esa-sceva/satcom-chunk-collection huggingface dataset:
# Secrecy Control of Wireless Networks with Finite Encoding Blocklength
## 3 Control with Finite Secrecy Codewords
### Control Algorithm  
For the secrecy transmission strategy with finite secrecy codewords, the control algorithm is:  
**Multi-hop Secrecy Control Algorithm with Finite Block (MSCAFB):**  
* **End-to-End Encoding**: At each new secrecy codeword generation, i.e., \(P_{n}^{c}(t)=0\), let \(k_{n}(t+1)=k_{n}(t)+1\), the confidential message \(R_{n}^{k_{n}(t+1),cs}\) allowed to encode into a new codeword satisfies: \[R_{n}^{k_{n}(t+1),cs}=\arg\max_{r}\left\{r\left[Q_{n}^{p,c}(t)-Y_{n}^{k_{n}(t ),c}(p_{n}^{out}(r)-\gamma_{n})\right]\right\}.\] (13)
* **Flow Control**: In each slot \(t\), for a given parameter \(V\), the admitted confidential message at each source node \(n\) is: \[R_{n}^{p,cs}=\arg\max_{x}\left\{VU_{n}^{c}(x)-xQ_{n}^{p,c}(t)\right\},\] (14)* **Scheduling**: In each slot \(t\), if \(I_{g}(t)=1\) and \(I_{ij}^{n,c}(t)=1\), then the flow of node \(n\) is on the link \((i,j)\in g\) and the scheduler selects the set of \(l^{*}\), where \[\begin{split}&(n,l^{*})=\arg\max_{n,g}\bigg{\{}\sum_{(n,i)\in g }u_{ni}^{n}(t)\bigg{(}\frac{R_{n}^{c}}{R_{n}^{k_{n}(t),cs}}Q_{n}^{p,c}(t)+P_{n} ^{c}(t)\\ &-Q_{i}^{n,c}(t)\bigg{)}+\sum_{(i,j)\in g}u_{ij}^{n}(t)\left(Q_{i }^{n,c}(t)-Q_{j}^{n,c}(t)\right)\\ &-\sum_{e\in E}\sum_{e\
eq i}Z_{e}^{n,c}(t)\bar{R}^{e}(t)\bigg{\}}.\end{split}\] (15) The term \(\frac{R_{n}^{c}}{R_{n}^{k_{n}(t),cs}}\) of \(Q_{n}^{p,c}(t)\) is used to normalize it to the value of other queues.  
Note that, the long-term average secrecy outage \(p_{n}^{out}(r)\) is increasing with variable \(r\). Once \(r\) increases, secrecy codeword is encoded with less randomization bits, such that eavesdropper can intercept the confidential message with a higher probability. Hence, as the queue length \(Q_{n}^{p,c}(t)\) increases, the confidential message \(R_{n}^{k_{n}(t),cs}\) is increased. Moreover, it decreases with the increasing of the virtual queue length \(\gamma_{n}^{k_{n}(t),c}\), such that the constraints of problem (3) are satisfied.
# Study on the Macro-Fine Mechanical Behavior of Ore Flow Based on the Discrete Element Method
## 3 Construction of the Numerical Model
### Reliability Analysis of the Numerical Models  
Figure 5 shows the comparison of the static lateral pressures obtained, respectively, by numerical simulation, the physical similarity experiment, and the theoretical calculation. The theoretical lateral pressure value of the shaft wall is calculated according to [PERSON]'s theory [33].  
Although this study did not carry out more relevant research based on [PERSON] theory, and only compared the lateral pressure calculated results of the Janssen equation with those of physical experiments and numerical simulations, it can be seen from Figure 5 that the static lateral pressure values obtained by the three methods have a good consistency.  
Figure 4: Ore-drawing model of ore pass and measurement circle layout.  
Figure 5: Comparison of lateral pressures on ore-pass wall obtained by numerical simulation, physical test and [PERSON] theoretical calculation. The green dashed line shows the maximum height of the ore-rock stored in the ore pass and that in 2D and 3D experiments.  
