esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-User Linear Equalizer and Precoder Scheme for Hybrid Sub-Connected Wideband Systems
## 4 Multi-User Equalizer Design
### Proposed Method Derivation
With \(\mathbf{U}^{(1)}\), the same procedure may be repeated for the other users according to the mapping between users and iterations defined by \(u_{i}=\text{imod}\ U\). To compute the optimization problem of (29), we need to compute the correlation matrix \(\mathbf{w}_{a}^{(i)^{H}}\mathbf{P}^{(i-1)^{H}}\mathbf{h}_{\alpha\mu,i\mu,k}\mathbf{h }_{\alpha\mu,i\mu,k}^{H}\mathbf{P}^{(i-1)}\mathbf{w}_{a}^{(i)}\) for all the elements of the selected codebook \(\mathcal{F}_{a,\mu_{1}}\), which may be accomplished with the following expression:  
\[\mathbf{r}_{i,k}=\text{diag}\Big{(}\mathbf{A}_{\pi x,\mu_{i}}^{H}\mathbf{D}_{ \mu_{i}}\mathbf{P}^{(i-1)^{H}}\mathbf{h}_{\alpha\mu,i\mu,k}\mathbf{h}_{\alpha \mu,i\mu,k}^{H}\mathbf{P}^{(i-1)}\mathbf{D}_{\mu_{i}}\mathbf{A}_{\pi x,\mu_{i}} \Big{)}. \tag{30}\]  
Notice that \(\mathbf{P}^{(i)}\mathbf{P}^{(i)}=\mathbf{P}^{(i)}\) since \(\mathbf{P}^{(i)}\) is an idempotent matrix. Nonetheless, as the Gram-Schmidt procedure may lead to a loss of orthogonality among vectors [35], we use a Gram-Schmidt algorithm with reorthogonalization that amounts to applying two times the projection matrix \(\mathbf{P}^{(i)}\) or using \(\left(\mathbf{P}^{(i)}\right)^{2}\) instead of \(\mathbf{P}^{(i)}\). ```
Inputs:\(N_{\pi x},N_{RF},N_{c},\mathbf{H}_{\alpha\eta}\)
```  
```
1:\(\mathbf{U}^{(0)}=0\)
2:for\(i=1\) to \(N_{RF}\)do
3:\(u_{i}=\text{imod}U\)
4:\(\mathbf{P}^{(i-1)}=\left(\mathbf{I}-\mathbf{U}^{(i-1)}\mathbf{U}^{(i-1)^{H}} \right)^{2}\)
5:\(\mathbf{f}^{(i)}=0\)
6:for\(k=1\) to \(N_{c}\)do
7:\(\mathbf{r}_{i,k}=\text{diag}\Big{(}\mathbf{A}_{\pi x,\mu_{i}}^{H}\mathbf{P}^ {(i-1)^{H}}\mathbf{h}_{\alpha\mu,i\mu}\mathbf{h}_{\alpha\mu,k}^{H}\mathbf{P}^ {(i-1)}\mathbf{D}_{\mu_{i}}\mathbf{A}_{\pi x,\mu_{i}}\Big{)}\)
8:\(\mathbf{f}^{(i)}=\mathbf{f}^{(i)}+\left(\mathbf{r}_{i,k}+\sigma_{n}^{2}\right) ^{-1}\)
9:endfor
10:\((q,l)=\text{arg min }\mathbf{f}^{(i)}\)
11:\(\mathbf{w}_{a}^{(i)}=\mathbf{P}^{(i-1)}\mathbf{D}_{\mu_{i}}\mathbf{A}_{\pi x,\mu_{i}}(\phi_{q}^{u_{i}}-\varphi_{q,l}^{u_{i}})\)
12:\(\mathbf{U}^{(i)}=\left[\mathbf{U}^{(i-1)},\mathbf{P}^{(i-1)}\mathbf{w}_{a}^{( i)}/|\mathbf{w}_{a}^{(i)}|\right]\)
13:\(\mathbf{W}_{a}^{(i)}=\left[\mathbf{W}_{a}^{(i-1)},\mathbf{w}_{a}^{(i)}\right]\)
14:endfor
```  
**Algorithm 1**The proposed analog-digital multi-user linear equalizer algorithm for sub-connected architecture
# Optimal Proactive Caching for Multi-View Streaming Mobile Augmented Reality
## 3 System Model
### Mathematical Programming Formulation
In previous expressions, the product of decision variables \(p_{sj}h_{rl}^{s}\) and \(p_{sj}y_{rj}\) exists and creates a non-linearity. In addition, note that when executing the matching function at the location \(j\,(W_{rj}y_{rj})\), the product of decision variables \(p_{sj}h_{rl}^{s}y_{rj}\) also appears. To linearize the expressions above, so that to utilize linear integer programming solution methodologies, new auxiliary decision variables are brought in. A new decision variable \(\alpha_{rsj}\) is introduced as \(\alpha_{rsj}=p_{sj}y_{rj}\) and the constraints should be added as follows,  
