esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Attenuation and group delay effects on millimeter wave (MMW) propagation in clouds and fog are studied theoretically and verified experimentally using high resolution radar in an indoor space filled with artificial fog. In the theoretical analysis, the frequency-dependent attenuation and group delay were derived via the permittivity of the medium. The results are applied to modify the millimeter-wave propagation model (MPM) and employed to study the effect of fog and cloud on the accuracy of the Frequency-Modulated Continuous-Wave (FMCW) radar operating in millimeter wavelengths. Artificial fog was generated in the experimental study to demonstrate ultra-low visibility in a confined space. The resulted attenuation and group delay were measured using FMCW radar operating at 320-330 GHz. It was found that apart from the attenuation, the incremental group delay caused by the fog also played a role in the accuracy of the radar. The results were compared to the analytical model. It was shown that although the artificial fog has slight different characteristics compare to the natural fog and clouds, in particle composition, size, and density, the model predictions were good, pointing out that the dispersive effects should be considered in the design of remote sensing radars operating in millimeter and sub-millimeter wavelengths.  
Keywords:extremely high frequencies; FMCW radar; atmosphere; millimeter waves; Tera-Hertz frequencies +
Footnote †: journal: Sensors
# Efficient Rule Generation for Associative Classification
## 3 Basic Definitions  
Let \(A=\{a_{1},a_{2},\ldots,a_{m}\}\) be a finite set of all attributes in dataset. \(C=\{c_{1},c_{2},\ldots,c_{n}\}\) is a set of classes, \(g(x)\) is a set of transactions containing itemset \(x\), and \(|g(x)|\) is the number of transactions containing \(x\).  
**Definition 1**.: _An item can be described as an attribute \(a_{i}\) containing a value \(v_{j}\), denoted as \((a_{i},v_{j})\)._  
**Definition 2**.: _An itemset is the set of items, denoted as \((a_{i1},v_{i1}),(a_{i2},v_{i2}),...,(a_{ik},v_{ik})\)._  
**Definition 3**.: _A ruleitem is of the form \(\langle\mathit{itemset},c_{j}\rangle\), which represents an association between itemsets and class in a dataset; basically, it is represented in the form itemset \(\rightarrow\)\(c_{j}\)._  
**Definition 4**.: _The length of a ruleitem is the number of items, denoted as \(k-\mathit{ruleitem}\)._  
**Definition 5**.: _The absolute support of ruleitem \(r\) is the number of transactions containing \(r\), denoted as \(sup(r)\). The support of \(r\) can be found from (1)._  
\[sup(r)=|g(r)| \tag{1}\]  
**Definition 6**.: _The confidence of ruleitem \(\langle\mathit{itemset},c_{j}\rangle\) is the ratio of the number of transactions that contains the itemset in class in \(c_{j}\) and the number of transactions containing the itemset, as in (2)._  
\[conf(\langle\mathit{itemset},c_{j}\rangle)=\frac{|g(\langle\mathit{itemset},c_ {j}\rangle)|}{|g(\mathit{itemset})|}\times 100 \tag{2}\]  
**Definition 7**.: _Frequent ruleitem is a ruleitem in which support is not less than the minimum support threshold (\(\mathit{minsup}\))._  
**Definition 8**.: _Class Association Rule (CAR) is a frequent ruleitem in which confidence is not less than the minimum confidence threshold (\(\mathit{minconf}\))._
# Smoothed Particle Hydrodynamics Simulation of Orthogonal Cutting with Enhanced Thermal Modeling
## 5 Results & Discussion
### Preliminary Study  
In this example, the Laplacian of \(f(x,y)=x^{2}+y^{2}\) is computed in a domain of \((x,y)\in[-1\ +1]\) discretized by \(21\times 21\) uniformly spaced particles, for which the analytical answer is given by \(\
abla^{2}f(x,y)=+4\). This choice was made to verify the second-order consistency of the FMFS approximation. As mentioned before, the cubic B-spline kernel with a smoothing length of \(h=1.5\Delta x\) is used.  