Based on the physical similarity experiment, it is found that the maximum errors between the numerical simulation and the theoretical calculation via the Janssen formula are, respectively, 11.8% and 18.1%; this indicates that the numerical model has a certain reliability in the process of analyzing the characteristics of the pressure distribution on the side wall of the ore pass.  
Unfortunately, in the physical experiment and numerical simulation, the ore-rock storage height of the studied ore storage section in the ore pass was limited to 32 m, i.e., the maximum ore storage height of the ore pass in the case mine site, and the pressure curve obtained by these two methods did not show the pressure saturation phenomenon.  
The pressure fluctuation phenomenon in the experimental curve may be related to the rotation and mobility of the ore-rock blocks in bulk systems caused by the gravitational compaction of the stored materials [10; 34]. The rotation and mobility of the large and irregular blocks in the bulk system will change their contact mode (such as point, line, and surface contact [24]), which results in a decrease in the voidage of the bulk and the changing of the force transfer effect of granular particles.
## 3 Methodology
### GWR Modeling  
GWR model extends the ordinary least squares regression (OLS) [40] by weighting the spatial dependence [41]. Based on the established spatial proximity, the coefficients of the model are estimated for each unit, and the value and symbol of the coefficients vary at different units [16]. This model is in the form of Equation (1)  
\[y_{i}\ =\ \beta_{0}(\mu_{i},v_{i})+\sum_{j\ =\ 1}^{P}\beta_{ij}(\mu_{i},v_{i})x_{ ij}+\varepsilon_{i} \tag{1}\]  
where \((\mu_{i},v_{i})\) represents the coordinates of an \(i\)th unit in space and \(p\) is the number of independent factors. \(x_{ij}\) is the \(j\)th independent variable of the \(i\)th unit. \(\beta_{0}\ (\mu_{i},v_{i})\) is the intercept parameter in position \(i\) and \(\varepsilon_{i}\) is the random error. \(\beta_{ij}\ (\mu_{i},v_{i})\) is the local regression coefficient for the \(j\)th explanatory variable in position \(i\), which varies with the change of spatial position and is a very important parameter for the embodiment of spatial non-stationary.  
The establishment of the weight kernel function is an important step of GWR, which is used to determine the scope and degree of spatial dependence. The establishment process includes the selection of the type of kernel function and the determination of its bandwidth, and previous studies have found that the latter has a greater impact on the result of GWR than the former [42]. This paper used a common kernel function, Gauss kernel function [43], as the type of kernel function. Its function form is  
\[w_{ij}\ =\ exp\left(-\left(d_{ij}/b\right)^{2}\right) \tag{2}\]  
where \(w_{ij}\) is the weight for unit \(j\) in the neighborhood of unit \(i\), and \(d_{ij}\) is the distance between the center point of the unit \(i\) and \(j\) as the measurement of spatial proximity degree. \(b\) is the bandwidth of the Guass kernel function. Many approaches are available for determining the bandwidth, including cross-validation (CV) method [42], Akaike information criterion (AIC) method [44]. Compared with CV method, AIC method is easier to avoid over-fitting problems, and the selected optimal model is often more effective. Therefore, the AIC method was adopted, and the bandwidth corresponding tothe weight function with the minimum AIC value is the optimal bandwidth. Equation (3) is used for the calculation of AIC,  
\[AIC=2n\ln(\hat{\sigma})+n\ln(2\pi)+n\left[\frac{n+tr(S)}{n-2-tr(S)}\right] \tag{3}\]  
where \(tr(S)\) is the function of \(b\), and \(\hat{\sigma}\) is the maximum likelihood estimation for GWR model.
# Virtual Flux Control Methods for Grid-Forming Converters: A Four-Method Comparison
## 4 HIL Results and Comparison  
This section presents a comparative analysis of the four control schemes based on the requirements established for GFM by NESO, which outlines the essential functionalities and performance criteria expected from GFM converters operating in Great Britain. A concise summary of the core requirements is presented below:  
* Reactive Power Capability: GFM units must be capable of both generating and absorbing reactive power.  
Figure 6: Model predictive control scheme.  