\[ \begin{split}\alpha_{rsj}&\leqslant p_{sj},\\ \alpha_{rsj}&\leqslant y_{rj},\\ \alpha_{rsj}&\geqslant p_{sj}+y_{rj}-1\end{split}  \tag{15}\]similarly, a new decision variable \(\beta_{rslj}\) is introduced as \(\beta_{rslj}=p_{sjl}h_{rl}^{s}\) and the constraints should be added as follows,  
\[ \begin{split}&\beta_{rslj}\leqslant p_{sj},\\ &\beta_{rslj}\leqslant h_{rl}^{s},\\ &\beta_{rslj}\geqslant p_{sj}+h_{rl}^{s}-1\end{split}  \tag{16}\]  
the aforementioned constraint (6) is affected and should be rewritten as follows,  
\[h_{rl}^{s}\leqslant\sum_{j\in\mathbf{M}}\beta_{rslj},\ \forall r\in\mathbf{R},\ \forall s\in\mathbf{S_{r}},\ \forall l\in\mathbf{L_{rs}} \tag{17}\]  
In addition, note that \(p_{sj}\) is a binary decision variable and causes \(p_{sj}=p_{sj}^{2}\). Thus, we have \(p_{sjl}h_{rl}^{s}y_{rj}=\alpha_{rslj}\beta_{rslj}\). A new decision variable \(\lambda_{rslj}\) is introduced as \(\lambda_{rslj}=\alpha_{rslj}\beta_{rslj}\) and the following constraints should be added as follows,  
\[ \begin{split}&\lambda_{rslj}\leqslant\alpha_{rslj},\\ &\lambda_{rslj}\leqslant\beta_{rslj},\\ &\lambda_{rslj}\geqslant\alpha_{rslj}+\beta_{rslj}-1\end{split}  \tag{18}\]
# An Entity Relation Extraction Method Based on Dynamic Context and Multi-Feature Fusion
## 3 Methodology  
SPERT-DC extends the joint entity recognition and relation extraction model based on SPERT in structure, whose framework is shown in Figure 1. It performs entity relation extraction subtasks while completing the named entity recognition. The model is divided into word emdedding layer, entity recognition layer, and relation classification layer. The word embedding layer convert the text sentence to vector with BERT to get the representation of text (\(e_{1}\), \(e_{2}\),..., \(e_{n}\), cls), where the last token cls represents the word vector of the special token CLS. All token subsequences among sentence are used for detecting entities in the entity recognition layer. For example, the sentence (you are the most beautiful) maps to the subsequences (you), (you, are), (the, most, beautiful), etc. For possible entities, its representation, width embedding and CLS context are input into entity recognition module. At last, the entity type is obtained by softmax classifier. The relation is extracted from some entity pairs in relation classification layer, which also relies on text context. The model chooses the local context between the entity pair instead of the token CLS as text context. Then the entity pair representation is input to the relation extraction module, and relation class is obtained by activation of sigmoid.  
The baseline creates a static mask matrix with the start and end position of entity pair in sentence, where mask[startend] = 1, with which the baseline selects the corresponding area of sentence representation as text context. It can be seen that the static mask only focus on the limited information between entity pair, which will lead to lack of context when the distance between entity pair is small.  
