esa-sceva/satcom-chunk-collection huggingface dataset:
# Refinement Orders for Quantitative Information Flow and Differential Privacy +
### Contribution  
The main contributions of this paper are the following:  
* We introduce two refinement orders for the max case, \(\sqsubseteq^{\max}_{\mathbb{Q}}\) and \(\sqsubseteq^{\text{prv}}_{\mathbb{M}}\), that are robust with respect to a large class of adversaries.
* We give structural characterizations of both \(\sqsubseteq^{\max}_{\mathbb{Q}}\) and \(\sqsubseteq^{\text{prv}}_{\mathbb{M}}\) in terms of relations on the stochastic matrices of the mechanisms under comparison. These relations help the intuition and open the way to verification.
* We study efficient methods to verify the structural relations above. Furthermore, these methods are such that, when the verification fails, they produce counterexamples. In this way, it is possible to pin down what the problem is and try to correct it.
* We show that \(\sqsubseteq^{\text{avg}}\subset\sqsubseteq^{\max}\subset\sqsubseteq^{\text{ prv}}\).
* We apply the three orders (\(\sqsubseteq^{\text{avg}}_{\mathbb{G}}\), \(\sqsubseteq^{\max}_{\mathbb{Q}}\), and \(\sqsubseteq^{\text{prv}}_{\mathbb{M}}\)) to the comparison of some well-known families of \(d\)-private mechanisms: geometric, exponential and randomised response. We show that, in general, \(A\sqsubseteq^{\text{avg}}_{\mathbb{G}}B\) (and thus all the refinement orders between \(A\) and \(B\)) holds within the same family whenever the \(\varepsilon\) of \(B\) is smaller than that of \(A\).  
* We show that, if \(A\) and \(B\) are mechanisms from different families, then, even if the \(\varepsilon\) of \(B\) is smaller than that of \(A\), the relations \(A\sqsubseteq_{G}^{\text{avg}}B\) and \(A\sqsubseteq_{\mathbb{Q}}^{\text{max}}B\) do not hold, and in most cases \(A\sqsubseteq_{\mathbb{M}}^{\text{prv}}B\) does not hold either. We conclude that a comparison based only on the value of the \(\varepsilon\)'s is not robust across different families, at least not for the purposes illustrated above.
* We study lattice-properties of these orders. In contrast to \(\sqsubseteq_{G}^{\text{avg}}\), which was shown to not be a lattice, we prove that suprema and infima exist for \(\sqsubseteq_{\mathbb{Q}}^{\text{max}}\) and \(\sqsubseteq_{\mathbb{M}}^{\text{prv}}\), and that therefore these orders form lattices.
## 3 Three-Phase Unbalanced Distribution Network Fault Location Algorithm  
### Distribution Network Model in Normal and Faulty Operating States  
Figure 1 depicts the three-phase line's type equivalent admittance model in normal operation between any two nodes \(i\)_j_ in the three-phase distribution network. \(Y_{ij}^{\mathit{an}}\) represents the line admittance of the a-phase between node \(i\) and \(j\); \(Y_{ii}^{\mathit{an}}\) represents the mutual admittance between the a-phase and b-phase; \(Y_{i}^{\mathit{an}}\) represents the a-phase-to-ground admittance of node \(i\); \(Y_{i}^{\mathit{ab}}\) represents the admittance between the a-phase and b-phase at node \(i\). Therefore, taking nodes \(i\) and \(j\) as an example, the network should conform to the following equation when it operates normally:  
\[I^{\bar{q}}=Y^{\bar{q}}U^{\bar{q}} \tag{8}\]where \(I^{\varphi}\) is the three-phase injection current of the node, \(Y^{\varphi}\) is the node admittance matrix, and \(U^{\varphi}\) is the three-phase voltage of the node.  
The failure of a distribution line can be compared to expanding the original model by a virtual node and injecting current into the faulty line from this virtual node. As shown in Figure 2, when the c-phase line between nodes \(i\) and \(j\) is faulty, the equivalent fault model can be changed to have one equivalent grounding current source at the c-phase at both nodes in order to lessen the effect of virtual nodes on subsequent calculations. When the phase line fails, the equivalent current source will be non-zero, whereas when the system operates normally, the injection current of the equivalent current source to the node is zero.  
