esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of Downlink 5G Networks in Realistic Environments
## 2 Related Work
The authors argue that locations with many LISs can attain maximum capacity using a greedy scheduling technique and Monte Carlo simulation. For urban regions, a new route loss model was proposed in [27] that considersnovel characteristics, including window size, humidity, and temperature. In comparison with other models in the literature, the authors asserted that the proposed model increases the estimation of path loss by 10%. A capacity study of device-to-device (D2D) communication over mmWave 5G cellular networks was introduced in [28], taking into account real constraints such as transceiver distortion noise, channel state information (CSI), and interference from neighboring cells. Based on the numerical results, the authors concluded that transceiver distortion noise has a significant impact on the performance of future communication systems, particularly at high SNRs. In addition, the performance of such systems is compromised by the addition of more disruptive nodes to the network. To achieve the aim of maximizing sum spectrum efficiency, the authors in [29] have proposed a joint interference suppression scheme in heterogeneous networks with dense small cells and users considering co-tier intra-cell interference alignment. Due to the exponential growth of the output layer neurons faced by general deep reinforcement learning algorithms, the authors proposed a deep deterministic policy gradient-based algorithm to solve the problem. As per the provided numerical results, the authors claimed that the proposed algorithm is able to achieve better performance and wider application scope compared with existing algorithms. On the other hand, article [30] examines computational offloading in networks beyond 5G in response to the trend of combining wireless communications and multi-access edge computing. A distributed learning framework is proposed in order to overcome the technical difficulties arising from the uncertainties and the sharing of restricted resources in multi-access edge computing systems. Moreover, and in order to demonstrate the possibilities of an online distributed reinforcement learning algorithm created using the suggested architecture, the authors provided a case study on resource orchestration in computation offloading. As per the provided comparative experimental results, the authors show that the suggested learning method algorithm works better than the benchmark resource orchestration algorithms.
# AOA-Based Three-Dimensional Positioning and Tracking Using the Factor Graph Technique
## 4 Fg-Ekf  
The FG structure-based EKF is explained in this section. Based on (11) and (12), the goal of the tracking system is to obtain the values of \(\mathbf{s}_{k}\) and \(\mathbf{v}_{k}\), which allow the a posteriori probability \(p\) (\(\mathbf{s}_{k},\mathbf{v}_{k}|o_{1:k}\)) to be maximized, with \((\cdot)_{1:k}\) denoting time indexes. The following equation calculates the marginal function of \(\mathbf{s}_{k}\) and \(\mathbf{v}_{k}\), denoted by \(\hat{p}\) (\(\mathbf{s}_{k},\mathbf{v}_{k}\)), as:  
\[\hat{p}\left(\mathbf{s}_{k},\mathbf{v}_{k}\right) =p\left(\mathbf{s}_{k},\mathbf{v}_{k}|\mathbf{o}_{1:k}\right)\] \[=\sum_{\sim\mathbf{s}_{k}\sim\mathbf{v}_{k}}p\left(\mathbf{s}_{1: k},\mathbf{v}_{1:k}|\mathbf{o}_{1:k}\right), \tag{53}\]  
where \(\sim\) is the operator for exclusion. Factorization of the conditional PDF function of (53) can be given by:  
\[p\left(\mathbf{s}_{1:k},\mathbf{v}_{1:k}|\mathbf{o}_{1:k}\right)\] \[= \frac{p\left(\mathbf{o}_{k}|\mathbf{s}_{1:k},\mathbf{v}_{1:k}, \mathbf{o}_{1:k-1}\right)p\left(\mathbf{s}_{1:k},\mathbf{v}_{1:k},\mathbf{o}_ {1:k-1}\right)}{p\left(\mathbf{o}_{1:k}\right)} \tag{54}\] \[= \frac{p\left(\mathbf{o}_{k}|\mathbf{s}_{k}\right)p\left(\mathbf{ s}_{1:k},\mathbf{v}_{1:k},\mathbf{o}_{1:k-1}\right)}{p\left(\mathbf{o}_{1:k} \right)}, \tag{55}\]  
where (54) is calculated by following Bayes' rule, and (55) is computed since \(\mathbf{o}_{k}\) only depends on \(\mathbf{s}_{k}\). Moreover,  
\[\frac{p\left(\mathbf{s}_{1:k},\mathbf{v}_{1:k},\mathbf{o}_{1:k-1} \right)}{p\left(\mathbf{o}_{1:k}\right)} \tag{56}\] \[= \frac{p\left(\mathbf{s}_{k}|\mathbf{s}_{k-1},\mathbf{v}_{k-1} \right)p\left(\mathbf{v}_{k}|\mathbf{v}_{k-1}\right)p\left(\mathbf{s}_{1:k-1},\mathbf{v}_{1:k-1}|\mathbf{o}_{1:k-1}\right)}{p\left(\mathbf{o}_{k}|\mathbf{ o}_{k-1}\right)},\]  
