esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Methodology Proposed for Energy Management with a Realistic Point of View
### Methods for Solving the Problem (OCP): Pontryagin's Maximum Principle  
This subsection is dedicated to showing the approach used. The primary tool used is PMP [7], which ensures a necessary optimality condition for the optimal control problem. This is achieved by introducing the adjoint vector \(P=(p_{1},p_{2})\) to the state vector \(X=(x,v)\), and the Hamiltonian function \(H(X,P,F_{m})\) is then defined as follows:  
\[H(X,P,F_{m})=-|F_{m}|v+p_{1}v+p_{2}\frac{F_{m}-Fr}{M} \tag{12}\]  
The pair \((X,F_{m})\) is called an admissible solution if \(X\) is the state trajectory corresponding to \(F_{m}\) and the conditions of the optimal control problem (11) are satisfied. An admissible pair that minimizes the cost function is called an optimal solution, denoted by \((X^{*},F_{m}^{*})\).  
The adjoint vector \(P=(p_{1},p_{2})\) verifies these equations:  
\[\begin{cases}\dot{p}_{1}&=-\frac{\partial H(X,P,F_{m})}{\partial x}=0\\ \dot{p}_{2}&=-\frac{\partial H(X,P,F_{m})}{\partial v}=|F_{m}|-p_{1}\end{cases} \tag{13}\]  
The optimal control problem (11) is autonomous; then, \(H(X,P,F_{m})\) is constant. The optimal control \(F_{m}^{*}\) maximizes the Hamiltonian almost everywhere:  
\[\begin{split} H&=\max_{F_{m}}H(X,P,F_{p})\\ &=p_{1}v-\frac{p_{2}}{M}F_{r}+\max_{F_{m}}\Bigl{\{}-|F_{m}|v+\frac{p_{2 }}{M}F_{m}\Bigr{\}}\end{split} \tag{14}\]  
According to [35], the optimal strategy for the minimum time problem is given by the following:  
\[F_{m}^{*}=\left\{\begin{array}{ll}F_{max}&\text{for}\;\;0\leq t\leq\Delta t _{o}\\ F_{min}&\text{for}\;\;\Delta t_{o}<t\leq T_{min}\end{array}\right. \tag{15}\]  
The expression of the minimum travel time, \(T_{min}\), with \(\Delta t_{o}=\frac{T_{min}}{2}\):  
\[T_{min}=-\frac{M(F_{min}v_{0}-F_{max}v_{f})+\sqrt{M(F_{max}-F_{min})\Bigl{(}2F _{max}F_{min}(x_{f}-x_{0})-M(F_{min}v_{0}^{2}-F_{max}v_{f}^{2})\Bigr{)}}}{F_{ max}F_{min}} \tag{16}\]  
This strategy is reflected in the accelerator being at full travel until it reaches the maximum speed (high acceleration). Then, very high braking is applied until the motor stops, as there is not enough time.  
For \(T_{min}<T<T_{lim}\), \(T_{lim}\) is the time limit when the optimal strategy of the problem (11) is generated via the following:  
\[F_{m}^{*}=\left\{\begin{array}{ll}F_{max}&\text{for}\;\;0\leq t\leq\Delta t\\ 0&\text{for}\;\;\Delta t<t\leq T-\Delta t\\ F_{min}&T-\Delta t<t\leq T\end{array}\right. \tag{17}\]  
The terminal condition \(x_{f}\), allows the explicit computation of \(\Delta t\):  
\[\Delta t=\frac{F_{max}T-\sqrt{F_{max}^{2}T^{2}+2M(F_{max}-F_{min})(x_{0}-x_{f} +v_{0}T)}}{F_{max}-F_{min}} \tag{18}\]  
The optimal strategy is illustrated in Figure 8 with its optimal speed profile, which was determined through the dynamic system. A solution is the _bang_ type, if the control is equal to its maximum, or its minimum, which corresponds to the maximum acceleration or deceleration phase. An _inactivation_ is defined when the control variable is null over a time interval, which corresponds to maintaining a constant speed.