Figure 3 demonstrates the surface plots of numerical solutions, where FMFS recovers second-order consistency by producing exact results throughout the domain. In the case of using the reference SPH method, as shown in the middle plot of Figure 3, severe inconsistencies resulting from the boundary deficiency of SPH exist outside the range of \([-0.8\ +0.8]\). The computations show that the Laplacian result using the reference SPH model is approximately +4.027 within \([-0.8\ +0.8]\). Outside this range, the approximation error of \(\langle\
abla^{2}f\rangle\) increases drastically and reaches its maximum value at four corners. The approximate value of Laplacian at these points is \(-22.12\), corresponding to a relative error of 453%. As discussed already, the FMFS method employed by the proposed thermal model resolves this issue by incorporating higher-order terms and kernel renormalization, giving exact results with no error.  
This preliminary investigation indicates that boundary effects can deteriorate the accuracy of the SPH Laplacian operator. Indeed, this situation occurs in metal cutting problems, where the thermal boundary conditions are associated with high surface temperatures, especially in the chip formation zone. By applying FMFS in the proposed thermal model, the heat equation in SPH metal cutting simulations is solved more accurately at the price of a higher computational cost.
## 2 Operation of the Fast-Charging Station
### Station Operation  
The researched DC PEB fast-charging station served two loop lines, H55 and H56, and three other lines operated in the origin-terminal mode, lines 863, 864, and 869. The details of the PEB lines are shown in Table 2.  
For the constant operation rules of the bus lines, the charging load is similar day by day from the recorded charging data in the same season. This paper selected the data of a normal working day to analyze the charging load characteristics. Because of the shortage of measurements at the station, we obtained the total charging power based only on the recorded charging duration and electricity quantity. Through the charging power accumulation of each PEB, the charging load is shown in Figure 2 in the unit of minutes.  
Although a great number of charging stations have been designed and deployed in different cities in China in recent years, most stations are designed and constructed according to the slow charging mode or fast-swapping mode. For insufficient design experience on a fast-charging station, the design on the distribution capacity and the quantity of charging sticks is based only on the existing rules  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Vehicle Brand & Foton \\ Vehicle Type & BJ612 EVCA-19 \\ Total Weight (kg) & 18,000.00 \\ Shape (L \(\times\) W \(\times\) H) (mm) & 12,000 \(\times\) 2550 \(\times\) 3100 \\ Battery Voltage (V) & 553.80 \\ Battery Capacity (Ah) & 216.00 \\ Electric Quantity (kWh) & 119.62 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Pure electric bus parameters.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Line No.** & **Num.** & **Distance** & **Time** & **Loops before** \\  & & **(km)** & **(minutes)** & **Charging** \\ \hline H55 & 5 & 9 & 30 & 3 or 4 \\ H56 & 5 & 12 & 30 & 3 or 4 \\
863 & 14 & 54 & 120 & 2 \\
864 & 7 & 18 & 35 & 2 \\
869 & 14 & 44 & 120 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The operation details for each line.  
Figure 2: Total charging load of the station.  
and considers the extreme charging conditions. Furthermore, all of the PEBs are charged randomly according to the driver's will. Therefore, the peak charging power is huge and the charging load fluctuation is serious. The load curve is shown in Figure 2. The peak charging power was 1601.7 kW and the average charging power was only 284.93 kW. The load rate was 17.79% and the total quantity of the charging electricity was 5128.8 kWh in one day. The operation duration of the station was 16 h, from 6:00 a.m. to 10:00 p.m. Compared with the station configuration and distribution capacity, the real demands of the charging power and facilities were not as high as the designed estimation. The charging station was not well used, which is a huge waste of capital investment and distribution resources.