assuming that \(\mathbf{s}_{k}\) only depends on \(\mathbf{s}_{k-1}\) and \(\mathbf{v}_{k-1}\), while \(\mathbf{v}_{k}\) only depends on \(\mathbf{v}_{k-1}\). Then, by substituting (56) into (55), \(p\left(\mathbf{s}_{1:k},\mathbf{v}_{1:k}|\mathbf{o}_{1:k}\right)\) can be expressed by:  
\[p\left(\mathbf{s}_{1:k},\mathbf{v}_{1:k}|\mathbf{o}_{1:k}\right) \tag{57}\] \[\propto \prod_{1:k}p\left(\mathbf{s}_{k}|\mathbf{s}_{k-1},\mathbf{v}_{k-1 }\right)p\left(\mathbf{v}_{k}|\mathbf{v}_{k-1}\right)p\left(\mathbf{o}_{k}| \mathbf{s}_{k}\right),\]  
where the denominator of (56) is omitted, i.e., \(p\left(\mathbf{o}_{k}|\mathbf{o}_{k-1}\right)\). It should be noted that \(p\left(\mathbf{s}_{1:k-1},\mathbf{v}_{1:k-1}|\mathbf{o}_{1:k-1}\right)\) in (56) denotes the output of FG-EKF from the previous time index. Hence, \(\prod\) is used in (57) considering the case with the time index from one to \(k\). Given (57), the FG-EKF algorithm used in this paper can be detailed in two steps as follows.
# A Narrow-Down Approach Based on Machine Learning for Indoor Localization
## 4 Experiment Evaluations
### Evaluation Metrics  
Our proposed scheme follows the narrow-down approach (NDA) that consists of two stages. To measure the performance of the classification model, we use \"Accuracy\" as a metric:  
\[AccuracyScore=\frac{tp+tn}{tp+fn+tn+fp}, \tag{5}\]  
where \(tp\) is true positive, \(tn\) is true negative, \(fp\) is false positive, and \(fn\) is false negative. In the second stage, DNLoc finds the final location of the targeted object in the already-predicted area. To measure the performance of the model, we use the average distance error:  
\[AED=\frac{1}{N_{T}}\sum_{p=1}^{N_{T}}\|(y^{\prime}_{test}-y_{test})\|_{2} \tag{6}\]  
where \(N_{T}\) represents the total number of online testing samples. \(y^{\prime}_{test}\) and \(y_{test}\) are predicted and actual labels of the test data, respectively, and both comprise 2D positioning components.
# V2X Communications in Highway Environments: Scheduling Challenges and Solutions for 6G Networks
###### Abstract  
As the automotive industry moves toward fully autonomous driving, the goal is to enable vehicles to operate safely without human control in all environments. Implementing Vehicle-to-Everything (V2X) communications in highway environments poses considerable challenges. Several critical services have strict network performance requirements as they deal with safety features. Existing fifth-generation (5G) base station schedulers do not discriminate among critical and non-critical automated driving functions. Therefore, in cases of increased traffic load, there is a significant drop in their performance, and, consequently, increased risk for accidents. Our paper discusses these issues and provides an adaptive scheduler called SOVANET+. The new scheduler acknowledges the Radio Access Network (RAN) load, and the requirements of critical, automated driving applications, together with channel quality, and optimizes the allocation of resources to critical services. The performance of SOVANET+ is evaluated through extensive simulations in the highway environment, an area less examined than urban scenarios. Results indicate that the adoption of SOVANET+ presents clear advantages to critical services compared to existing solutions.  
6G; scheduling; critical V2N services; highway V2X +
Footnote †: journal:  
0000-0001-5000-0000-0000][PERSON]  
0000-0001-5000-0000][PERSON]
## 3 Case Studies
### Regional Transportation  
The airport network reflects the economic and social characteristics of the region and is an integral part of the transport system for the carriage of passengers and cargo by air.  
Figure 3: Methodology of the time series system represented by the transfer entropy graph model.  
Airports provide reliable logistics links within a region or country, and with other regions of the world. Airport congestion, and thus passenger traffic, reflects the geographical, social, and economic characteristics of the region, directly affecting the characteristics of the communication space. Airport capacity statistics reflect the overall traffic of aircraft, the directions of air transport movements, and the intensity of the flow of passengers and cargo.  