This paper propose SPERT-DC to solve the limitation of baseline on selecting text context for the research of relation extraction. According to position of entity, the method corrects text context with dynamic mask to improve the relation extraction effect of model at last.
## 4 Results  
### Measured Values of Pavement Mechanical Response  
When a vehicle enters the monitored cross-section, the wavelengths of the strain and stress FBG sensors exhibit measurable shifts. As the vehicle traverses the section, pavement deformation recovers, and the sensor wavelengths return to their baseline values, as illustrated in Figure 6. The stress-strain response induced by vehicular loading is derived from these wavelength shifts. Due to variations in vehicle types, heavier axles generate larger strain and stress magnitudes, evidenced by distinct peak variations in Figure 6. To isolate the impact of individual axles during modulus back-calculation, only the max-mum peak wavelength shift corresponding to strain and stress from a single vehicle pas-sage was selected for analysis.  
Due to space constraints, Table 2 presents only the mechanical response data measured by the central column sensors in each cross-section during the passage of a randomly selected vehicle. Preliminary observations indicate that pavements treated with high-strength geopolymer and polymer grouts exhibit superior mechanical performance. Low-strength geopolymer without waterproofing agents exhibits significantly increased vertical strain under humid conditions, leading to susceptibility to cracking and deformation. The addition of waterproofing agents improves vertical strain by mitigating water erosion, though transverse strain remains higher than in high-strength systems. Untreated conventional materials are most affected by loading and water erosion, showing pronounced vertical and transverse strains. Additionally, crack-free construction sections validate the practical effectiveness of theoretical material performance, demonstrating stable stress-strain  
Figure 6: The wavelength change of the sensors when a two-axle vehicle passed by. The red circles represent the peak response value used for subsequent calculations. (**a**) Vertical strain sensor; (**b**) longitudinal strain sensor; (**c**) transverse strain sensor; (**d**) vertical stress sensor.  
behavior and well-controlled transverse deformation. Crucially, the back-calculation of pavement elastic modulus incorporated the statistical analysis of mechanical response data from a large sample of randomly selected vehicles, ensuring enhanced representativeness of the results.
# Preliminary Analysis of a Lightweight and Deployable Soft Robot for Space Applications
## 2 Robot Concept Design and Models
### Load and Pose Estimation  
To study the behaviour of the flexible robot, it is essential to estimate the external load acting on the system. In this way, the deformation of the flexible link can be calculated, and the pose estimation can be performed.  
The motor torques \(\mathbf{\tau}_{m}=~{}[\tau_{1},~{}\tau_{2},~{}\tau_{5}]^{T}\) and positions \(\mathbf{q}_{m}=[\theta_{1},~{}\theta_{2},~{}\theta_{5}]^{T}\) are considered as known parameters. In a real case, appropriate sensors can be used to provide these quantities. The external forces \(\mathbf{f}_{e}=[f_{e,~{}x},f_{e,y},~{}f_{e,z}]^{T}\) act on the robot end-effector (EE) and the external torques are assumed null \(\mathbf{\mu}_{e}=[0,~{}0,~{}0]^{T}\). The virtual springs have all the same stiffness \(k=k_{3}=k_{4}=k_{6}=~{}k_{7}\), as the system is assumed to be orthotropic. In addition, space application is considered, so weight force is set to zero in the algorithm.  