# Wireless Caching: Making Radio Access Networks More than Bit-Pipelines
## 3 Request Time Prediction: Beyond Content Popularity
### RDI Estimation Methods  
Artificial Intelligence (AI) and big data technologies provide powerful tools for understanding user behaviors in the time domain [25; 26; 27]. A time-varying popularity prediction for video clips can be found in [28; 29], in which real data from YouTube and Facebook are used. In practice, the request time is also affected by one's environments, activities, social connections, etc. For instance, one tends to watch video clips to kill time in the subway or during leisure time, but internet surfing is strictly prohibited while driving. Consequently, user-specific prediction brings together human behavior analysis, natural language processing (NLP), social networks, etc., leading to many cross-disciplinary research opportunities that include but are not limited to  
* Learning a user's historical requests and data rating [30; 31],
* Exploiting the impact of social networks, recommendation systems, and search engines,
* Discovering relevant content using NLP,
* Analyzing a user behaviors, e.g., activities, mobilities, and localizations.  
We demonstrate a method to estimate empirical RDI based on a user's historical requests. In practice, a user is mostly attracted by content labels, titles, or keywords of content items. Content items with the same label may have identical RDI. A BS collects a user's historical request data for \(N\) content items with the same label and counts the total number of requests by delay \(x\), denoted by \(N(x)\). The cumulative probability density (\(c.d.f.\)) of the request delay can be estimated as the ratio given by \(\hat{P}(x)=\frac{N(x)}{N}\). On the other hand, similar users can be classified according to their preferences. The BS may also collect \(K\) similar users' historical requests for a content item and count their total number of requests by delay \(x\), denoted by \(K(x)\). Then the empirical \(c.d.f.\) of \(X\) can be estimated as \(\hat{P}(x)=\frac{K(x)}{K}\).  
Practical RDI estimation is a rather challenging issue, because the labels of content items are similar but not the same, and so are the request behaviors of users. Moreover, a user's request time can be affected by many other factors, such as social media, relevant reading, and the user's own physical status or environments, as noted above. Besides, one may easily imagine a periodic request fluctuation as users tend to generate more video requests in everyday leisure time. In summary, request time prediction induces many cross-disciplinary research opportunities, bringing together machine learning, recommender systems, social networks, NLP, and human behavior analysis. To protect user privacy, federated learning without the need for uploading raw data is expected to play a key role in capturing popularity skewness and asynchronous requests [32].
## 3 Materials and Methods
### Meshing of the Flow Domain  
The flow domain has been spatially discretized with an unstructured tetrahedral to reduce the skewness of the mesh elements, as shown in Figure 2a. The mesh density has been kept significantly higher in the aperture region as it is the primary area of interest in the current investigations. In order to ensure that the numerically predicted results are independent of the mesh sizing used [39; 40], five different meshes have been generated and compared for accuracy in predicting the multiphase flow behavior at the fracture-matrix interface. These meshes correspond to element sizes of 5 mm, 3.5 mm, 2.8 mm, 2 mm, and 1.8 mm. The resulting number of elements in the flow domain is summarized in Table 1.  
Figure 1: Naturally fractured tight reservoir sample: (**a**) 3D model; (**b**) geometric dimensions.  
Figure 2: The meshing of the flow domain: (**a**) mesh M(3); (**b**) mesh sensitivity analysis results.  
The mesh sensitivity analysis results are shown in Figure 2b. It can be seen that as the number of mesh elements increases from M1 to M3, the stress fluctuations (\(\Delta\sigma\)) and overall displacement of the rock matrix (\(d\)) increase. When the number of mesh elements increases from M3 to M5, there is a negligibly small change in \(\Delta\sigma\) and \(d\). Thus, it is clear from the mesh sensitivity analysis that M3 (shown in Figure 2a) is the most suitable mesh and accurately predicts the petrophysical properties of naturally fractured tight reservoirs, and hence it has been chosen to conduct further investigations in this study.