Figure 7: MPC operation with a two-step prediction horizon [41].  
* Voltage Regulation: GFM converters are expected to maintain control over their internal voltage vector, both in magnitude and angle, regardless of grid or load conditions.
* Active Power Control: These converters must emulate the behavior of synchronous machines, supplying active power in response to control setpoints.
* Frequency Support: GFM units should respond to frequency deviations by adjusting their active power output, thereby contributing to system frequency stability.
* Damping of Oscillations: PSS or equivalent mechanisms are encouraged to mitigate low-frequency oscillations. In this case, prior validation has been conducted by the authors using Hardware-in-the-Loop simulations [62].
* Fault Ride Through and Dynamic Current Injection: GFM converters must remain connected during grid faults and must be able to inject reactive current rapidly to support voltage recovery.
* ROCOF Response: These units must be able to provide inertial response by injecting active power during periods of high rate of change of frequency (ROCOF), in addition to regular frequency support.
* Phase Jump Response: In events involving sudden phase angle changes between the converter and the grid, the system must manage transient power exchanges to maintain stability.  
To evaluate their performance as established by NESO, several tests have been conducted, including frequency variations in the grid, phase jumps, voltage sags, and grid disconnection to assess islanded operation. These tests have been performed in the Hardware-in-the-Loop (HIL) setup shown in Figure 8. The experimental setup consists of a real-time RTDS simulator, where the plant depicted in Figure 1 has been modeled, and a dSpace controller board executing the control algorithms. The parameters used for both platforms can be found in Appendix A.  
Throughout all the tests, the same nomenclature has been used for all signals. Results corresponding to the virtual flux-oriented control scheme are represented in blue with the subscript \"VFOC\". The results for the control scheme based on flatness theory are shown in red with the subscript \"FBC\". The direct control results are displayed in green with the  
Figure 8: HIL setup.  
subscript \"DC\", and finally, the model predictive control results are represented in yellow with the subscript \"MPC\".
# 5G Network Slice Isolation
## 2 The Principles of Network Slicing and Network Slice Types  
Network slicing is a logical network representation, composed with a specific mobile network infrastructure configuration, which consists of various levels and types of isolation in a physical infrastructure. It is basically enabled by virtualization, containerization, software-defined network (SDN), virtual network function (VNF) service chain, network function virtualization (NFV) [9] and flexible transport network technologies. The MNOis expected to utilize those technologies to provide a secure network environment across the radio access network, transport network and core network. This secure network environment shall be fully optimized with the coexistence of multiple network slices and their different service characteristics and requirements. On the other hand, the tenant expects their network slices' structure to be a standalone and fully independent mobile network. Moreover, other tenants shall not have unauthorized access to their network slices nor unauthorized interception with the other tenants' data.  
Network slicing dynamically gives an MNO flexibility in organizing, coordinating and orchestrating any available resources in the wireless and wired network environment. Those resources can be differentiated into a specific service in a particular location. For example, a manufacturer customer would like to have a network slice with a particular location within a few cell sites only. A utility company would like to have a smart grip network slice in some remote sites. Another case would be a hospital authority customer that would like to have a network slice within a hospital area. Those three typical cases illustrate network slice services that can be dynamically deployed and provisioned in a unique geolocation. Furthermore, these individual network slices can port to other network slice providers or MNO network slice platforms. The GSM Association (GSMA) has provided an introduction of network slice [10] and has proposed the Network Slice Generic Template to formulate a menu for selecting the network slice's perimeters. This GST model can be converted into a network provisioning data model for deploying the network slice [11].