Let us further consider an analysis of the activities of the airport network of one region over a certain period of time using the example of Great Britain. Statistical data were collected by the Civil Aviation Authority [33]. The statistics reflect the operating characteristics of more than 60 airports in Great Britain from 1981 to 2015. Figure 4 shows curves reflecting the main characteristics of the intensity of traffic flows: total traffic density, air transport traffic density, and cargo traffic density.  
Figure 5 shows the corresponding change curve of the flow for registered passengers over the same period of time.  
Using the previously presented method for analyzing the mutual influence of the processes \(S_{n}\) (\(n=1\ldots 4\)), we determined the values of the set of paired estimates of transfer entropy \(p\) and formed the matrix \(M_{TE}\). The results of calculation are presented in Table 1.  
Figure 4: Air transport statistics on cargo [33].  
Figure 5: Air transport statistics on passengers [33].  
Next, we present the data from Table 1 as a directed graph \(G_{TE}\) = (\(N\), \(V\)), where \(N\) is the set of vertices describing processes in time, and \(V\) is the set of edges whose weights reflect the information measure of entropy transfer between processes.  
Figure 5(a) shows the graph \(G_{TE}\), where Node 1 is total movements, Node 2 is air transportation movements, Node 3 is terminal passengers, and Node 4 is freight tonnes.  
Figure 5(b) shows the approximation of the graph \(G_{TE}\) in the form of the graph \(G_{TES}\), in which edges with small weights (c < 0.095) are removed. The graph \(G_{TES}\) reflects the cause-and-effect relationship between the processes of organizing transportation.  
This model in the form of the graph \(G_{TES}\) can be used as a knowledge representation model for forming a data structure in a graph database, which can be used to solve various problems of analyzing the activities of the country's airport network and assessing the performance indicators of the passenger and cargo transportation system, which in turn will allow assessing the properties of the communication space.
## 4 Discussion
### Qualitative Analysis  
This section interprets the implications and significance of the findings. The high accuracy and F1-score achieved by the model suggest that the fine-tuning of hyperparameters was instrumental in optimizing performance. IndoBERT, a pre-trained model tailored for the Indonesian language, allowed the system to handle better linguistic complexities, such as informal expressions and cultural nuances commonly found in student reviews.  
The strong performance in aspect extraction highlights the model's ability to identify and categorize relevant aspects, such as \"lecturers\", \"curriculum\", and \"infrastructure\", with high precision. This capability is critical for providing actionable insights to educational institutions, enabling them to pinpoint improvement areas. For instance, the results indicate high satisfaction with infrastructure but reveal mixed sentiments regarding curriculum, suggesting the need for curriculum reform to better align with student expectations.  
However, some limitations persist. Despite the high accuracy, the model occasionally struggled with ambiguous sentiments, particularly in reviews expressing mixed opinions about multiple aspects. This highlights the importance of integrating additional contexts, such as teaching methods or course difficulty, to refine sentiment classification further. Future work could address this by incorporating external knowledge graphs to enhance semantic understanding and provide deeper insights into inter-aspect relationships.
# Design and Evaluation of Steganographic Channels in Fifth-Generation New Radio +
## 4 Protocol Analysis and Covert Channel Design
### Analysis of Exploitable Protocols  
#### 4.1.1 Service Data Adaption Protocol  
The header of the SDAP Data PDU [11] offers only a few bits per packet to hide information. This is mainly due to the fact that the SDAP is only responsible for identifying the QoS flow of the packet, and the header therefore consists mostly of the QoS flow identifier, which is less suitable for hiding information. Overall, the SDAP only offers one reserved bit in the header of the uplink PDU, which is possibly suitable for embedding covert data. Therefore, the SDAP was not considered further.
## 3 Results and Discussion
### Air Purification Performance
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Medium** & \multicolumn{2}{c}{**Day 1**} & \multicolumn{2}{c}{**Day 2**} & \multicolumn{2}{c}{**Day 3**} & \multicolumn{2}{c}{**Day 5**} & \multicolumn{2}{c}{**Day 7**} \\ \hline SOT excl. NaHCO\({}_{3}\) & \(\mu\) & 817.9 & 885.5 & 709.6 & 497.6 & 521.0 \\  & \(\sigma\) & 6.1 & 23.1 & 17.9 & 5.8 & 16.6 \\  & \(p\)-Value & *** & *** & *** & *** & *** \\  & _Max._ & 828 & 928 & 743 & 510 & 547 \\  & _Min._ & 807 & 842 & 671 & 479 & 483 \\ \hline Standard SOT & \(\mu\) & 533.0 & 525.6 & 413.9 & 442.6 & 532.2 \\  & \(\sigma\) & 10.2 & 23.6 & 12.8 & 16.9 & 17.1 \\  & _p_-Value & *** & *** & *** & *** & *** \\  & _Max._ & 542 & 580 & 476 & 465 & 588 \\  & _Min._ & 486 & 452 & 365 & 373 & 489 \\ \hline Mann–Whitney U & \(p\)-Value & *** & *** & *** & *** & *** \\ Brown–Forsythe & \(p\)-Value & 0.767 & *** & *** & *** & 0.805 \\ \hline \hline \end{tabular} \({}^{***}p<\alpha\), \(\alpha=0.001\), \({}^{**}p<\beta\), \(\beta=0.01\).  
