for the ANN technique [21; 22]. There are even studies that go beyond supervised learning and reinforcement learning and apply deep reinforcement learning, showing even superior performance with greater accuracy and efficiency [23].
## 5 Feasibility Study Through System Simulations
All of the pattern-matching methods are benchmarked against each other, primarily to learn whether the use of spatial side information brings some gain and secondarily to learn how the different pattern-matching methods that process the side information perform against each other. We investigate the performance of the four pattern-matching methods from the results of the simulations in the next section. The next subsections explain the different components of the system simulator and provide details of the simulation process, simulation execution, the NN structure for each pattern-matching method, and other relevant considerations.
## 2 Preliminaries and Background
### Limited-Memory Warping LCSS Gesture Recognition Method  
SegmentedLCSS and WarpingLCSS, introduced by [18], are two template matching methods for online gesture recognition using wearable motion sensors based on the longest common subsequence (LCS) algorithm. Aside from being robust against human gesture variability and noisy gathered data, they are also tolerant to noisy labeled annotations. On three datasets (10-17 classes), both methods outperform DTW-based classifiers with and without the presence of noisy annotations. WarpingLCSS has a smaller runtime complexity, about one order of magnitude, than SegmentedLCSS. In return, a penalty parameter, which is application-specific, has to be set. Since each method is a binary classifier, a fusion method must be established, which will be discussed and illustrated in detail later.  
A recently proposed variant of the WarpingLCSS method [21], labeled LM-WLCSS, allows the technique to run on a resource constrained sensor node. A custom 8-bit Atmel AVR motion sensor node and a 32-bit ARM Cortex M4 microcontroller were successfully used to illustrate the implementation of this method on three different everyday life applications. On the assumption that a gesture may last up to 10 s and given that the sample rate is 10 Hz, the chips are capable of recognizing, simultaneously and in real-time, 67 and 140 gestures, respectively. Furthermore, the extremely low power consumption used to recognize one gesture (135 uW) might suggest an ASIC (Application-Specific Integrated Circuit) implementation.  
In the following subsections, we review the core components of the training and recognition processes of an LM-WLCSS classifier, which will be in charge of recognizing a particular gesture. All streams of sensor data acquired using multiple sensors attached to the sensor node are pre-processed using a specific quantization step to convert each sample into a sequence of symbols. Accordingly, these strings allow for the formation of a training data set essential for selecting a proper template and computing a rejection threshold. In the recognition mode, each new sample gathered is quantized and transmitted to the LM-WLCSS and then to a local maximum search module, called SearchMax, to finally output if a gesture has occurred or not. Figure 1 describes the entire data processing flow.
## 5 TSN Scheduling Problem Formulation
### TSN Scheduling Problem Formulation Assumptions
#### 5.3.9 Assumptions on the Problem Design  
Many works used assumptions on the input of the problem. Examples are assuming that the topology is fixed (as in [45, 46, 117]), the routes are given (as in [41, 63, 69, 86, 116, 118]), and the positions of the talkers and listeners are fixed (as in [79]). These assumptions eliminate the possible extensions of the scheduling task such as joint routing, topology optimization, and application embedding. On the contrary, other works used assumptions from this group to extend the problem even further by considering redundancy aspects, such as [30, 109] assuming that the redundancy level is given or [126] assuming the tolerance level (i.e., the number of disjoint paths) is provided. In the same way, [104] considered security aspects by assuming that the security importance is an input of the problem and establishing a set of assumptions on the attacker. Refs. [88, 102, 103, 120] considered fault tolerance by introducing some assumptions on the fault model such as assuming single faults with minimum interarrival time, assuming a fault affects only one frame, assuming a single fault in the cyclic redundancy check (CRC), or assuming a constant error rate.  
Another type of assumption related to the problem design touches the formulation of the problem. For example, assuming the use of TAS, as in [51], assuming an ideal network (no loss, no drop, error-free channel), as in [85], assuming valid solutions always meet the deadline, as in [117], assuming a densely connected topology where path always exists, as in [117], assuming certain characteristics on the path of the traffic such as there is at least one switch in the path, as in [86], the path of the stream does not change from one cycle to another, as in [110], and there are no loops in the paths, as in [95]. Although these assumptions ease the problem formulation to some extent, they have an applicability issue. A clear example is the consideration of an ideal network. This is physically not possible and, consequently, the algorithms built for an ideal network are not usable in real use cases. Other assumptions from this group include considering that maximizing gaps between HTS windows reduces starvation of other traffic types, as in [52]. Although this makes sense intuitively, the assumption itself needs formal proof to consolidate it.  