When the line fails, the three-phase injection current of the node and the three-phase voltage of the node have the following relationship:  
\[I^{\varphi^{\prime}}=Y^{\varphi}U^{\varphi^{\prime}} \tag{9}\]  
where \(I^{\varphi^{\prime}}\) and \(U^{\varphi^{\prime}}\) are the node current and voltage after the fault occurs.  
The different wiring modes of the transformer also affect the short-circuit fault characteristics differently because it is the main device for connecting various voltage levels. The common wiring mode of a low-voltage distribution transformer is Dyn11, that is, the voltage phase angle changes by \(30^{\circ}\) when the primary side phase voltage is converted into the secondary side line voltage. When a short-circuit fault occurs in the medium-voltage side line of the distribution network, the voltage sag value caused by it propagates to the low-voltage side and is not directly propagated according to the transformation ratio. The  
Figure 1: Normal model of distribution networks.  
Figure 2: Single-phase-to-ground fault model of distribution networks.  
change in fault characteristics is shown in Table 1. Conversely, when a short-circuit fault occurs on the low-voltage side of the distribution network, due to the large leakage reactance of the transformer, the impact on the primary side can be ignored after the transformer is isolated. The propagation effect of the transformers with other wiring modes on the system feeder fault is shown in [29].
# On the Capacity of Optical Backbone Networks
## 2 Basics on Optical Networks and Physical Impairments
### Major Physical Impairments
Despite this situation, there are several methods available in the literature that can be used to mitigate the impact of this impairment (the cascading effects of ROADMs) [21]. Crosstalk in optical networks refers to unwanted interferences between different optical channels. This interference occurs due to imperfect isolation of optical devices like WSSs, resulting in signal leakages responsible for the impairment. Furthermore, crosstalk accumulates as the optical channel traverses multiple network nodes, which increases its damaging effect [22]. However, it is possible to reduce crosstalk to a negligible level by properly designing ROADMs and selecting high-quality WSSs.
## 2 Materials and Methodology
### Data and Variables
#### 2.1.3 Input and Output Variables  
This paper includes two types of input variables, one of which is trajectory variables. These variables have been demonstrated to play a significant role in modeling car-following behavior [33]. That is listed in the following Table 5.  
Another type of input variable is heterogeneity. Past researchers have stated that traffic flow and car-following combinations will affect car-following behavior [19; 24; 34; 35; 36]. In this paper, different traffic flows are represented by the mean velocity of the lane. Using 80 km/h and 100 km/h as the threshold, the traffic flow is divided into three situations: high flow, medium flow, and low flow. There are four types of car-following combinations: Car-Car, Car-Truck, Truck-Car, and Truck-Truck. When using categorical variables as input features in predictive models, it is necessary to encode them into numerical values.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **High Flow 1** & **Middle Flow** & **Low Flow** \\ \hline Car–Car 2 & 55,329 & 67,853 & 6123 \\ Car–Truck & 1768 & 6064 & 5086 \\ Truck–Car & 1565 & 9579 & 11,293 \\ Truck–Truck & 781 & 22,683 & 19,293 \\ \hline \hline \end{tabular} \({}^{\dagger}\)High flow, middle flow, and low flow denote the different traffic conditions. 2 Car–Car denotes that the following vehicle is a car, the heading vehicle is a car, and others are similar.  
\end{table}
Table 4: Overview of the dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbols** & **Meaning** & **Unit** \\ \hline \(v_{ego}\) & The longitude velocity of the following vehicle & \(m/s\) \\ \(v_{rel}\) & The relative velocity between FV and HV & \(m/s\) \\ \(d\) & The distance between FV and HV & \(m\) \\ \(thv\) & Time headway & \(s\) \\ \(TTCi\) & The reciprocal of TTC (time to collision) & \(s^{-1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Input variables based on trajectory data.  
The appropriate encoding method will be an important consideration in the development of the model. This is discussed in Section 2.2.  
The output variable is the longitudinal velocity after Ts. The model expression is as follows:  
\[\begin{array}{l}\dot{g}(t)=f(\overset{\rightarrow}{X}^{(t)}),\\ \overset{\rightarrow}{X}^{(t)}=[v_{ego}^{(t)}v_{rel}^{(t)},a^{(t)},thw^{(t)},TT Ci^{(t)},h_{t},h_{c}],\\ \dot{g}(t)=\hat{\varrho}_{ego}^{(t+T)},\end{array} \tag{3}\]  
where \(h_{t}\) denotes the categorical variable of traffic flow, \(h_{t}\) denotes the categorical variable of car-following combination, and \(f(\cdot)\) denotes the implicit function.
