esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Optimal Calculation of the Rice Coding Parameter
## 1 Introduction
### Applications  
Rice coding has been employed traditionally for data compression of audio [3], images [4; 5; 6; 7; 8; 9], video [10; 11], and electrocardiogram [12] data. In most cases, the input data is first transformed (using, i.e., a wavelet transform), then the transform coefficients are quantised. The quantised transform coefficients usually follow a two-sided geometric distribution [13; 14], for which Rice coding can provide short codes close in length to Huffman codes [15].  
In recent years, our research group has developed prototypes of battery-operated resource-constrained Internet of Things (IoT) devices. Such IoT devices usually communicate over low-power lossy wireless networks [16]. In current IoT telecommunication technologies--such as LoRa [17; 18; 19]--data rates do not exceed more than 56 kilobits per second, there is no guarantee of successful packet delivery, and the available space for encoding application data in a data frame is no more than 220 bytes [20]. Such small IoT devices may have up to 32 KB of RAM memory with CPUs running at frequencies below 100 MHz [21; 22; 23].  
In our most recent application, we developed IoT devices for measuring the pH and conductivity of sewage waste water [24; 25], with the aim of providing information about the presence and localisation of illegal spills of toxic chemicals in sewers. The pH and conductivity values of aggregated sewage waste water are mostly constant in normal conditions. Based on this, we employed Rice codes for coding the difference between consecutive pH and conductivity measurements of sewage wastewater without employing complex CPU operations or using large amounts of memory.  
Several other compression methods have been proposed for resource-constrained IoT devices. Most of the proposed data compression methods in the literature for IoT applications--e.g., [26; 27; 28; 29; 30; 31]--are lossy. Those works propose an estimator method that extracts enough information from a time-sequence and then encodes the minimum set of parameters to recreate, as close as possible, the original information based on the transmitted parameters of the estimator method. [PERSON] et al. [32] presented a summary of other compression algorithms for IoT prior to the year 2010.  
The focus of this work is to evaluate different methods for calculating the Rice parameter. A comparison study of the compression ratio achieved by other different methods is out of the scope of this article, since it would require evaluating the overhead imposed on the network for transmitting frequency tables, volatile memory usage at constrained nodes, and energy used for coding and decoding.
## 2 System Model
### Key Definitions and Symbols  
To formally describe the scheduling problem and algorithms, we define key terms and mathematical symbols in Table 1. A data stream (\(F_{i}\)) refers to a sequence of packets with specific timing and QoS requirements. The hyperperiod (\(\mathcal{T}\)) is the least common multiple of the periods of all scheduled streams, representing the overall repeating cycle of the GCL. The Gate Control List (GCL) itself is a schedule that dictates, for each output queue of a TSN switch port, the time intervals during which its gate is open or closed.  
The characterization of each data stream \(F_{i}\) (length \(L_{i}\), transmission time \(t_{i}\), period \(T_{i}\), deadline \(D_{i}\), path \(P_{i}\), and priority \(PCP_{i}\)) provides the input for the scheduling algorithms. The goal is to determine the start times \(\phi_{i,n}\) or \(\varphi_{i,j,n}\) for all streams/sub-streams. It is important to note that the total end-to-end delay experienced by a flow comprises several components: sending delay (serialization delay, calculable from \(L_{i}\) and link speed),propagation delay (dependent on medium and distance), processing delay (time taken by TSN switches for GCL execution and forwarding, measurable), and queuing delay. The primary aim of the TAS scheduling algorithms presented is to minimize or strictly bound the queuing delay by ensuring conflict-free, pre-scheduled transmission opportunities.
# A Distributed Execution Pipeline for Clustering Trajectories Based on a Fuzzy Similarity Relation
## 4 Materials and Methods  
The project is implemented in Scala, aiming to extract clusters from massive trajectory logs stored in HDFS and persist them in MongoDB. Spark is leveraged to conduct the work in a distributed manner. RDD distributed across the memory and disk are being created and the work's stages are being achieved in parallel by distributing tasks across worker nodes. In particular, the system must provide the possibility to employ either the smart or semi-naive algorithm for computing the transitive closure; the choice of applying either the max-min, max-delta and max-product t-norms for transitivity. Moreover, the max-delta and max-product t-norms need a specific kind of handler.  
