esa-sceva/satcom-chunk-collection huggingface dataset:
# Unmanned Vehicles' Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles
## 5 Towards Algorithm Development: Propagation Concepts, Path Loss and Communication Graph
### Air-to-Ground Channel (A2G)  
The air-to-ground channel oversees the ability of an unmanned aerial vehicle to provide a communication link for the ground nodes. Due to the high altitude they can reach, the channel conditions for UAV will be improved, as compared to standard terrestrial base stations [22]. Nevertheless, when the communication between a UAV and a mobile base station is desired, one must consider the fact that the base station antenna is down tilted, such that the ground coverage is optimised, to the detriment of UAV coverage [65]. According to International Telecommunication Union (ITU) studies [66], for urban/suburban regions, buildings and vegetation impact in a more significant measure the quality of communication than multipath reflections. Consequently, for our purpose, multipath reflections are not taken into consideration, supposing the deployment of the proposed solution in a high vegetation index area.  
Furthermore, for the simplicity of the model, given the low node density and the small probability that other similar networks are deployed in the area, we do not take into account the interfering nodes.
# Unlicensed Spectrum Allocation for LTE and Wi-Fi Coexistence with HAP
## 3 The Time-Division Multiplexing Problem
### Problem Formulation  
The objective of this study is to achieve good throughput performance without compromising the fairness between LTE users and Wi-Fi stations. Therefore, the utility function is defined as the sum of the weighted throughputs of Wi-Fi and LTE subject to constraints that both Wi-Fi and LTE can meet their QoS requirements. The optimization problem is formulated as follows:  
\[\underset{\delta}{\text{max}}U(\delta)=\mathbb{E}\{\alpha\cdot Throughput_{ WiFi}+(1-\alpha)\cdot Throughput_{LTE}\}\] (1a) Subject to the following constraints: \[Throughput_{i}^{WiFi}\geq Throughput_{th}^{WiFi},\;\forall\;\text{Wi-Fi station}\;i \tag{1b}\] \[T_{j}^{LTE}\cdot\mathbb{E}\Big{(}s_{j}^{LTE}\Big{)}\geq s_{th}^{LTE},\; \forall\;\text{LTE user}\;j\] (1c) \[P(d<D|N_{STA})\geq P_{th}\] (1d) \[0\leq\alpha,\delta\leq 1 \tag{1e}\]  
where \(Throughput_{WiFi}\) is the throughput of all Wi-Fi stations; \(Throughput_{LTE}\) is the throughput of all LTE users; \(Throughput_{i}^{WiFi}\) denotes the throughput of Wi-Fi station \(i\); \(Throughput_{th}^{WiFi}\) refers to the throughput threshold of Wi-Fi station; \(T_{j}^{LTE}\) means the duration that LTE user \(j\) can access the unlicensed band; \(\mathbb{E}\Big{(}s_{j}^{LTE}\Big{)}\) represents the expected throughput of LTE user \(j\); \(s_{th}^{LTE}\) symbolizes the throughput threshold of LTE users; \(P(d<D|\;N_{STA})\) is the probability that a Wi-Fi station transmits successfully with a transmission delay \(d\) less than a maximum saturation delay \(D\) when the number of stations is \(N_{STA}\); \(P_{th}\) represents the threshold of successful transmission probability of Wi-Fi stations; \(\alpha\) is the weight assigned to Wi-Fi throughput.  
The constraint (1b) assures that the throughput of Wi-Fi station \(i\) must be above a certain threshold \(Throughput_{th}^{WiFi}\). The throughput of Wi-Fi station \(i\) is defined as (2).  
\[Throughput_{i}^{WiFi}=\delta\cdot L\cdot P\Big{(}d<D|\;N_{STA})\cdot\mathbb{E} \Big{(}s_{i}^{WiFi}\Big{)} \tag{2}\]  
where \(\mathbb{E}\big{(}s_{i}^{WiFi}\big{)}\) is the expected throughput of Wi-Fi station \(i\). Constraint (1c) assures that the throughput of LTE user \(j\) must be larger than a certain threshold \(s_{th}^{LTE}\). Constraint (1d)  
Figure 3: Repetition interval.  
guarantees that the successful transmission probability of Wi-Fi stations must be above a certain threshold. Constraint (1e) represents that the value of \(\alpha\) and \(\delta\) is between 0 and 1.