An elastostatic approach is adopted to solve the problem. The Jacobian matrix is \(\mathbf{J}=[J_{1},~{}\dots,~{}J_{7}]\), with  
\[\mathbf{J}_{i}=\left[\begin{array}{c}\mathbf{z}_{i-1}\times\begin{pmatrix}\mathbf{p}_{e} -\mathbf{p}_{i-1}\\ \mathbf{z}_{i-1}\end{pmatrix}\end{array}\right] \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Link** & \(\mathbf{a}_{i}\) & \(\mathbf{a}_{i}\) & \(\mathbf{d}_{i}\) & \(\mathbf{\theta}_{i}\) \\ \hline
1 & 0 & \(\pi/2\) & 0 & \(\theta_{1}\) \\
2 & \(l_{1}\) & \(-\pi/2\) & 0 & \(\theta_{2}\) \\
3 & 0 & \(\pi/2\) & 0 & \(\theta_{3}\) \\
4 & \(l_{2}\) & 0 & 0 & \(\theta_{4}\) \\
5 & \(l_{1}\) & \(-\pi/2\) & 0 & \(\where \(\mathbf{z}_{i-1}\) is given by the third column of the rotation matrix \(R_{i-1}^{0}\), \(\mathbf{p}_{e}\) is the position of the EE and \(\mathbf{p}_{i-1}\) is the position of the joint \(i-1\), expressed in the operating system. The following equation is valid:  
\[\mathbf{\tau}=\mathbf{J}^{T}\cdot\mathbf{\gamma}_{e} \tag{5}\]  
where \(\mathbf{\tau}\) is the torque vector and \(\mathbf{\gamma}_{e}=\left[\bar{\mathbf{\tau}}_{e}^{T},\ \mathbf{\mu}_{e}^{T}\right]^{T}\) represents the external load. Considering only the motor torques \(\mathbf{\tau}_{m}\), it is:  
\[\mathbf{\tau}_{m}=\mathbf{J}_{m}^{T}\cdot\mathbf{\gamma}_{e} \tag{6}\]  
where \(\mathbf{J}_{m}=\left[\mathbf{J}_{1}\,\mathbf{J}_{2}\,\mathbf{J}_{5}\right]\). Finally, for the inflatable link model, reclaiming (2):  
\[\mathbf{\tau}_{k}=k\cdot\mathbf{q}_{k}. \tag{7}\]  
where \(\mathbf{\tau}_{k}=\left[\tau_{3},\ \tau_{4},\ \tau_{6},\ \tau_{7}\right]^{T}\) are the virtual spring torque.  
Using the (5-7), a recursive algorithm is developed, as illustrated in Figure 5. It needs as input the motor torques \(\mathbf{\tau}_{m}\) and positions \(\mathbf{q}_{m}\) and the virtual spring stiffness \(k\), and it estimates the external load \(\mathbf{\gamma}_{e}\), the joint variable related to the link deformations \(\mathbf{q}_{k}\) and relative torques \(\mathbf{\tau}_{k}\).
# A Convolutional Neural Network SAR Image Denoising Algorithm Based on Self-Learning Strategies
## 2 SAR Image Denoising Algorithm Based on Self-Learning Strategy DnCNN
### Network Structure
As shown in Figure 3, it can be seen that the untrained and optimized DnCNN model has poor denoising performance on SAR images.
# Investigation of Data Quality Assurance across IoT Protocol Stack for V2I Interactions
## 1 Introduction
### Availability  
Availability refers to the continuous and uninterrupted data flow between systems or devices in real-time. It ensures that data remains readily available and immediately accessible after transmission, free of interruptions, delay, or loss. A typical use-case example within V2I is incident detection and reporting. As observed by [PERSON] et al. [PERSON] et al. (2018), inefficient accident detection and reporting systems have resulted in an increase in the number of fatalities from auto crashes. The authors proposed an accident detection and reporting system (ADRS) using Wi-Fi or 3G/4G to transmit and report vehicle telemetry data from a crash site to an emergency response unit and a nearby hospital.
## 2 Related Work
It elects cluster heads based on the votes that it collects from the network nodes. The Energy Efficient Cluster Formation Protocol (EECFP) [17] elects the nodes with the higher energy as cluster heads and rotates them in each round to provide a balance of energy consumption and to minimize the energy spend for cluster formation. In [18], a centralized routing protocol, called Base-Station Controlled Dynamic Clustering Protocol (BCDCP), which distributes the energy dissipation evenly among all the sensor nodes to improve the network lifetime, and its average energy savings are presented. The base station receives the residual energy of each node, and then, it computes the average energy level of all the nodes. Then, it elects as candidate cluster heads a number of nodes, which have a higher residual energy than this value. This protocol provides a balanced energy consumption. However, the selection of the node with the highest energy as a cluster head at a round may cause the other nodes to spend more energy to send data to this node. The selection of a node that allows the other nodes in the cluster to spend less energy is a better solution. All the aforementioned protocols try to minimize the energy consumption using different algorithms. These algorithms offer a good solution, since they select the node with the higher residual energy in the cluster as the cluster head for the next round. However, this does not assure the maximum prolongation of the overall network lifetime. Therefore, if the node with the highest residual energy is a node located at the side of the cluster, this can lead other nodes to spend considerable amounts of energy to reach that node, which cannot be energy efficient for the entire network. This is the reason we propose a protocol that elects as cluster heads nodes that minimize the total energy consumption in a cluster.