# Cosmo ArduSiPM: An All-in-One Scintillation-Based Particle Detector for Earth and Space Application
## 3 Overview of the Discrete Analog Functional Blocks of ArduSiPM
### Optical Test
#### 4.2.2 The Maximum Counting Rate in the Optical Bench  
The maximum possible rate for the Cosmo ArduSiPM readout is evaluated in a similar way used for electrical tests. This measurement takes into account the capacity of SiPM and the limit due to pulse width. To simulate a typical scenario, we use a pulse consisting of a few photons. Due to the stochastic nature of LED light emission, there is some variability in the number of photons per pulse, which was set to an average of eight to nine photons. This is achieved by manipulating the solid angle as viewed by the detector in relation to the source, taking advantage of the fact that the source has a specific beam angle and the SiPM (silicon photomultiplier) intercepts a fraction of this solid angle.  
Until 40 MHz, the Cosmo ArduSiPM is able to measure the pulse rate without errors. Since we used a 20 ns width pulse in the electrical test, we expected this result, even if the SiPM signal has a longer tail.
# Comparative Study of Blockchain Hashing Algorithms with a Proposal for HashLEA
## 3 Background
### Hash Functions
#### 3.3.9 Fugue  
Fugue is a cryptographic hash function developed by [PERSON], [PERSON], and [PERSON]. It was submitted to the NIST hash function competition as a candidate for the next generation of secure hashing standards. Fugue is capable of supporting variable-length inputs of up to \(2^{64}-1\) bits while producing fixed-length hash outputs, including 224, 256, 384, or 512 bits. These variants are designated Fugue-224, Fugue-256, Fugue-384, and Fugue-512, respectively [70]. Unlike traditional hash functions designed using the Merkle-Damgard paradigm, Fugue follows a different approach that avoids the inherent vulnerabilities of the Merkle-Damgard construction, such as multi-collision and herding attacks. Rather than depending on a compression function, Fugue maintains a substantial evolving internal state and utilizes an innovative \"super-mix\" operation to achieve diffusion across the state. The algorithm includes the following features [71]:  
* In Fugue, the design adopts and extends the Grindahl approach [72] by maintaining a large, evolving internal state and leveraging AES-like primitives for state evolution.
* Building on the AES-inspired design of Grindahl, Fugue replaces AES's 4 \(\times\) 4 column mixing matrix with a 16 \(\times\) 16 \"super-mix\" operation. This transformation improves diffusion while maintaining computational efficiency. The super-mix involves multiplying a 16-byte vector by a 16 \(\times\) 16 matrix.
* The rounding function is applied selectively to parts of the state rather than uniformly. This selective approach is combined with XOR-based mixing.
* Computational methods have been used to verify that differential cryptanalysis and related attacks cannot find collisions in Fugue faster than the simple birthday attack.
# WiFi Positioning in 3 GPP Indoor Office with Modified Particle Swarm Optimization
## 5 Simulation Results
The distance between the SPs of the RL-PSO scheme is 3 m, and there are a total of 697 SPs, as shown in Table 2. The number of particles of the particle filter is 697, the same as the number of SPs of the RL-PSO. As can be seen from the results of Table 4, the processing time of the RL-PSO is shorter. The RL-PSO can position the user by performing the RSSI-based positioning process once, but the particle filter is a sensor-based positioning method of the UE and moves particles according to the movement of the UE to the position the user. Although the above result is the processing time obtained through simulation, it can be seen that a longer processing time is required for positioning, considering that the user's moving speed is about 3 to 5 km/h in the real environment. Figure 9 shows the cumulative distribution function(CDF) of the positioning error according to the distance between SPs. In the figure, it can be seen that when the distance between SPs is 3 m, about 90% of the positioning errors are within 1.5 m. However, it can also be seen that the positioning error increases as the distance between SPs increases. This is because when the number of iterations of PSO is fixed, as the distance between SPs increases, the region where particles need to be searched becomes wider. Therefore, it is necessary to set the distance between SPs in consideration of the algorithm processing time and target positioning accuracy. \begin{table}
\begin{tabular}{c c} \hline \hline
**Scheme** & **Average Processing Time** \\ \hline Particle Filter [15] & 0.50162 \\ RL-PSO & 0.15314 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of average processing time of each scheme to achieve positioning error of 1 m.  