# Opportunistic Network Algorithms for Internet Traffic Offloading in Music Festival Scenarios
## 3 A Festival Mobility Model
### The Sonar Festival Dataset
#### 3.1.2 Challenges  
For validating our proposed solution, we used MobEmu [14], a mobile interaction tool that is able to simulate the behavior of mobile nodes and the way they interact with each other. For this reason, the Sonar festival trace data had to suffer some transformations. Some of the filters applied were:  
* Duplicate filter--we had to delete the duplicates from the dataset, caused by clock synchronization errors and buffering between the server and the scanners;
* Format filter--we had to convert the anonymized MAC address to a node identifier format supported by the simulator; the end goal of parsing the dataset was to obtain contacts between nodes, which are the centerpiece structures of a mobility trace; a contact is an interaction between two nodes, which has a start and end timestamp and during which messages are exchanged following a communication protocol;
* Time filter--we checked the data for outlier values and validated that all timestamps were between 9:00 a.m. and 12:00 a.m. Looking at the data, we noticed some anomalies:
* Some of the timestamps were between 12:00 a.m. and 9:00 a.m., although the researchers mention they had only collected data between 12:00 p.m. and 10:00 p.m.; we chose not to include data recorded during the night in the final dataset;
* Some of the nodes were observed consistently in the same place for hours; these data points might exist because the main stage was surrounded by food trucks and other non-music related attractions; naturally, if a node spent around 11 h at a location, it can be assumed that it was the phone (or other device with Wi-Fi connectivity) of one of the staff members;
* Some of the nodes were observed for a brief period of time in a location, so we discarded them. After computing the time a node spent at the festival each day, the following decisions were reached:
* Each day, there were a significant number of nodes present at the festival for less than 60 min; whatever the causes might be, we have chosen not to consider these nodes as contact-worthy (in order to account for communication or data collection errors that might affect and alter our conclusions);
* Although there were around 100 nodes that spent more than 10 h at the festival each day, we considered them for contact opportunities only if that time was not spent at the same location.  
The previous conclusions are supported by Figure 1, which shows the outlier values for the times spent by a node at the festival on the first day (the charts for the second and third days are very similar).
# Short-Term Traffic Flow Forecasting via Multi-Regime Modeling and Ensemble Learning
## 2 Literature Review
Several researchers have noticed this problem, and tried to solve it by adopting some prior feature selection strategies [15; 16]. Similarly, we presented a short-term traffic flow forecasting model using a data-driven feature selection strategy and bias-corrected random forests [24], which shows an excellent forecasting performance and good interpretability. As the well-known \"No Free Lunch\" theorem [25] states, there is no one model can work best for every problem. Furthermore, the above analyses indicate that each of the analyzed models has its own advantages and limitations. To compare different forecasting models from distinct viewpoints, some researchers conducted comprehensive and deep literature reviews on traffic flow forecasting [1; 26; 27; 28]. Interested readers can refer to these studies for more comparison details of various models. A key issue in traffic flow forecasting is how to properly define and capture the temporal patterns of traffic flow. To effectively handle this issue, two categories of coping strategies are commonly adopted. The first is a detrending strategy, which is to build forecasting models by separating the trends of traffic flow time series from the remaining fluctuations, and developing distinct models to describe the trends and fluctuations, respectively. For instance, the SARIMA model assumes that there is a weekly or monthly cyclic trend in the traffic flow [29]. [PERSON] et al. [30] studied different forecasting models built on either the original traffic flow sequence or the remaining time series. They discovered that in the latter case, a significant performance improvement can be achieved. The second is a multi-regime strategy, which assumes that different patterns are contained in traffic flow dynamics. In some relevant studies [31; 32; 33], researchers divided the traffic flow data into several regimes according to distinct patterns, and established separate models to characterize the traffic flow dynamics in each regime. The associated results demonstrate the potential of the multi-regime strategy. A comprehensive comparison study with the two strategies was conducted by [PERSON] et al. [6]. They indicated that the multi-regime strategy could be utilized to improve the forecasting performance when using an accurate regime identification model. With this in mind, in the subsequent sections, we will develop an effective traffic flow forecasting model based on a multi-regime strategy, which establishes the regime identification model by probabilistic modeling. In addition, an ensemble learning mechanism is introduced to further improve the performance of the developed forecasting model.