**Theorem 1**.: _Let \(x\in[\alpha,1]\) and \(y\in[\alpha,1]\) with \(\alpha\in[0,1]\). The product \(x\bullet y\) is only in \([\alpha^{2},1]\) but not forcibly in \([\alpha,1]\)._  
Proof of Theorem 1.: Let us put \(\alpha=1/2\) then \(1/4\leq x\bullet y\leq 1\). For \(x\bullet y\) to be in \([\alpha,1]\), \(\alpha^{2}\geq\alpha\) must be true. But, in our case \(1/2\geq 1/4\). Hence, by contradiction \(x\bullet y\
otin[\alpha,1]\) only in \([\alpha^{2},1]\).  
**Theorem 2**.: _By considering the same supposition as the Theorem 1. The preposition \(x+y-1\geq\alpha\) is not necessarily true, and it holds only if \(\alpha=1\)._  
Proof of Theorem 2.: Let us suppose that \(x=y=\alpha\). This is equivalent to \(x+y-1=2\alpha-1\). Hence, if \(x+y-1\geq\alpha\), then \(2\alpha-1\geq\alpha\). The last statement is equivalent to \(\alpha\geq 1\), and this holds only if \(\alpha=1\) because by our supposition \(\alpha\leq 1\). Consequently for \(\alpha\in[0,1[\), \(x+y-1\geq\alpha\) is not always true.  
These two theorems proof that at each step of computing the t-norms, we must filter the norms inferior to the chosen \(\alpha\)-level. To provide all these possibilities, we present the Figures 1 and 2 explained in the next subsection.
# A Traffic Information Detection Method at Single Intersection Based on Wi-Fi Data
## 3 Methodology
### Calculation of Intersection Data  
After the original Wi-Fi data are preprocessed in real-time, the inherent spatiotemporal information of the Wi-Fi data at the intersection can be further mined so that the space mean speed and vehicle steering ratio information of the entrance and exit sections of a single intersection can be calculated.
# Strategy for Non-Orthogonal Multiple Access and Performance in 5G and 6G Networks
## 6 Characteristics of the Methodology of Implementing NOMA Schemes  
All of the suggested NOMA variants come with their own set of benefits and challenges. All of these connections are designed for use in the uplink, or between subscriber terminals and the base station. Since NOMA systems use complicated code sequences, sophisticated methods of modulation and coding, and complex algorithms for multi-user reception of such signals [38], their processing is inherently difficult. The widely used SCMA method, for instance, requires both sophisticated transmitting code books and elaborate receiving signal processing algorithms. Complicated algorithms for the multi-user receipt of such signals are necessary for the MUSA system due to its usage of complex sequences. Trellis coding techniques are the most difficult to implement in an LPMA setting. Research into efficient algorithms for creating code books and coding, innovative methods of modulation and mapping, and combinations with orthogonal access schemes and MIMO technology are all areas that 3 GPP standardization members have identified as requiring further investigation [39]. The 3 GPP paper emphasizes the significance of developing low-computability algorithms for multi-user receipt of NOMA signals [40].
# A Novel Method Using 3D Interest Points to Place Markers on a Large Object in Augmented Reality
## 1 Introduction
Although a virtual object was not augmented in the proposed method, multiple markers were used for stable scanning. The markers were placed around the edge of the actual object, and the distance between them was set to about 25 cm [15]. Additionally, marker placement criteria, such as the number of markers used and positions of markers, were arbitrarily selected in most studies; therefore, these inevitable problems were not considered. This means that, even if previous studies are replicated, it is difficult to obtain the same results (e.g., accuracy of registration) as previous studies because of human subjectivity in the process of placing markers. In consideration of the above-mentioned problems, this paper proposes a method for placing multiple markers to provide accurate registration on a large object. The proposed method (1) divides a virtual object evenly considering the geometric discrepancy problem, (2) uses identifiable positions based on 3D interest points considering the relative position consistency problem, and (3) automatically determines the positions of multiple markers. This paper is organized as follows. Section 2 describes the proposed method in detail. Section 3 presents two experiments conducted to validate the proposed method, along with discussions, followed by conclusions in Section 4.
# A Novel Global Key-Value Storage System Based on Kinetic Drives +
## 5 Design and Architecture
### Initial Stage  
When key-value pairs are generated, we use the above indexing scheme to store _original data_ in the drives at the initial stage. Figure 6 shows our system with multiple regions. At the very beginning, all drives in all regions are empty. When a user in Region 1 generates a key-value pair, it first contacts the metadata server MS1 of the user's region by sending the _key_. Based on the prefix of the _key_, the metadata server MS1 looks at the _key_ and finds where (i.e., which region) this key-value pair should be stored (Region 2 in this case) as the _original data (primary copy)_.  