# Modification of Hand Muscular Synergies in Stroke Patients After Robot-Aided Rehabilitation
###### Abstract  
The central nervous system (CNS) is able to control a very high number of degrees of freedom to perform complex movements of both upper and lower limbs. However, what strategies the CNS adopts to perform complex tasks are not completely clear and are still being studied. Recent studies confirm that stroke subjects with mild and moderate impairment show altered upper limb muscle patterns, but the muscular patterns of the hand have not completely investigated, although the hand represents a paramount tool for performing activities of daily living (ADLs) and stroke can significantly alter the mobilization of this part of the body. In this context, this study aims at investigating hand muscular synergies in chronic stroke patients and evaluating some possible benefits in the robot-aided rehabilitation treatment of the hand in these subjects. Seven chronic stroke patients with mild-to-moderate impairment (\(FM>30\)) were involved in this study. They received a 5-week robot-aided rehabilitation treatment with the Gloreha hand exoskeleton, and muscle synergies of both the healthy and injured hand were evaluated at the beginning and at the end of the treatment. The performed analysis showed a very high degree of similarity of the involved synergies between the healthy and the injured limb both before and after the rehabilitation treatment (mean similarity index values: H-BR: \(0.88\pm 0.03\), H-AR: \(0.94\pm 0.03\), BR-AR: \(0.89\pm 0.05\)). The increasing similarity is regarded as an effect of the robot-aided rehabilitation treatment and future activities will be performed to increase the population involved in the study.  
muscular synergies; robot-aided rehabilitation; non-negative matrix factorization algorithm; stroke; hand +
Footnote †: journal: Applied  
Acticle
# Comparative Study of Sound Transmission Losses of Sandwich Composite Double Panel Walls
## 3 Double Panel Air Cavity, Sandwich and Composite Structures
### Double Panel Air Cavity Structures
In their investigation, it was observed that at some low frequency range, the STL of the DPS with enclosed air cavity followed the same trend with that of the sandwich structure with corrugated core. However, along the medium and high frequency range, the STL of the sandwich corrugated core was lower than the STL of the double panel with air cavity. The reason for this result was adduced to the fact that the corrugated core between the panels provided a strong structural connection that reduced the effectiveness of the sound energy transmitted. To improve the STL of such sandwich corrugated core, an investigation was carried out by [PERSON] et al. [52]. The authors introduced perforations both in the corrugated core and the facesheet panel which significantly increased the STL at the low frequency region. They further showed that panels with non-uniform perforated pore diameter produced higher STL than those whose pore diameters were uniform. Moreover, the corrugated cores were also filled with porous materials and the sandwich plate was upgraded to a functionally graded material. Their solution showed different STL performance of the functionally graded material sandwich structure at different frequency regions. It is therefore necessary to investigate the type of corrugated core and their structural design when it is to be used for specific application to produce improved STL of sandwich structures.
# A Decentralized Receiver in Gaussian Interference
## 4 Ergodic Bounds
### Performance in the Presence of an Interferer  
One motivation for increasing receive diversity is to better mitigate the effect of interference, so it is important to study the extent to which the strategies from Section 3 can do so.  
We model interference as a zero-mean Gaussian broadcast independent of our system. Our system's nodes are informed of nothing more than the scales at which the interferer appears in our receivers' observations. This assumption prohibits using dirty paper coding [23] and other transmit-side interference mitigation strategies. With this model, the covariance matrix of noise terms associated with a particular interferer seen by the receivers is a matrix \(\boldsymbol{A}=\boldsymbol{a}\boldsymbol{a}^{\dagger}\), where \(\boldsymbol{a}\in\mathbb{C}^{N+1}\). In all trials run, a single interferer was assumed to be present at all nodes with power \(\alpha\) and random phase so that  
\[\boldsymbol{\Sigma}=\mathbf{I}_{N+1\times N+1}+\alpha\cdot\boldsymbol{a} \boldsymbol{a}^{\dagger}. \tag{38}\]  
with \(\boldsymbol{a}_{n}=e^{j2\pi\cdot\theta_{n}}\), \(\theta_{n}\sim\text{Unif}\left[0,1\right]\) where \(\text{Unif}\) is the uniform distribution.  