## 4 Implementing Models with the Geographic Automata Add-In: Two Case Studies
### Case Study 1: Multi-Class CA Modeling of a Forest Insect Infestation
#### 4.1.1 Study Area and Datasets  
This case study focuses on a location spanning 130.29 km\({}^{2}\) within Manning Provincial Park, situated in the Cascade Mountains of British Columbia, Canada. To initialize the model scenarios configured with the _Geographic Automata_ add-in tooling, real-world datasets were acquired from the BC Data Catalogue [90]. These include an initial infestation map of the infested areas in 2004 [91], MPB susceptibility indices [92], and water bodies. The MPB susceptibility indices were obtained from the \"Bark Beetle Susceptibility Rating\" dataset [92], which contains hazard ratings derived from key factors including basal  
Figure 7: An overview of the _Model Evaluation_ tool interface and the metrics available.  
area, age, density, and location of pine trees. To prepare each dataset for this case study, each of the original vector datasets was clipped to the extent of the study region. Next, the vector datasets were rasterized to a 30 m spatial resolution, each with 384 rows and 377 columns (Figure 8). Maps are displayed with the NAD 1983 UTM Zone 10N projected coordinate system.
## 3 Results
### Seasonality as Seen by ALOS-2/Palsar-2  
The longer penetration depth at L-band in comparison to that of C-band allowed interpreting ALOS-2/PalsAR-2 \(\sigma^{0}\) in terms of subsurface scattering rather than surface scattering. For ALOS-2/PalsAR-2, variations in both HH- and HV-polarized backscattered power from 2014 to 2018 are shown in Figure 12 for type 2 Hard and Soft crusts, and Figure 13 for type 1 Hard crust. Images were acquired on ascending passes only with local incidence angles ranging from 30\({}^{\circ}\) to 35\({}^{\circ}\). In what follows, seasonal, annual, and episodic temporalities were analyzed together.  
Figure 11: Sentinel 1 temporal backscattering observed over the salt pan for the remaining sites considered in this study. Sentinel 1A (magenta) and Sentinel 1B (blue) in ascending (full markers) and descending (empty markers) orbits. VV polarization is indicated as triangles, and VH are circles. The green circles indicate episodic events: on 13 April 2015, for sites S4, S5, S7, S8, S9, and 20 April 2016, for site S8. Daily accumulate rainfalls recorded at Sociare are in grey bars, and snowfall cover also shown as black crosses.  
Overall, hard crust 2 (Figure 12, upper panel) showed a higher \(\sigma^{0}\) than soft crust (Figure 12 bottom panel) because of the difference in surface roughness. The snowfall during the winter of 2017 and its subsequent melting during the spring, contributed significantly to change the \(\sigma^{0}\) in several sites. In some of them, it leads to a high backscatter coefficient (S12, S1, S8), which arises from the rise of the brine layer, thus resulting in a moistened medium with high dielectric constant.  
Sites distributed over hard crust 1 (Figure 13) had differences from each other. The higher \(\sigma^{0}\) are observed over Sites S5 and S7. These sites are located over elevated grounds, which prevented them from flooding and salt dissolution, except under heavy rains. On the other hand, \(\sigma^{0}\) decreased in S10 since its surface flooded and S11 surrounded flooding areas seemed to be influenced by those events decreasing \(\sigma^{0}\) during 2017-2018 springtime.  
Figure 12: ALOS-2/PALSAR-2 temporal backscattering observed over the salt pan. Sites corresponded to Hard crust 2 (upper panel) and Soft crust (bottom panel). Ascending orbit. The local incidence angles are indicated in degrees. Daily accumulate rainfalls recorded at Sociare are in grey bars, and snowfall cover also shown as black crosses.  
Figure 13: The same as Figure 12 for sites corresponding to Hard crust 1. Ascending orbit. The local incidence angles are indicated in degrees. Daily accumulate rainfalls recorded at Sociare are in grey bars, and snowfall cover also shown as black crosses.