# Leveraging Large Language Models for Sensor Data Retrieval
## 2 Related Work
### Word Embeddings for Table Retrieval
Three statistical models were proposed to achieve this goal: intersecting content between two columns, semantic similarity between content by mapping columns to classes in an ontology, and using word embeddings to measure the similarity of textual content. The work in Ref. [35] proposed a novel technique for pretraining embeddings on a large web table corpus. The authors applied their models to various data analysis tasks, including table retrieval, across different data sources. Evaluation demonstrated that models using pretrained Web table embeddings outperformed those pretrained on text. The approach in Ref. [36] uses graph-based methods to represent tabular data. More specifically, data from relational databases was represented as a graph with three types of nodes: content, header, and file name. In the domain of question answering from tables, notable models include TAPAS [37], which leverages BERT's encoder. This model flattens the table into smaller tokens and concatenates them with the query answer. Additionally, token embeddings are integrated with table-aware positional embeddings before being input into the model. Lastly, also in the field of question-answering models, TaBERT [38] aims to acquire contextual representations for both utterances and the structured schema of tables. This is achieved by generating a content snapshot comprising relevant information related to the utterance, encoding both the utterance and content snapshot using BERT, and applying a vertical self-attention layer to aggregate information from various rows within the content snapshot, effectively capturing cross-row dependencies among cell values. To the best of the authors' knowledge, there are no previous approaches that leverage LLMs for sensor data retrieval with the aim of augmenting existing datasets with new relevant instances.
###### Abstract  
The relaying technologies in co-operative systems are considered a core element in actual and future wireless communications, assisting the network by enhancing its reliability and improving its capability through exploiting co-operativity. In this paper, a co-operative system testbed based on Software Defined Radio (SDR) through Universal Software Radio Peripherals (USRPs) and the Matlab(tm) software is presented. The main novelty in this development of the platform is the implementation of 4G signal features, such as Physical Downlink Shared Channel (PDSCH) and Downlink Shared Channel (DL-SCH) for transport channel coding, which is one of the main contribution of the paper. The developed Multi-Input and Multi-Output (MIMO) SDR co-operative platform is capable of developing prototypes for the Relay Nodes. More specifically, the Amplify-&-Forward (A&F)--with or without Zero Forcing (ZF) and Minimum Mean Square Error (MMSE) Pre-Equalization--and Decode-&-Forward (D&F) protocols were implemented. Both Single-Input and Single-Output (SISO) and MIMO modes are supported by our testbed. The developed A&F and D&F MIMO co-operative systems in this paper utilize Orthogonal Space-Frequency Block Codes (OSFBCs) for the transmission of data symbols from the source to the destination. Our results show that relay nodes can substantially improve the Bit Error Rate (BER) and throughput in communications between the eNodeB (eNB) and User Equipment (UE). In particular, the maximum throughput achieved by conventional MIMO A&F is 9.3 Mbps at \(\text{SNR}=16\) dB, which is 4 Mbps higher than throughput of MIMO Non-Co-operative. It also shows the capacity improvement when considering the pre-equalization in the A&F schemes, compared to the conventional A&F Relay Node. For example, with MIMO A&F-MMSE pattern, a value of 11.8 Mbps is achieved for \(\text{SNR}=16\) dB, which is 84.8 % of the maximum system throughput (13.95 Mbps). On the other hand, the obtained results with D&F schemes far exceed those obtained with A&F strategies, achieving the maximum performance with the 2 \(\times\) 2 MIMO D&F protocol from \(\text{SNR}=8\) dB. Furthermore, this work constitutes a first stage to the implementation of a 5G New-Radio Co-operative System platform.  
amplify-&-forward; decode-&-forward; 4G-software defined radio +
Footnote †: journal: _Electronics_  
0000-0001-6600-6000]R.M.Verdecia-Pena  
125 February 2021
## 4 Node Activity Scheduling
### Cross Layering Optimization
#### 4.4.2 Cross-layering with the MAC Layer  
SERENA provides the MAC layer with the color of each node, the color of its one-hop neighbors and the maximum number of colors used. Given this information, the MAC layer dimensions the frame, assigns active time slot to each node according to its color. During a time slot, all the nodes having the corresponding color can transmit their messages whereas their one-hop neighbors must remain awake to receive them. The other nodes can sleep. The medium access is scheduled in such a way that two nodes with the same color can transfer simultaneously without interfering. No energy, no bandwidth is lost in collisions.  
However, in a real wireless environment, because of the existence of unidirectional links, node mobility, late arrivals of nodes or instability of radio propagation conditions, colors conflict can occur. In this case, the MAC layer is the first layer able to detect this conflict. Hence, it can inform SERENA of conflicting nodes. SERENA solves this conflict by assigning a new color to one conflicting node [10].