\end{table} 
Table 4: Statistical analysis of the carbon dioxide density in the chamber.
## 4 Security Analysis and Discussion
### External Attacks
#### 4.1.2 The Quantum Entangled-Probe Attack  
A quantum entanglement-based attack refers to a strategy in which an adversary [PERSON] introduces quantum states that are pre-entangled with those of legitimate communication parties (e.g., PD and HP). The adversary aims to indirectly eavesdrop on or manipulate the communication process by exploiting these entangled states, all while preserving the overall entanglement structure of the system to avoid detection. Consider a scenario where [PERSON] attempts to perform an entanglement-based attack by introducing an auxiliary qubit \(|E\rangle\) and applying a unitary transformation \(U\) to entangle it with the transmitted quantum states. The action of \(U\) on the standard basis states can be described as:  
\[U(|0\rangle|E\rangle)=a|0\rangle|e_{00}\rangle+b|1\rangle|e_{01}\rangle,\ U(|1 \rangle|E\rangle)=c|0\rangle|e_{10}\rangle+d|1\rangle|e_{11}\rangle\]  
where \(|e_{ij}\rangle\) are four normalized pure states in the adversary's ancilla system, and the coefficients satisfy \(|a|^{2}+|b|^{2}=|c|^{2}+|d|^{2}=1\). When applied to superposition states \(|+\rangle\), and \(|-\rangle\), the unitary yields coherent mixtures of \(|+\rangle\), and \(|-\rangle\) tensor product with corresponding ancilla states, revealing the entanglement structure. To avoid introducing detectable errors during this interaction, the adversary must satisfy the stringent conditions \(a=d=1\), \(b=c=0\) and  
\[a|e_{00}\rangle-b|e_{01}\rangle+c|e_{10}\rangle-d|e_{11}\rangle=\overset{ \rightarrow}{0},\ a|e_{00}\rangle+b|e_{01}\rangle-c|e_{10}\rangle-d|e_{11}\rangle= \overset{\rightarrow}{0}.\]  
Solving these constraints implies that \(|e_{00}\rangle=|e_{11}\rangle\), which leads to the conclusion that the adversary cannot extract any distinguishable information from the ancillary system. Therefore, any entanglement-based attack of this form is mathematically proven to beineffective in gaining useful information without being detected, reinforcing the intrinsic security of the protocol against such quantum threats.
# Resource Indexing and Querying in Large Connected Environments
## 4 Preliminaries & Assumptions
### Spatio-Temporal Dimension  
We detail here the spatio-temporal dimension concepts of a connected environment: (i) zones (cf. Definition 1); (ii) location stamps (cf. Definition 2); and temporal stamps (cf. Definition 3).  
**Definition 1** (Zone).: _A zone \(z\) is defined as a 3-tuple:_  
\[z:\langle label,shape,L\rangle\quad\text{where}: \tag{1}\]  
* _label: is the zone label_
* _shape: is a geometric form describing the zone._
* _L: is the set of location stamps_ \(\bigcup_{t=0}^{n}l_{i}\forall i\in\mathbb{N}\) _that constitute the area of the shape (cf. Definition_ 2_)._  
In this study, we simplify the _shape_ parameter and consider it a minimal bounding rectangle or _mbr_ that bounds the actual real shape. Thus, L only needs two location stamps, L = \(\{l_{min},l_{max}\}\). Furthermore, zones having no devices are named uncovered zones.  