The IP address of metadata server MS2 of Region 2 is returned to the user by the metadata server MS1. Next, the user sends the _key_ to that metadata server MS2, which finds the specific drive that should store the key-value pair, based on additional \(\lceil log_{2}M\rceil\) bits after the first \(\lceil log_{2}N\rceil\) bits, as mentioned earlier. Then, metadata server MS2 returns the IP address of that particular Kinetic Drive to the user. Finally, the user directly contacts the drive via its IP address to store the key-value pair.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Key Range**} & **IP Address of the Drive** \\ \hline
100 0000000 0... to 100 0000000 1... & 135.37.118.3 \\
100 0000001 0... to 100 0000011 1... & 135.37.118.4 \\
100 0000010 0... to 100 0000010 1... & 135.37.118.5 \\
100 0000011 0... to 100 0000011 1... & 135.37.118.6 \\... &... &... \\ \hline \hline \end{tabular}
\end{table}
Table 1: An example of the key indexing table for _original data_ (Region 4).  
Figure 6: An example of our key-value store system. MS, metadata server.  
We can see that during this initial stage, only _original data (primary copies)_ are stored without additional backup copies.
## 4 Detection and Mitigation Mechanism
### Idbn  
DBN is a widely used intelligent classification algorithm, which has exhibited a good performance in intrusion detection of cyber-domain [25]. The data processing of the heavy-duty robot takes place in the cloud server, while the data is exchanged between control system and cloud server in real time. It is necessary to carry out intrusion detection against targeted attacks from the cyber-domain.  
Traditional DBN has a complicated data classification step and a slow learning process, which lead to the low efficiency of the classifier. Furthermore, it also reduces the performance of the intrusion detection mechanism. Therefore, in order to meet the requirements for intrusion detection of heavy-duty robots, increasing the detection speed and performance, it's essential for the traditional DBN to be optimized and improved. The improvement for DBN is mainly reflected in the data processing and DBN model design.
## 6 Conclusions  
This study investigated the use of advanced soft computing techniques, including Multiple Linear Regression (MLR), Genetic Programming (GP), Classification and Regression Trees (CART), and Genetic Algorithm-Emotional Neuron Network (GA-ENN), to predict the ultimate bearing capacity (UBC) of shallow foundations on cohesionless soils. The following is a brief summary of the results:  
* These AI models, utilizing input parameters such as footing width (B), footing depth (D), footing geometry (L/B), unit weight of sand (\(\gamma_{\text{d}}\) or \(\gamma^{\prime}\)), and internal friction angle (\(\phi\)), outperformed traditional theoretical approaches in predictive performance.
* The Genetic Algorithm-Emotional Neural Network (GA-ENN) model consistently demonstrated superior predictive accuracy, evidenced by its lowest Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) values in both the training and the testing datasets.
* The GA-ENN model also consistently achieved the highest coefficient of determination (\(R^{2}\)) values, indicating exceptional fit and a strong ability to explain variance in the target variable.
* Compared to historical models, including \(R^{2}\) and Nash-Sutcliffe Efficiency (NSE) values, the GA-ENN model consistently outperformed these benchmarks.
* Sensitivity analysis highlighted the significance of foundation depth, internal friction angle of soil, and foundation width as key determinants in predicting the ultimate bearing capacity of shallow foundations.
* The successful application of these AI models offers a substantial advancement in geotechnical engineering practices, providing engineers and practitioners with powerful tools for more reliable and informed foundation design. These models can handle complex spatial variability and uncertainties in soil properties, allowing engineers to optimize shallow foundation designs for safety, stability, and cost-effectiveness.  
Conceptualization, K.K. and A.B.; methodology, K.K. and A.B.; software, K.K. and A.B.; validation, K.K. and A.B.; formal analysis, K.K. and A.B.; investigation, K.K. and A.B.; resources, K.K.; data curation, A.B.; writing--original draft preparation, H.A.-N., H.B., M.A. and M.M.S.; writing--review and editing, H.A.-N., H.B., M.A. and M.M.S.; visualization, H.B. and A.B.; supervision, H.A.-N. and M.A; project administration, M.M.S.; funding acquisition, A.B. and H.A.-N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data that support the findings of this study are available on request from the corresponding author.  