Figure 8: Positioning error according to distance between SPs.
# Enhancing Energy Efficiency in IoT-NDN via Parameter Optimization
## 6 Evaluation
### Results of the Simulation
#### 6.2.1 Relative Energy Deviation  
The relative energy deviation of all parameters is small. Specifically, the deviation of the _packet size_ is frequently approx. 2 to 4%, as can be seen in Figure 3(a). An exception to that is the complete topology (Figure 3(b)), which has produced much smaller deviations. The next most significant one is the caching scheme: It has a deviation of at most 0.15%. After that, the replacement strategy deviates by approx. 0.01% for many combinations, but some outliers reach 0.02%. The forwarding strategy presents the smallest energy footprint with a maximum deviation of approx. 0.0006%.  
Analyzing the concrete relative or absolute differences in energy consumption is not very interesting due to the minor impact. For that reason, the analysis of the parameters will be primarily qualitative, not quantitative: So the remainder of this subsection will  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Amount** & **Unit** \\ \hline Energy per node & 235.515 & J \\ Total consumed energy & 3768.24 & J \\ Standard deviation & 81.3037 & J \\ Min energy & 81.9 & J \\ Max energy & 409.604 & J \\ \hline \hline \end{tabular}
\end{table}
Table 1: An example of a results.csv file for batch-1/graph-size=16/run0/run0.  
Figure 4: Two examples for the data packet size: The average energy consumption of packets sizes 128, 1024, and 8192 is compared to the overall mean of the corresponding fixed parameter combination. The black line represents the mean (100%).  
focus on explaining what parameters and parameter values impact energy consumption the most.
## 2 The Ubiquitous Sensor Network Platform
### The USN-Enabler
* An improved discovery mechanism: Standard OGC discovery for all services is defined through a PULL-based discovery mechanism that retrieves a list of results specifying some aspects of the sensors but not a full description, nor is a filter criterion allowed. In the USN-Platform the PULL-based interaction allows filtering based on the content of the fields in SensorML. In addition to query results, the USN-Enabler can return SensorML descriptions and enable PUSH-based interaction by allowing applications to subscribe to alerts of changes in SensorML documents that match certain criteria. * Improved subscription: SES does not allow subscriptions to changes in SensorML descriptions; the USN-Platform does. The platform is also able to increase the granularity of notification channels to the level of gateways (e.g., all sensors accessible through a particular gateway) or even down to a single sensor (e.g., all measurements provided by sensor \"X\"). * Refined observation retrieval: SOS defines a PULL-based mechanism to recover information regarding observations that meet certain criteria (e.g., recover only observations collected from a specific sensor, related to a particular feature of interest, and obtained under given spatial or temporal restrictions). However, more complex conditions (such as temp \(>\)25 \(\,\)C) are not specifiable. An extension has been defined in the USN-Platform that allows using the same filtering criteria as for PUSH-based interactions.
# The Study on the Text Classification Based on Graph Convolutional Network and BiLSTM +
## 3 Proposed Method
### Feature Fusion and Classification
#### 3.3.4 Sentence Level Feature Extraction Layer  
Since a text contains multiple sentences, there are semantic dependencies between each sentence. Therefore, the main task of the sentence-level feature extraction layer is to extract the semantic dependency information between sentences within the text in units of sentences. The sentence vector \(\mathbf{s}_{\text{i}}\) obtained by the contextual attention layer is input into a layer of BiLSTM for sentence sequence learning. Where \(\overrightarrow{\mathbf{h}_{\text{i}}}\) is the hidden state obtained by the forward LSTM layer through the forward input, \(\overleftarrow{\mathbf{h}_{\text{i}}}\) is the hidden state obtained by the backward LSTM layer through the reverse input, and finally, the two hidden states are spliced to obtain \(\mathbf{h}_{\text{i}}\).  