Likewise, [99] assumed that the traffic is always schedulable. This assumption depends on the resources available in the network as well as their distribution. For example, if bandwidth is highly fragmented, then it might happen that the total available bandwidth is enough to accommodate traffic, but in reality, there is no fitting slot. In the general case, this assumption is not valid. Assuming that the maximum path length is 8 hops (as in [37]), this assumption may hold for specific cases, but in general, paths can be much longer (e.g., industrial automation profile [10] supports up to 64 hops).
## 5 Evaluation
### Test-Bed Preparation  
Three realistic network topologies are used for the experimental evaluation of our approach; a modified Abilene topology utilised in [43], Geant [44], and Cernet [45]. The topologies have been built and implemented in Mininet using a Python script; SDN-Openflow switches replaced the nodes for each network topology. Each switch has a host that forwarded and received different types of traffic. Multimedia providers are deployed in a number of Openflow switches. The provider is able to stream real-time Dynamic Adaptive Streaming over HTTP (DASH) based video flows. Due to Mininet resources constraints, links capacities for each topology have been scaled to meet the experimental environment requirements. Table 1 shows the utilised network topologies employed to evaluate our RL-based multimedia traffic routing.
## 2 Background Literature Review
### Assistive Technology in Assessments for BVI Students
#### 2.2.2 Computer-Based Exams  
Computer-based exams have revolutionized assessment methodologies for BVI students, providing enhanced flexibility in question formats and real-time feedback. These digital platforms support multimedia integration, significantly increasing engagement and accessibility [2; 13; 15; 17; 21; 22; 29]. Research indicates that BVI students often perform better in computer-based environments, where the cognitive load associated with navigating tactile or auditory materials is minimized compared to traditional formats [15; 17; 21; 22; 29; 30]. This adaptability promotes inclusivity and encourages a more interactive assessment experience. However, challenges such as software malfunctions and connectivity issuescan hinder accessibility [8; 18]. While computer-based technology is advanced, it is often costly to accommodate many students and requires specialized expertise to operate effectively and provide adequate support [22]. Additionally, noise and distractions in the testing environment can affect the focus, impacting BVI students and their peers [2; 31].  
Multiple-choice questions (MCQs) can be effectively adapted for computer-based assessments, allowing answers to be selected via keyboard inputs or voice commands [17; 29]. This structure facilitates answer selection and provides immediate feedback, which is crucial for learning. Clarity in audio delivery is essential to prevent misunderstandings [2; 18; 32]. Short and long-answer questions present additional challenges, as BVI students may struggle to articulate complex ideas without visual aids, affecting their performance [18]. Thus, carefully considering the testing environment and format is essential to ensuring that all students have an equitable assessment experience, regardless of ability [1; 2].  
In conclusion, while computer-based exams provide promising benefits for BVI students, including enhanced accessibility and engagement, addressing technological challenges and inclusivity issues is crucial. Instructors and technologists must collaborate to design assessments that accommodate diverse needs, ensuring a fair testing experience for all students.
# An Efficient Parallel Algorithm for Detecting Packet Filter Conflicts
## 5 Results and Discussion
### Comparing Speed Performance  
In this subsection, we present a comparison of the speed performance of the Host, the GPCDA, and the EPCDA in different filter databases. The GPCDA and the EPCDA used two different total numbers of thread allocations, (256, 4) and (512, 8), for parallel computing. Figure 7 shows the required time for each filter to execute conflict detection in 12 different databases when the database size is 30K, among which databases the time for the GPCDA and the EPCDA is defined in Section 4.1. After the acceleration of parallel computing by the GPU, the GPCDA and the EPCDA are better than the Host in terms of conflict-detection speed. When the (256, 4) allocation is used, the GPCDA and the EPCDA are faster than the Host alone, by 1.3 to 4.5 and 3.9 to 9.3 times, respectively. Under the (512, 8) allocation, more threads are available to support parallel computing; thus, the speed performance significantly improves. The GPCDA and the EPCDA are 4.2 to 12.4 and 10.9 to 32.4 times faster, respectively, than the Host alone. Because the dispatch method in the EPCDA enables every thread to have a balanced workload, it improves the parallel computing performance. With the same number of threads, the EPCDA outperforms the GPCDA. The average time required for conflict detection in the EPCDA (256, 4) allocation is almost the same as in the GPCDA (512, 8) allocation, which demonstrates that workload balancing affects performance significantly.  