# Learning Word Embeddings with Chi-Square Weights for Healthcare Tweet Classification
## 3 Algorithms
### Continuous Bag-of-Words Model (CBOW)  
The CBOW model is a neural language model which consists of three layers: an input layer, a projection layer (also known as a hidden layer) and an output layer. In the input layer, the CBOW model uses context words both \(b\) before and after the target word to predict it; the vocabulary is represented as an input vocabulary matrix \(\mathcal{V}\in\mathbb{R}^{n\times|\mathcal{V}|}\); each column in \(\mathcal{V}\) is represented as the vector representation of the words in the vocabulary; \(\mathcal{V}\) is randomly initialized from the uniform distribution in the range \([-1,1]\). The matrix \(\mathcal{U}\in\mathbb{R}^{|\mathcal{V}|\times n}\) is also initialized, which contains parameters learned by the neural language model during training. In the projection layer, the vector representation of the context, \(\mathcal{C}\), is calculated as the arithmetic mean of the vector representation of all words \(w_{i}\) in the context window with \(b\) words before and after the target word, as shown in Formula (2).  
\[\mathcal{C}=\frac{1}{2b}\sum_{i\in[-b,-1]\cup[1,b]}w_{i} \tag{2}\]  
\(\mathcal{C}\) is used to calculate the probability of the target word as shown in Formula (3), which is represented as a softmax function over the dot product of the vector representation of the context \(\mathcal{C}\) and target word \(w_{t}\).  
\[p(w_{t}|\mathcal{C})=\frac{e^{w_{t}\cdot\mathcal{C}}}{\sum\limits_{w_{i}\in \mathcal{V}ocab}e^{w_{i}\cdot\mathcal{C}}} \tag{3}\]  
Finally, we can depict the loss function of the CBOW model in Formula (4).  
\[\mathcal{L}=\sum_{w_{t}\in\mathbb{C}}\log\,p(w_{t}|\mathcal{C}) \tag{4}\]  
where over all training tuples in the corpus \(\mathbb{C}\), we are maximizing the probability of finding the target word \(w_{t}\) given \(\mathcal{C}\), its context. However, to go over all the words in the vocabulary in Formula (3) is expensive. Instead of computing all the words in the vocabulary, distinguishing only the target word from several noise words largely reduces the computation load. This is called negative sampling. In this work, we adopt negative sampling when training the CBOW model. The window size \(2b+1\) and the word embedding dimension \(n\) are all hyperparameters.  
By averaging the context words, the CBOW model overlooks the fact that the contribution of the words for the prediction should not be equal. We develop two algorithms to re-weight the context words.
## 3 Results
### The Liquid Crystal Cells with Integrated Thin-Film Polarizers
To evaluate the results, we utilized the 1951 USAF resolution test target that was imaged with a CCD camera and objective under white light illumination, as presented in Figure 2c. The same scene was photographed under the same conditions with an additional NLC device (6 CHBT NLC + 1% SBII \(w/w\), TN-cell with integrated foil polarizers, thickness equal to 12 \(\upmu\)m) placed between the objective and a photographed resolution test, as can be seen in Figure 2d. The overall image remained clearly visible without any noticeable loss in quality. The blue color results from the absorption spectrum of the SBII dye, in our opinion, does not pose a problem as the color balance can be corrected through software during the image processing stage. The entire field of view appeared uniformly bright, with minor deviations in the center-right (brighter) and upper-left (darker) parts. These slight variations in brightness may be attributed to the inhomogeneity of the thickness of the liquid crystal layer, possibly due to the unevenness of the film polarizers' thickness. To verify the accuracy of the NLC cell with integrated film polarizers, we also performed transmission measurements, shown in Figure 2e, for a typical TN-textured NLC thick cell (50 \(\upmu\)m width and 6 CHBT NLC, represented by red circles) placed between crossed dichroic economy film polarizers (of similar quality to film polarizers integrated into a complex TN cell) and for an analogous cell of the same texture and thickness with internal integrated film polarizers (black circles). Both datasets align closely with theoretical projections (represented