\[\overrightarrow{\mathbf{h}_{\text{l}}}=\overrightarrow{\text{LSTM}}( \mathbf{s}_{\text{i}},\overrightarrow{\mathbf{h}_{\text{t}-1}}),\text{ t }\in[1,\text{L}] \tag{19}\]  
\[\overleftarrow{\mathbf{h}_{\text{l}}}=\overleftarrow{\text{LSTM}}( \mathbf{s}_{\text{i}},\overleftarrow{\mathbf{h}_{\text{t}-1}}),\text{ t }\in[L,1] \tag{20}\]  
\[\mathbf{h}_{\text{i}}=\left[\overrightarrow{\mathbf{h}_{\text{l}}}, \overleftarrow{\mathbf{h}_{\text{l}}}\right] \tag{21}\]  
The deepening of the number of network layers can improve the accuracy of the model, but there are problems with the disappearance of the gradient of the deep network and the degradation of the network. When the depth reaches a certain level, the accuracy of the model begins to decline again, and the fitting ability becomes worse. In the deep neural network, the residual network can effectively inhibit the network degradation phenomenon and avoid the problem of sub-optimal solutions in deeper networks. Therefore, we introduce a residual connection between two adjacent layers and perform a residual operation on the sentence feature representation learned by the sentence-level feature extraction layer and the sentence feature representation output by the context attention layer. Let the model learn the residual to obtain a higher-level feature representation to achieve a better classification effect, as shown in Formula (22):  
\[\mathbf{O}_{\text{i}}=\mathbf{s}_{\text{i}}+\mathbf{h}_{\text{i}} \tag{22}\]  
Finally, the result \(\mathbf{O}_{\text{i}}\) is input to the Softmax layer for classification, and the final category of the text can be obtained.
# Towards Mobile Federated Learning with Unreliable Participants and Selective Aggregation
## 2 Background and Related Work
### Expensive Communications  
Client-client and client-server communication is a critical bottleneck in FL solutions [8]. When paired with privacy concerns over the transmission of raw data, it becomes necessary for the data generated on each side to remain local. It is undeniable that FL networks potentially include a huge number of devices, and network communications can be many orders of magnitude slower than local computations, due to limited resources such as bandwidth, energy, and power [19; 20].  
To fit a model to the data generated by devices in the federated network, it is important to develop communication-efficient methods that iteratively send small messages or model updates as part of the training process, rather than sending the entire dataset across the network. To reduce communication in such an environment, there are two key aspects to consider: minimizing the total number of communication rounds and the size of messages transmitted in each round [5; 20].
# Bio-Inspired Algorithms for Efficient Clustering and Routing in Flying Ad Hoc Networks
## 3 Preliminaries
### Network Models and Assumptions  
The network consists of \(N\) UAVs flying in three dimensions (3D) space, each represented by its position \((x_{i},y_{i},z_{i})\) and velocity \((Vx_{i},Vy_{i},Vz_{i})\). A simplified collision technique [24] is used in the simulation to avoid collision. In this simplified collision technique, the UAV changes its altitude in the event of a potential collision. A UAV can reach speeds up to 30 m/s at its highest point. UAVs are equipped with wireless communication interfaces, global positioning systems (GPS), and inertial measurement units (IMU). GPS and IMU are provided for the positioning and motion sensing of UAVs. UAVs communicate with each other if they are within the communication range \(R_{max}\). The distance between UAV \(i\) and UAV \(j\) is calculated as follows:  
\[d_{ij}=\sqrt{\left(x_{i}-x_{j}\right)^{2}+\left(y_{i}-y_{j}\right)^{2}+\left(z _{i}-z_{j}\right)^{2}}. \tag{1}\]  
If \(d_{ij}\leq R_{max}\), UAVs can communicate directly; otherwise, multi-hop communication is required. Every UAV is aware of its position, its neighbors' position, and the position of its ground station. Every UAV is assigned a distinct identifier (\(U_{ID}\)) for unique identification within the network. UAVs move predictably over short time intervals, allowing future position estimation based on current velocity. Mobility stability is sufficient for selecting stable CHs and maintaining cluster consistency. Ferry nodes periodically collect and distribute global information such as network congestion and UAV positions to support dynamic clustering and routing decisions. Congestion may occur when UAVs are concentrated in a small area, or when a CH is overloaded with traffic.  