## 3 Results
### Annual Maps of Sky Status
Speaking about interpreting \(\mathrm{k_{d}}\) as ASI, Figure 10a shows the distribution of ASI in the event of DCS sky conditions over Greece. This can be compared again with Figure 12b in [42]. The resemblance of the two Figure is remarkable and anticipated as the \(\mathrm{k_{d}}\) values in the DCS cases (interpreted as \(\mathrm{ASI_{CS}}\)) should reflect the same \(\mathrm{T_{L}}\) pattern under clear skies over Greece. One should, however, note that this resemblance does not imply two identical copies because the \(\mathrm{T_{L}}\) factor refers to the total solar radiation extinction, while the ASI to only the scattering effect of atmospheric constituents on solar radiation. Now that the \(\mathrm{ASI_{CS}}\) values have been estimated, it is easy to compute the atmospheric absorption index under clear skies (\(\mathrm{AAI_{CS}}\)) because of the validity of the equation: \(\mathrm{AAI_{CS}}+\mathrm{ASI_{CS}}=1\)[23]; the unity in the equation denotes the total attenuation of solar radiation due to both absorption and scattering effects. Based on this equation, Figure 10b was prepared, which shows the distribution of \(\mathrm{AAI_{CS}}\) over Greece. As anticipated, the pattern in Figure 10a is the reverse of that in Figure 10b. By comparing both Figure with Figure 12b in [42], one observes that the scattering pattern of Figure 10a looks more similar to that of Figure 12b [42] than the pattern of Figure 10b does. This observation leads to the assumption that most of the solar radiation attenuation (expressed by \(\mathrm{T_{L}}\)) is due to atmospheric scattering than absorption over Greece on a yearly basis. Figure 8: _Cont._  
Figure 8: Distribution of the annual mean sunshine duration (SSD) over Greece for (**a**) clear, (**b**) intermediate, and (**c**) overcast skies derived from the TMYs of the 43 sites. The subscripts CS, IS, and OS to SSD represent clear, intermediate, and overcast skies, respectively. All SSDs are in hours. Figure 10: Cont. Figure 9: Distribution of the annual mean sunshine duration under clear skies (SSDm) over Greece derived from the TMYs of the 43 sites. These values were estimated by applying the model developed in Section 3.5: SSDm = DCS + DIS-1 + 0.8 \(\times\) DIS-2 + 0.5 \(\times\) DIS-3. The subscript m to SSD implies model.
## 3 Result and Discussion
### Stress-Strain Curves  
The load-longitudinal displacement (\(N\)-\(\Delta\)) curves at each end of the column under axial compression were converted to \(\sigma\)-\(\varepsilon\) curves and plotted on the same graph as the longitudinal and transverse stress-strain curves measured at the column surface, as shown in Figure 11. The left side of the graph shows the transverse strain at the column surface (indicated by the letter L in the graph), while the right side shows the longitudinal strain at the two ends of the column (indicated by the letter Y in the graph) and at the surface of the column (indicated by the letter S in the graph) respectively. The longitudinal strains at the two ends of the column represent the overall compression strain of the compressed column, converted from the longitudinal displacement obtained by the loading testing machine. The strains at the surface of the column reflect the longitudinal deformation in the area of the concrete cover, measured by the resistance strain gauges.  