Intuitively, when the number of allocated threads is doubled, performance should also be doubled. However, the experimental results (Figure 7) show that the performance did not double as expected. In the experiment, the number of allocated threads was four, but the performance improved by only 2 to 3 times. This was because when more threads executed parallel computation, the degree of resource competition increased, resulting in performance degradation.  
Table 4 lists the average time required for each filter to execute conflict detection using algorithms in different databases when the filter database size is 100 K. Here, the GPCDA and the EPCDA outperformed the Host alone. In the EPCDA, the maximum time for a filter to perform conflict detection was lower than 22 \(\upmu\)s, suggesting that the EPCDA still maintained good performance for a large database. In addition, the EPCDA can operate in applications with frequent filter updates; in such cases, fast conflict detection is required to achieve high throughputs.  
Figure 7: Average detection time.
# A Blockchain-Based Secure Inter-Hospital EMR Sharing System
## 2 Preliminary and Security Requirements
### Security Requirements
#### 2.2.2 Data Integrity  
The system is vulnerable to malicious attacks in the form of modification for any message transmitted in an unencrypted network environment. This means that the information delivered to the receiver is not the original information transferred by the sender. Therefore, the integrity of the transferred message must be ensured, and the message must also be protected against tampering during the transmission.
## 3 Methodology
### Layer Attention  
The proposed layer attention mechanism is explained in depth in this subsection with its architecture shown in Figure 9. To compress the global spatial information in a deep convolutional neural network, we introduce a GAP operation after each convolutional layer and convolutional module. Let us assume the input feature map has dimensions \(H_{i}\times W_{i}\times C_{i}\), where \(H_{i}\), \(W_{i}\), and \(C_{i}\) represent the height, width, and number of channels of the feature map at the \(i\)-th layer, respectively. First, the feature map is compressed from size \(H_{i}\times W_{i}\times C_{i}\) to \(1\times 1\times C_{i}\) through GAP. Then, we fuse the \(1\times 1\times C_{i}\) feature maps obtained from the compressed part of each layer along the channels. The fused vector has a size of \(1\times 1\times(C_{1}+C_{2}+C_{3}+\ldots+C_{n})\), where \(n\) represents the number of convolutional layers or modules to be fused. Next, we pass the fused vector through the fully connected layer to predict the contribution of each convolutional layer or module. The output consists of \(n\) numbers, representing the contribution of each layer's feature to the final prediction result. The weights obtained from this prediction are then multiplied by feature maps of each layer to obtain the weighted feature maps of them, as shown in Figure 10. The weighted feature maps are subsequently used for feature fusion. Finally, they are fed into a classification or regression layer to make the prediction. The pseudocode of LA is shown in Algorithm 2.  
Figure 9: Schematic diagram of the layer attention mechanism to predict the weight of each layer.
## 2 Materials and Methods
### Expectation Maximization Algorithm  
The Expectation Maximization (EM) algorithm, initially introduced by [PERSON] et al. [25], was devised for the purpose of estimating parameters within mixture distribution models. The implementation of the EM algorithm relied on the complete-data likelihood function as depicted in Equation (24), where the log-likelihood \(lnL(\Psi)\) is enhanced by the inclusion of randomly assigned 0-1 binary variables \(z_{i,k}\), which identifies the \(i\)th observation as coming from the \(k\)th component.  
\[lnL(\Psi,Z)=\sum_{i=1}^{N}\sum_{j=1}^{K}z_{i,k}\ln(\pi_{k}f(x_{i},\theta_{k})) \tag{24}\]  
Starting from an initial \(\Psi^{(0)}\), the EM algorithm iterates between an Expectation (E)-step and Maximization (M)-step.  
* E-step The posterior probability of data point \(i\) belonging to component \(k\) at iteration \(r\) is denoted as \(z_{i,k}^{(r)}\) is obtained by using [PERSON] theorem and given in Equation (25). \[z_{i,k}^{(r)}=\frac{\pi_{i,k}^{(r)}f(x_{i},\theta_{k}^{(r)})}{\sum_{j=1}^{K} \pi_{i,j}^{(r)}f(x_{i},\theta_{j}^{(r)})}\] (25) where \(\pi_{i,k}^{(r)}\) is the mixing proportion of component \(k\) at iteration \(r\), and \(f(x_{i},\theta_{k}(r))\) is the probability density function of component \(k\) evaluated at data point \(x_{i}\) using parameters \(\theta_{k}^{(r)}\).
* M-step Update the parameter estimates to maximize the expected \(lnL(\Psi,Z)\) based on the computed posterior probabilities in the E-step. These steps are repeated until the expected log-likelihood converges to the maximum.