# Reduced Graphene Oxide-Metalloporphyrin Sensors for Human Breath Screening
## 2 Materials and Methods
### VOC-Sensing Pattern Recognition  
The gas-sensing responses of the assembled RGO-metalloporphyrin sensors were statistically analyzed to evaluate their discriminative capabilities. First, the dimensions of the original data obtained from RGO-metalloporphyrin sensors were reduced using principal component analysis (PCA) to visualize and explore the sensing data (in MATLAB). \(R_{norm}\) and the integral area under the curve (_Area_) from a sensing response curve were used as variables for PCA. A PCA data matrix (\(n\times p\)) consisted of the experimental measurements (\(n=24\)) and the measured feature values (\(R_{norm}\) and _Area_) of each sensor (\(p=8\)).  
VOC classification analysis was then conducted using three ML algorithms, namely logistic regression (LR) [41], support vector machine (SVM) [42], and artificial neural network (ANN) [43], and their performances were compared (Figure 3). MATLAB, Scikit-learn, and TensorFlow were used to implement LR, SVM, and ANN, respectively. LR is a classification technique that assigns a probability to the relationship between input features and a particular outcome using a sigmoid function (Figure 3a). SVM constructs a hyperplane in high-dimensional feature space with the largest margin to separate different classes (Figure 3b). Finally, ANNs are widely used to capture non-linear patterns in the data through training of the weights carrying information from one node to another (Figure 3c). The algorithms predicted the gas identity (output) using \(R_{norm}\) and _Area_ (input). A one-vs.-rest multi-class classification was used, where a single classifier for each class was trained considering the samples in the target class as positive and all other classes as negative samples. The classification performance was evaluated using 3-fold cross-validation, where the dataset was randomly partitioned into 3 equally sized bins. The first bin was then used for the validation, while the remaining 2 bins were used for training to generate the models. This process was repeated 3 times until all bins were used for the validation. The performance of the classifier model was evaluated using the aggregate of all predictions of the test datasets. LR with L1 regularization was used to prevent model overfitting. A linear kernel was used for SVM. For ANN, 200 epochs with the learning rate of 0.001, the rectified linear unit activation function and the sparse categorical cross-entropy loss function were used. Here, the number of epochs defines the number of times that ANN worked through the entire training dataset. The number of hidden layers and nodes were the result of a trial-and-error procedure; the optimal architecture had one hidden layer with three nodes. To evaluate the predictive performance of the classification methods, accuracy, precision, recall, and F1-score metrics were calculated using a confusion matrix [44].
# CoMP-Aware BBU Placements for 5G Radio Access Networks over Optical Aggregation Networks
## 4 CoMP Aware BBU Placement Algorithm
### Computational Complexity
At 25 usec and 35 usec, there is no change in the number of BBUs and the number of physical links after the reconfigurable placement of the front-haul links because the maximum delay is not sufficient for reconfiguring the RRH to be served by BBU. At delays equal 50 usec, 60 usec, and 70 usec, the number of BBUs do not change after reconfiguration, while the number of physical links increased by 94%, 82%, and 65% of the optimal solution, respectively. This can be explained by the fact that the maximum allowable delay increases the probability that the adjacent sites are served by the same BBU increase, leading to a higher number of intra-CoMP. Therefore, the number of migrated users decreases, and the number of sites that need to change their served BBU decreases. At delays equaling 80 usec, the number of BBUs decreased to two BBUs instead of three in the previous delays. This led to an increase in the number of intra-CoMP as the probability  
Figure 6: Percentage of transferred users from Inter-BBU COMP to Intra-BBU COMP (for each case, W = 6). Figure 7: Number of BBU pools and physical links (W = 6, 1 st case). of sites being served by different BBUs decreases. Therefore, physical links increased by 40%. At delays of 100 usec and 200 usec, all sites in the area are served by one BBU; as a result, there is no inter-CoMP. Therefore, there is no reconfiguration, and the number of physical links remains constant.