# FPGA-Based Pulse Compressor for Ultra Low Latency Visible Light Communications
## 3 Communication Protocol
### Format of the Data Packet  
The data to be transmitted is organized in 24-bit packets with the simple format sketched in Figure 3. Data to be transmitted (the payload) is subdivided in words of 16-bit each (i.e., 2-byte). A fixed preamble of 4-bit \"1111\" is added before the payload. It will help the receiver to synchronize to the packets' boundaries. Finally, a 4-bit Check Redundancy Code (CRC) is appended at the end of each packet, so that the receiver can check the data integrity. In Figure 3, the packet is represented with bits P\({}_{23\cdot 0}\), the payload is W\({}_{15\cdot 0}\), and the CRC has bits C\({}_{3\cdot 0}\).  
The CRC is calculated by subdividing the preamble and payload in 5 sub-words of 4-bit each, that are added. Carry to the 5 th bit is discarded in the process:  
\[CRC=\sum_{i=0}^{5}P_{ii+3}P_{4i+2}P_{4i+1}P_{4i+0} \tag{8}\]  
The 24-bit packets are queued one after the other, with no delay in between. The sequence produces a bitstream that is coded with the chirps, like described above.
# An Analysis of Methods and Metrics for Task Scheduling in Fog Computing
## 5 Taxonomy for Task Scheduling Algorithms in Fog Computing
### Problem Solution Techniques  
In a fog-based IoT environment, application service requests can be made at the level of the IoT device, the fog servers, or the cloud data centers [67]. Proper task scheduling algorithms determine the best allocation of incoming tasks for each application service, which may consist of a sequence of tasks. We propose a task-scheduling algorithm classification based on the technique used for problem formulation and solution. The categorization is presented in Figure 7.
# A Detailed Review on Foam Concrete Composites: Ingredients, Properties, and Microstructure
## 2 Compositions of Foam Concrete
### Aggregates  
Coarse aggregates are not deployed in the production of foam concrete. Generally, fine aggregates with a maximum particle size of 5 mm are used. The main reason for this can be that coarse aggregates settle in the lightweight mix and cause the foam to settle during mixing. In addition, fine aggregates should not contain harmful reactive substances that will cause the concrete to expand [4,50]. The replacements of aggregates, in terms of sustainability, partially or completely with recycled materials (such as glass waste, lime, broken concrete, ceramic pieces, and waste marble dust) have positive effects on foam concrete properties [4]. The workability of foam concrete decreased with the use of WMP as fine aggregate [5]. The use of RCA as a fine aggregate due to its empty texture increased porosity [51]. The stability increased with the replacement of sand with RCA as fine aggregate. It contributed to the formation of a more stable system by improving yield strength. However, with the increase in the amount of RCA, a decrease in strength occurred [19]. The optimal utilization ratio suggested of waste clay brick as a coarse aggregate is 25%. However, as the ratio is increased, the water absorption is increased and the compressive strength is decreased [52]. The use of various aggregate types in foam concrete is given in the Table 2.
## 2 The 3D A2A MIMO Channel Model
### Specular and Diffuse Components  
The channel matrix for the NLoS components is given as  
\[\mathbf{h}_{\mathrm{NLoS}}(t,\tau)=\mathbf{v}_{r}(\vartheta_{n},\varphi_{n}) \mathbf{v}_{t}^{\mathrm{H}}(\theta_{n},\phi_{n})\cdot\sum_{n=0}^{N}\sqrt{P_{n }}e^{-\frac{j\pi}{\lambda}d_{n}(t)}e^{j\xi_{n}}\delta(\tau-\tau_{n}(t)) \tag{27}\]  
where \(\theta_{n}\), \(\phi_{n}\), \(\phi_{n}\), and \(\varphi_{n}\) denote the ZoD, ZoA, AoD, and AoA of the \(n\)th ray, respectively. For the sake of conciseness, the subscript \(n=0\) indicates the specular ray, and \(n=1,...,N\) refers to the diffuse rays. Furthermore, \(d_{n}(t)\) and \(\tau_{n}(t)=d_{n}(t)/c\) are the time-varying length and delay of the \(n\)th ray, respectively. The phase shift \(\xi_{n}\) is caused by the interaction of the \(n\)th wave with the ground and is obtained as \(\xi_{n}=\arg(\Gamma_{n})\), where \(\arg(\cdot)\) returns the angle of a complex number and \(\Gamma_{n}\) is the complex reflection coefficient [37]. The modeling details of the above-mentioned parameters are described in the rest of this paper.