**Example 1**.: _The zone \(Z_{1}\) in our motivating scenario (Figure 1) can be assigned to the following representation: \(z_{1}:(plain\ field,mbr_{1},\{l_{1},l_{2}\})\) where \(l_{1},l_{2}\) are defined in Example 2._  
**Definition 2** (Location Stamp).: _A location stamp \(l\) is an atomic location coordinate defined as a 2-tuple:_  
\[l:\langle[format],value\rangle\quad\text{where}: \tag{2}\]  
* _format is the coordinate referential system that specifies the format of the location stamp value (e.g., default GPS, Cartesian, spherical, cylindrical)_
* _value is the point coordinate value (e.g.,_ \(\langle x,y,z\rangle\) _or_ \(\langle lat,lon,alt\rangle\) _depending on the chosen format)._  
**Example 2**.: _The location stamps \(l_{1}\) and \(l_{2}\) (previously used in Example 1) can be defined as follows: \(l_{1}:(Cartesian,(2,8,6))\)_  
\[l_{2}:\langle GPS,(47.6193757,6.1529374)\rangle.\]  
**Definition 3** (Temporal Stamp).: _A temporal stamp \(t\) represents a single punctual temporal value defined as a 2-tuple:_  
\[t:\langle[format],value\rangle\quad\text{where:} \tag{3}\]  
* \(format\) _is a string indicating the format of the date-time value of_ \(t\)__
* \(value\) _is the timestamp value._  
**Example 3**.: _Temporal stamps can be defined as follows:_  
\(t_{1}:\langle dd-MM-yyyy\ hh:mn:ss,10-12-2021\ 15:34:23\rangle\)_._  
**Note:** When devices/sensors from different manufacturers use different \(format\)(s) for location or temporal stamps, conversion functions \(\mathit{convert}_{tf}(t_{i}.v_{j})\longrightarrow t_{i}.v_{k}\) and \(\mathit{convert}_{lf}(t_{i}.v_{j})\longrightarrow l_{i}.v_{k}\) can convert the value from one format to another.
## 2 Study Area and Data
### Data and Data Processing  
#### 2.2.1 Satellite Data  
The HaiYang-1D satellite (HY-1D), launched on 11 June 2020, operates as a dual-satellite network with HaiYang-1C (HY-1C). Together, they can capture a full view of the green tide in the Yellow Sea twice every three days with a spatial resolution of 50 m and a scanning range of 950 km. The Coastal Zone Imager (CZI) on satellite HY-1C/D contains four wavebands for effective green tide monitoring: blue (0.42-0.50 \(\upmu\)m), green (0.52-0.60 \(\upmu\)m), red (0.61-0.69 \(\upmu\)m), and near-infrared (0.76-0.89 \(\upmu\)m) [40]. Since 2019, this imagery has been one of the primary satellite resources for green tide monitoring in the Yellow Sea. Eight HY-1C/D satellite images from 2021 were used in this study (see Table 1).  
The Huanjing-2 A/B Satellites (HJ-2A/B) were successfully launched on 27 September 2020. Each satellite has four types of optical payloads: a 16 m camera, a hyperspectral imager, an infrared camera, and an atmospheric corrector. The 16 m camera payload consists of four visible CCD cameras that cover the blue, green, red, red edge, and near-infrared bands, each with a single width exceeding 200 km. By mosaicing the fields of view, it can provide multispectral images with a width of 800 km [41]. The two satellites, networked in the same orbit, can quickly acquire imagery and can cover the green tide area with visible and infrared multispectral data once every two days, serving as a valuable supplementary data source for HY-1C/D satellites. Two HJ-2A/B satellite images from 2021 were used in this study (see Table 1).
###### Abstract
The primary objective is to achieve distributed frequency conversion and phase shift control through unified hardware architecture, thereby realizing high synchronization and phase coherence across multiple frequency bands. Specifically, the method achieves simultaneous up/down frequency conversions for multiple bands and acquires distinct phase compensation values for each band. The core innovation lies in the tight coupling of frequency, phase, and time through optical pulses. By modulating signals onto optical pulses and introducing precise delays, the system establishes a direct relationship between the time delay of the optical pulses and the phase adjustments for each frequency band. This coherent interaction ensures that frequency conversion and phase shifting are inherently synchronized. Electronic phase shifters are further integrated to fine-tune the phase control, meeting the stringent requirements of phased array radars. Based on this method, we propose a multi-band phased array radar architecture capable of efficient multi-band signal generation, transmission, and reception. To validate the approach, we experimentally construct a four-element phased array radar system that generates pure four-band signals (S, X, Ku, and K) using differential detection. The instantaneous bandwidth is expanded through two-channel interleaving, enabling the reception of a four-band signal with a total bandwidth of 4 GHz. Both the transmission and reception phased array antenna patterns are measured. Leveraging its inherent synchronization and coherence, the method achieves nearly four times improve ment in range resolution through multi-band data fusion, highlighting its potential for advanced radar applications.