by solid lines derived from a Mallus formula), demonstrating complete obscuring of the transmitted linearly polarized beam when the initial polarization is perpendicular to the first polarizer's optical axis. The attenuation observed in the analyzed NLC structures containing pure 6 CHBT NLC primarily arises from the transmission characteristics of the polarizer material, with the transmission level at 532 nm wavelength slightly exceeding 80% for a single polarizer. Consequently, the maximum transmission of a dual polarizer in a parallel arrangement is limited to approximately 65%. One of the last issues requiring attention was the power range of plastic polarizers without incurring damage. To investigate this, we illuminated the single-foil polarizer with a beam of approximately 2 mm width and linear polarization orthogonal to its optical axis and monitored the power of the beam at the output. Under ideal conditions, the transmission should be zero; damage is indicated by any transmission above zero. As can be seen in Figure 2f, the polarizer works properly up to a power of about 240 mW, at which point the transmission rapidly goes above zero. Therefore, a liquid crystal power limiter can handle up to 200 mW at 2 mm diameter, corresponding to a radiation intensity of 13 W/cm\({}^{2}\).
## 4 Experimental Results and Analysis
### Analysis of Algorithm Results
#### 4.3.1 Synthetic Datasets
Comparing the clustering effect of the DP-CDPC algorithm and the CDPC algorithm demonstrates that the clustering effectiveness of the algorithm after adding some noise is not necessarily weaker than the original algorithm due to the random and fluctuating nature of the noise.
## 6 Discussion  
When considering the land cover map scale, the cartographic generalization would be helpful after applying the pixel-based deep learning approach. After applying CNN-based models, there is a salt and pepper effect when considering the scale as middle-scale reference thematic maps. LCCS has some generalization requirements, especially for too small objects. In the experiments in the Sentinel dataset, generally, ships and ports caused errors in the assessment. For instance, ships are accepted as water bodies in ground truth images as they are too small to accept as an object on that scale. In reference data, too small green areas, e.g., ships in the sea, were included in the neighboring wider area class (Figures 12 and 13). This was a reason for the decrease in the class precision values (Figure 16) in the Sentinel dataset. In the Sentinel dataset, different dataset sizes were used in two test areas. The CNN (U-net)-MRS model was found to have a better classification with the five-band dataset, with overall accuracy of 97.31% and F1-score of 96.06% in Istanbul (Table 6). In the Kocaeli test area, the highest scores were found for the CNN (SegNet)-MRS model, with overall accuracy of 98.16% and F1-score of 92.54% for the seven-band dataset (Table 13).  
Moreover, the CNN-MRS model is suitable for both the five- and seven-band datasets. For each experiment, it was revealed that the proposed CNN-MRS model has better results than the CNN-only models. As the noise ratio in the image increased in experiments, the amount of refinement in the classification increased using the CNN-MRS model (Figures 12-15, Figures 18 and 19). In addition, the McNemar test for the Sentinel dataset showed that comparing these different methodological models is significant (Table 14).  
The Zurich Summer dataset used in the second experiment has very different weights of eight classes. Hence, as an imbalanced dataset, it affected the experimental results for LULC mapping. For this reason, swimming pools and bare land--the lowest weight classes in the datasets--could not be segmented in the CNN applications in the test images. This situation especially affected mean accuracy metric values (Tables 15 and 16).  
Previous studies of CNN for land cover mapping have generally focused on small areas. However, using traditional CNN methods in large areas produces coarser map results [21]. This study revealed that the classification results could be improved by combining object-based approaches and CNN models. Our findings are also consistent with similar studies [21; 63].
# Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey
## 7 Challenges and Future Research Directions
### Multitenant and Vertical Oriented Resource Management  
Considering 5G promotes realizing use cases such as uRLLC, mMTC, and eMBB and 6G advocates achieving sEMBB, umMTC, mURLLC, MBRLLC, and ERLLC, it is necessary to introduce new resource management solutions able to meet the requirements of such diversity of use cases. Indeed, Tables 1-5 show network slicing resource management solutions have not addressed 6G use cases to the best of our knowledge, opening the port for research on 6G resource management by using RL and DRL techniques. It is also remarkable that few solutions presented in Sections 3-6 operate in multitenant environments and, worst, some of them do not use 5G or 6G terminology, resulting in a research gap to fulfill. Furthermore, novel resource management solutions will be needed to meet vertical applications' QoE and QoS requirements such as remote surgery, immersive media, industrial IoT, and intelligent microgrids.
# Results and Achievements of the ALLIANCE Project: New Network Solutions for 5G and Beyond
## 2 Network Architecture and Project's Main Achievements  
### Alliannce Network Architecture  
Figure 1 shows an overview of the general ALLIANCE network architecture. It consists of different main blocks. On the top side, there is the **KDN-based orchestration layer** which is empowered with ML techniques (e.g., deep-learning tools) to increase the efficiency of the management of the overall resources. On the right-hand side, the cross-layer manager considered in ALLIANCE consists of two elements: the **monitoring element** in charge of collecting users, networks and Information Technology (IT) resources data and the **trust element** in charge of assuring confidence and reliability to external information. On the bottom side, the network infrastructure can consist of any levels including access, metro, core and DC network segments. We have considered two different network solutions to control this network infrastructure and provide end-to-end services. An **SDN/NFV-** enabled control layer has been developed to deliver network slices customised to the requirements of the vertical services and applications. This solution focuses on an ultra-low-latency, ultra-high-capacity optical infrastructure where both the packet and the optical layers are considered. Studies have been carried out to improve both the physical layer with investigations on novel modulation formats and the control layer to be able to take autonomous and automatic decisions to keep the best possible network performance. As an alternative solution for legacy protocols, we have also investigated the **RINA** network architecture. RINA is a clean-slate recursive multi-layer architecture based on a single type of layer and two programmable protocols. For this solution, we only focused on the multi-layer packet network for access and metro segments.  
We would like to point out that the final intention of this project was not to fully integrate all previous introduced blocks in one single platform. On the contrary, our idea was to investigate several network solutions addressing different problems and converge and combine some of them when practical and convenient. Therefore, the experiment setups and the main results presented throughout this paper are mostly related to a single or a couple of components of the ALLIANCE reference architecture. For example, the monitoring element fed the KDN framework with processed data containing a rich and timely view of the network states. If the data come from external entities, the trust  
Figure 1: The ALLIANCE network architecture.  
element can guarantee integrity and reliability before entering the monitoring element and, in turn, the KDN. The approaches developed in this project to take optimal, automatic and autonomous decisions regarding the network resources, consider the ML algorithms investigated in the KDN framework.
## 2 Materials and Methods
### Impedance Spectroscopy Analysis  
AC electrical response of the composites was investigated through Impedance Spectroscopy Analysis using a two-probe method formed by two aluminum discs as electrodes connected to an AUTOLAB PGSTAT302N (potentiostat/galvanostat) equipped with a FRA32M module (frequency response analysis module). Impedance spectra were recorded in potentiostatic mode with an amplitude of 0.01 V and frequencies ranging from 100 kHz to 0.01 Hz between the electrodes. For each measurement, three readings and 50 data points (logarithmic frequency step) per reading were collected.
## 4 Results and Discussions  
### Chemical Compositions of Coalbed Gas  
The coalbed gas composition (Mol%, air free basis) of Longtan Formation in the Dacun Block consists of CH\({}_{4}\), N\({}_{2}\), CO\({}_{2}\), H\({}_{2}\), and heavy hydrocarbon gas (C\({}_{2+}\)). Among them, CH\({}_{4}\) is the dominated component, with a concentration ranging from 33.8% to 100% (avg. 85.21%). N\({}_{2}\), CO\({}_{2}\), H\({}_{2}\), and C\({}_{2+}\) account for 0-62.05% (avg. 8.90%), 0-10.92% (avg. 4.97%), 0-1.55% (avg. 0.55%), and 0-8.84% (avg. 0.35%), respectively.  