This research is supported by an Australian Government Research Training Program (KTP) Scholarship for the second author ([PERSON]).  
The authors declare no conflict of interest.
# Build-Launch-Consolidate Framework and Toolkit for Impact Analysis on Wireless Sensor Networks
## 4 Case Study: Analyzing Flooding Attack
### Results and Analysis
#### 4.3.1 Impact on Packet Delivery Ratio  
Table 4 shows that the PDR for the binary layout network decreased by 19% under the flooding attack, compared to a 44% decrease in the grid layout. Moreover, Figures 10 and 11 illustrate the average PDR for each node of the binary and grid layouts, comparing the attack's impact with the normal baseline. In a regular scenario, most of the nodes in both layouts achieve a PDR score of 100%. The binary layout exhibits a minimum PDR of 90%, as shown in Figure 10, whereas the grid layout shows a minimum PDR of 83%, as illustrated in Figure 11. On the other hand, under the flooding attack, multiple nodes in both layouts failed to deliver any data packets and scored 0% PDR. We utilized the heatmaps generated by our tools to analyze how individual nodes performed during each simulation, which gives us insights into how the attack propagates. Figure 12 shows a heatmap for the changes in the PDR across the binary network nodes after the attack compared to the normal baseline scenario (without the attack). Figure 13 shows the same comparison for the grid layout network.  
Starting with the binary layout, Figure 12 demonstrates that the child nodes of the attacker failed to deliver any messages to the sink. The attacker's parent, however, was  
Figure 11: Average PDR for nodes in the grid layout, comparing normal and attack scenarios.  
Figure 10: Average PDR for nodes in the binary layout, comparing normal and attack scenarios.  
not impacted, but the average PDR of its other branch (nodes 12, 19, and 30) was notably decreased by 23% on average. The same can also be noted--to a lesser degree--for the further outer branch (nodes 6, 11, 18, 28, 29), which showed a decrease in PDR by an average of 11.8%. The other half of the binary layout showed no significant changes.  
Figure 12: Heatmap of binary layout, highlighting percentage change in average PDR post attack.  
Figure 13: Heatmap of grid layout, highlighting percentage change in average PDR post attack.  
As for the grid layout, as Figure 13 reveals, we can see that 13 out of 33 benign nodes (excluding the sink and the attacker) had a decrease in PDR by 50% or more. The most impacted nodes are those closer to the attacker and further away from the sink. We notice that nodes adjacent to the attacker but closer to the sink (nodes 13, 14, and 15) are significantly less impacted compared to those that are further from the sink (nodes 24, 23, 25). Apparently, node 18 changed its parent as the experiment progressed and lost all subsequent packets. The effect of the attack in this layout propagated to a larger and further population compared to the binary layout. For example, it reached node 5, which is one hop away from the sink and two hops away from the attacker.
## 3 Case Study
### Analysis of Influence Intensity  
Based on the identification of abnormal stations affected by the disruption, the influence intensity of abnormal stations was further analyzed from the perspectives of the whole network and several representative stations.
## 4 IETF 6 LoWPAN Working Group (IPv6)
### Implementation and Evaluation
#### 4.2.2 Evaluation
In addition to this, the transport layer protocol compression mechanism, HC2, compresses only limited numbers of protocols. New compression mechanisms, called IPHC and NHC, were introduced in RFC 6282. These mechanisms are significant improvements to the HC1 and HC2 compression mechanisms and new implementations are urged to use the new compression techniques instead of HC1 and HC2. To our knowledge, no detailed evaluations regarding the footprint and performance of these compression mechanisms have been published in the literature to date.
## 3 Research Design
### Output Variable
#### 3.2.3 Constructing RBF Neural Network  
The project performance forecasting model designed in this paper uses RBF neural networks to mimic the information processing ability of biological nerve cells and takes the PD, PC, SPI(t\({}_{1}\)), and RL of similar historical projects as input variables for simulation during construction projects so as to make a reasonable and scientific change of the current project's performance indicators.  
RBF neural network generally has three layers: input layer, hidden layer, and output layer. As illustrated in Figure 3, the input layer includes four indicators of input: BAC, PD, SPI(t\({}_{1}\)), and RL. The number of neurons in the hidden layer is determined by the number of sample items. Finally, the output layer includes two indicators SPI(t\({}_{2}\)) and SPI(t\({}_{3}\)).  