As can be seen from Figure 11, under the axial load, the longitudinal strain on the surface of both uncorroded and stirrup-corroded columns was greater than the transverse strain, and the average Poisson's ratio for each column was 0.27 (GZ0), 0.27 (GZ5), 0.24 (GZ10), and 0.39 (GZ15), respectively. The Poisson's ratio did not change much for the uncorroded columns and columns with an actual stirrup corrosion rate of less than 9.26%. However, for column GZ15 with an actual stirrup corrosion rate of 12.57%, the lateral strain grew faster than that of the other corroded columns and the Poisson's ratio increased significantly, indicating that the lateral restraint effect on the column was significantly reduced when the stirrup were seriously corroded. Comparing the stress-longitudinal strain curves of the column ends and column surfaces in Figure 11, it can be seen that during the axial compression process, the strain in the concrete cover area of both the uncorroded and corroded column surfaces was smaller than the compressive strain of the whole column at both ends, and the deformation of both was not synchronized under the same load, indicating that the load on the concrete cover was smaller than that on the core area of the concrete. In addition, the slope of the stress-strain curve decreased with increasing corrosion rate after the actual stirrup corrosion rate was greater than 4.95% for both column ends and column surfaces, and the ability of the column to resist deformation decreased.
# When 5G Meets Deep Learning: A Systematic Review
## 3 Results
### What Are the Main Deep Learning Techniques Used in 5G Scenarios?
#### 3.3.3 Cnn  
CNN models are created to deal with data that come from multiple arrays or multivariate arrays and extract relevant features from them. In other words, the convolution layer is applied to process data with different dimensionality: 1D for signals and sequences, 2D for images or audio spectrograms, and 3D for video or volumetric images [9]. As a result, this layer was typically used to deal with several types of data in the works found in this systematic review.  
The works in [29; 34; 35; 75], presented the input data for the CNN models as an image form in order to take advantage of the natural features of the convolutions applied by the CNN layers. Both temporal and geographical aspects were considered in the works presented in [6; 44; 45]. These are relevant plans since the metrics have different behavior according to the time of the day and the base station location. As a result, these works used CNN to take into consideration temporal and space aspects at the same time and extract relevant joint patterns. The works presented in [7] used CNN models and considered several aspects that affect the CSI as input for the models such as frequency band, location, time, temperature, humidity, and weather. The authors considered 1D and 2D convolutions in order to extract frequency representative vector from CSI information.  
A separate work used a special architecture of CNN called ResNet [19]. This architecture was proposed to solve the notorious problem of a vanishing/exploding gradient. The main difference offered by the ResNet architecture is that a shortcut connection is added every two or three layers in order to skip the connections and reuse activation from a previous layer until the adjacent layer learns its weights. This architecture was used to process modulated frequency-domain sequence data for the purpose of channel estimation.  
In addition to the LSTM and CNN models, the authors proposed a model named a temporal convolutional network (TCN) in [35]. Unlike the other models, the TCN architecture considers the temporal dependency in a more accurate way. The interested reader may find out more detailed information TCN by consulting [91].  
In [26], besides describing a fully connected layers and an LSTM models, the authors also proposed a CNN model for use with LSTM to represent the coding/decoding schema as convolution functions.
# Snow Depth Variations in Svalbard Derived from GNSS Interferometric Reflectometry
## 2 Data and Methods  
### Data  
A field experiment with GNSS-IR to sense snow depth variations was carried out in Ny-Alesund (78\({}^{\circ}\)55\({}^{\prime}\)N, 11\({}^{\circ}\)55\({}^{\prime}\)E), Svalbard, as shown in Figure 1. The local environment is ideal for snow depth estimation due to the radio silence in Ny-Alesund and flat ground around the GNSS tracking station. Considering a 1.67 m-high antenna, the specular reflection footprint is within a circle of \(\sim\)20 m in a planar area when the satellite elevation angle is 5\({}^{\circ}\)-25\({}^{\circ}\). Therefore, the shape of the terrain can be taken as a small plane and has almost no influence on snow depth estimation. The GNSS station is equipped with a Unicore UR4B0 receiver and a Novatel GNSS-750 antenna, and GPS L1 SNR observations from 2014 to 2018 were collected at a sampling rate of 30 s. The meteorological station at Ny-Alesund, approximately 230 m from the GNSS station, provided in situ snow depth measurements and rainfall observations. The in-situ snow depth measurements from the meteorological station were collected at UTC 6 h every day. All the meteorological observations can be found at [[http://eklima.met.no](http://eklima.met.no)]([http://eklima.met.no](http://eklima.met.no)), which contains historical data and real-time meteorological observations from the Norwegian Meteorological Institute (NMI).