In general, the depth zone where the CH\({}_{4}\) concentration in the coal seam is less than 80% is defined as the CH\({}_{4}\) weathering zone. Figure 3a shows that the CH\({}_{4}\) concentration in the study area, overall, takes on an increasing trend as the depth of coal seam increases. When the depth is more than 310m, the CH\({}_{4}\) concentration of the coal seam is basically more than 80%. Therefore, the CH\({}_{4}\) weathering zone depth of the coal seam in the Dacun Block is 310 m. The N\({}_{2}\) concentration shows a decreasing trend as the depth of the coal seam increases (Figure 3b). Both CO\({}_{2}\) and H\({}_{2}\) concentrations have a poor relationship with the depth of coal seam (Figure 3c,d). With the increase in depth, the C\({}_{2+}\) concentration in the coal seam presents a parabolic variation of first increasing and then decreasing, and the turning depth is around 550 m (Figure 3e).
## 1 Introduction
Recent research by [PERSON] [20] shows that the discrepancy between Equation (1) and experimental data are amplified when a wide-bore glass liner is used for passive sampling with SPME fiber retracted inside it. Work by [PERSON] [20] suggests that not only the tip of the fiber coating (at the physical retraction depth \(Z\)) is involved in extraction, but the whole fiber coating surface with an \"apparent\" \(Z\) that is ~55% longer. Apparent saturation sorption kinetics might also be involved as predicted by [PERSON] et al. (2000) [21]. Thus, research is warranted to address apparent problems with the use of Equation (1). Despite the simplicity, quantification of TWA concentrations of VOCs in ambient air using SPME can be associated with poor accuracy and precision [19]. Possible problems are variability of extraction efficiencies associated with inherent and acquired variability between individual SPME fibers, adsorption of analytes by metallic surfaces [16; 19], effects of sampled air temperature and humidity. Experimental optimization of the gas sampling process is very time-consuming, particularly at longer extraction times (>24 h). Such experimental setups are quite complex, and difficult to build and properly maintain in steady-state conditions (e.g., without leaks and with minimal impact of sorption onto the system itself). During experiments, the sensitivity of the analytical instrument can change leading to additional uncertainties. Uncertainties during experimental method optimization do not allow studying effects of parameters having potentially minor impacts on accuracy and precision. Numerical simulation could provide useful data at various sampling parameters in a much faster and more accurate way. It could also allow modeling of the sensitivity of Equation (1) to ranges of practical (user controlled) parameters for air sampling with retracted SPME. COMSOL Multiphysics allowed efficient numerical modeling of the SPME process using a finite element analysis-based model [22; 23; 24; 25; 26; 27] for liquid-phase extraction and absorption by SPME coating. Using this approach, it was possible to predict sampling profiles of analytes, which were consistent with experimental data. The goal of this research was to develop a model for SPME with both absorptive and adsorptive fibers located (retracted) inside a protecting needle using a finite element analysis-based model (COMSOL Multiphysics) and use it to disclose potential sources of inaccuracies in the quantification of time-weighted average concentrations of VOCs in ambient air. Specifically, the effects of SPME sampling time, coating type, diffusion coefficient, fiber coating-gas distribution constant, the internal diameter of protecting needle, and SPME retraction depth on extraction were modeled for several common VOCs. Based on the results of the modeling, alternative sampling geometries were proposed.
## 2 Related Works
### D2D Communication Underlaying Uplink Based on the Fractional Frequency Reuse  
There are many studies of D2D communication underlaying uplink systems based on cellular or WSNs [36]. In particular, we introduce studies based on FFR as related works. There is D2D communication with FFR based on ICIC introduced in [37; 38]. In [39], the coverage performances of both links are mainly analyzed with a Poisson point process (PPP) model with a similar system. In [40], an additional fractional power control scheme to the D2D communication on the FFR-based system is provided. The authors in [41] also propose FFR for D2D communication underlaying cellular systems. It separates a cell into three inner regions and three outer regions to achieve higher throughput. In [42], LTE-A is considered with a D2D link on the FFR system. In [43], a dynamic power control scheme is proposed for the same system. These studies describe how it is convincing to apply D2D communication on the FFR-based systems.