The model training needs to go through a preliminary process first, by normalizing the data. Then, the center vector and normalization constants of the Gaussian function are derived based on the positional division of the data. Finally, after the weights and hidden layer parameters are determined in the learning phase, the weight matrix of the output layer is derived using recursive least squares. The output function is shown in Equation (1):  
\[Y=\sum_{i=1}^{m}\varepsilon_{i}f(x_{i}-c_{i}) \tag{1}\]  
where \(Y\) is the values of the output variables, \(x_{i}\) is \(i\)-th input variable, \(c_{i}\) is the \(i\)-th center of the function, \(m\) is the number of variables, \(f(x_{i}-c_{i})\) is the \(i\)-th basis function, \(\varepsilon_{i}\) is the \(i\)-th weights of the nodes of the implicit layer to the output layer.  
The basis function is generally a Gaussian function because the Gaussian function has the advantages of rotational symmetry, utility, and simplicity of form. The general expression of the Gaussian function is shown in Equation (2):  
\[f(x_{i}-c_{i})=e^{-\frac{1}{2\sigma^{2}}\left\|x_{i}-c_{i}\right\|^{2}} \tag{2}\]  
\(\parallel x_{i}-c_{i}\parallel^{2}\) is edclideannorm, \(\sigma\) is variance of Gaussian function.  
Figure 3: RBF neural network framework.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of Variable** & **Variable** & **Calculation or Description** \\ \hline \multirow{3}{*}{input variable} & BAC & Budget at completion \\  & PD & Planned duration \\  & SPI(t\({}_{1}\)) & EV/FV \\  & RL & range of values [0, 1] \\ \hline \multirow{2}{*}{output variable} & SPI(t\({}_{3}\)) & – \\  & SPI(t\({}_{3}\)) & – \\ \hline \hline \end{tabular}
\end{table}
Table 3: Input variable and output variable.
# Bistatic Sea Clutter Suppression Method Based on Compressed Sensing Optimization
## 4 Simulation
### Simulation Experiment 1
Figure 4: The comparison of echo power spectra before and after sea clutter suppression using different algorithms under the condition of \(\text{SCR1}=-25\) dB : (**a**) comparison of root method before and after suppression, (**b**) comparison of SVD method before and after suppression, (**c**) comparison of the proposed method before and after suppression.
# Aroma Profile of Monovarietal _Pet-Nat_ Ciders: The Role of Croatian Traditional Apple Varieties
###### Abstract  
The aromatic and sensory profiles of monovarietal sparkling ciders made according to the modified _Methode Ancestrale_ or _Petillant Naturel (_Pat-Nat_) method were established. Three Croatian traditional apple varieties ('Boicicinca', 'Bobovac', and 'Crvenka') were basic raw materials for _Pat-Nat_ ciders in this study. The basic apple must and cider parameters were determined by applying OIV methods and nitrogenous compounds, total phenols, and color parameters were analyzed by spectrophotometer. Volatile compounds in final _Pat-Nat_ ciders were determined by SPME-Arrow-GC/MS method and Odor Active Values (OAV) were calculated. The results show that variety significantly altered the pH value, color, aromatic and sensory profile of _Pat-Nat_ ciders. The main contributors (OAV > 1) to the aroma of all _Pat-Nat_ ciders were 1-hexanol, 1-propanol, (6Z)-nonen-1-ol, 1-dodecanol, hexanoic, octanoic and isovaleric acid, citronellol, ethyl hexanoate, ethyl 9-deconate and isoamyl acetate, eugenol and methionol. 'Boicinca' _Pet-Nat_ was differentiated by a high concentration of 1-decanol and 4-ethylphenol, 'Bobovac' by 4-vinyl guaiaicol and 'Crvenka' by 4-ethyl guaicol. Sensory analysis showed that the highest rated overall quality was attributed to 'Crvenka' _Pet-Nat_ cider, with the high-quality color, fruity odor ('apple', 'apple juice/compote', 'pinepple', and 'buttery') and well-balanced taste. This research demonstrates the possibilities in the production of natural sparkling cider from traditional Croatian apple varieties by analyzing the composition and quality of the final product for the first time.  
cider technology; OAV; sparkling cider; spontaneous fermentation; volatile compounds; sensory attributes Article