Figure 1: Demonstration of UAV clustering and routing in FANETs: Cluster members (CMs) are connected to their respective cluster heads (CHs) within each cluster, while CHs communicate with each other. Furthermore, ferry UAVs establish high-throughput connectivity between CHs and the ground station.
# Time Domain Analysis of NB-IoT Signals
## 2 NB-IoT: Radio Interface
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**LTE Bandwidth** & **5 MHz** & **10 MHz** & **15 MHz** & **20 MHz** \\ \hline \multirow{9}{*}{NB-IoT anchor carrier offset to respect to LTE carrier} & \multirow{9}{*}{2, 12} & \multirow{9}{*}{2, 7, 17, 22} & \multirow{9}{*}{4, 9, 14, 19, 24, 29, 34, 35, 40, 45, 52, 57, 62, 67, 72} & \multirow{9}{*}{4, 9, 14, 19, 24, 29, 34, 35, 40, 45, 52, 57, 62, 67, 72} & \multirow{9}{*}{4, 9, 14, 29, 24, 47, 44, 55, 60, 65, 70, 75, 80, 85, 67, 72} & \multirow{9}{*}{4, 9, 14, 29, 24, 47, 48, 55, 60, 65, 70, 75, 80, 85, 67, 72} & \multirow{9}{*}{4, 9, 14, 29, 24, 47, 48, 55, 60, 65, 70, 75, 80, 85, 67, 72} & \multirow{9}{*}{4, 9, 14, 29, 24, 47, 48, 55, 60, 65, 70, 75, 80, 85, 67, 75, 80, 85, 67, 72} & \multirow{9}{*}{4, 9, 14, 29, 24, 47, 48, 55, 60, 65, 70, 75, 80, 85, 67, 75, 80, 85, 67, 72} & \multirow{9}{*}{4, 9, 14, 29, 24, 47, 48, 55, 60, 65, 70, 75, 80, 85, 67, 75, 80, 85, 67, 72} & \multirow{9}{*}{4, 9, 14, 29, 24, 47, 48, 55, 60, 65, 70, 75, 80, 85, 67, 75, 80, 85, 67, 72} & \multirow{9}{*}{4, 9, 14, 29, 24, 47, 48, 55, 60, 65, 70, 75, 80, 85, 67, 75, 80, 85, 67, 72} & \multirow{9}{*}{4, 9, 14, 29, 24, 47, 48, 55, 60, 65, 70, 75, 80, 85, 67, 75, 80, 85, 67, 75, 80, 90, 95} \\  & & & & 65, 70, 75,NRS is transmitted on symbols # 5,6,13,14 of each subframe except those where NPSS and NSSS transmission occurs. They are transmitted on 4 carriers, 45 kHz spaced, whose positions depend on the cell identifier (Cell_ID). Figure 5 shows the NRS mapping sequence in one or two ports use cases [34]. Figure 4: Allocation scheme of channels and signals in even frames (up) and odd frames (down) in guard-band and stand-alone mode [32]. Figure 5: Mapping sequence of NRS in one or two antenna port usage conditions [33].