Figure 4 shows the difference in achievable rates with and without interference for different receive-side strategies. Even when a strong rank-one interferer is present at all the nodes, achievable communications rates are comparable to the case without an interferer.  
[PERSON] et al. [1] provided an example of a system such as the one in the present work where a Gaussian transmitter is significantly sub-optimal. In contrast, we see in Figure 4 that, over an average of many random channels in a range of settings, achievable rates using Gaussian signaling come quite close to an upper bound on the system. Even the relatively simple Gaussian distortion bound is close in performance to the optimum in all regimes but one with strong interference and little cumulative helper-to-base information. In this regime the LAN constraint limits the helper's ability to provide the base with the diversity of observations necessary to perform good interference mitigation.
## 7 Case Studies
### Analysis of the Impact of BESS Capacity on Cooperative Dispatch  
In order to analyze the impact of BESS capacity on cooperative dispatch, the following four cases are set up for comparison.  
Case 2-1: Reduce the capacity of the energy storage in Section 7.1 by 50%.  
Case 2-2: The capacity of the energy storage in Section 7.1 is unchanged.  
Case 2-3: Increase the capacity of the energy storage in Section 7.1 by 50%.  
Case 2-4: Increase the capacity of the energy storage in Section 7.1 by 100%.  
Table 7 shows the comparison of system costs for the four cases. Table 8 shows the comparison of curtailed energy for PV and WT of the four cases. Figure 9 shows the trend in total system cost and curtailed energy for PV and WT of the four cases.  
As seen in Table 7, compared to Case 2-2, the operation cost of DN in Case 2-1 is increased by 3.9%, the operation cost of DHN is increased by 3.7%, and the profitability of BESS is reduced by 17.1%, which ultimately results in an increase of the total system cost by 3.9%. This is because when the BESS capacity is reduced, the regulation capacity it can provide to the DN is weakened, and therefore the absorption capacity of DN for PV and WT is reduced. As shown in Table 8, the curtailed energy for PV and WT of Case 2-1 is increased by 3.2% compared to that of Case 2-2. As seen in Table 7, compared to Case 2-2, the operation costs of DN of Case 2-3 and Case 2-4 are reduced by 0.32% and 0.42%, respectively; the operation costs of DHN of Case 2-3 and Case 2-4 are reduced by 0.73% and 0.80%, respectively; and the profitability of BESS of Case 2-3 and Case 2-4 is increased by 95.2% and 106.7%, respectively, which ultimately results in a reduction of the total system cost by 0.95% and 1.1%, respectively. This is because when the BESS capacity is increased, the regulation capacity it can provide to the DN is increased, and therefore the absorption capacity of DN for PV and WT is increased. As shown in Table 8, the curtailed energy for PV and WT of Cases 2-3 and 2-4 is reduced by 3.3% and 4.2% compared to that of Case 2-2.  
In addition, as seen in Figure 9, with the increase of BESS capacity, the reduction of the total system cost and the curtailed energy for PV and WT gradually become slower. Therefore, increasing the BESS capacity within a certain range can reduce the total system cost and improve the dispatch economy.
## 6 Simulation
**Null Hypothesis (H0):** The average network regret of the D-UCB algorithm is not significantly different from the average network regret of the MAB algorithm. **Alternative Hypothesis (H1):** The average network regret of the D-UCB algorithm is significantly lower than the average network regret of the MAB algorithm. The next steps for hypothesis testing are as follows. **Data Collection:**  
We gather the network cumulative regret data for both the D-UCB and MAB algorithms from the simulation outcomes over 500 times. **Selection of significance level (a):**  
We select a significance level \(\alpha=0.05\) to determine the threshold for statistical significance. **Selection of the Test Statistic:**  
We choose the \(t\)-test statistic to compare means. **Data Analysis:**  
We calculate the test statistic using the collected data. The result is shown in Table 2, where the variable n represents the experiment number, which is the sample size (number of data points); sd is the standard deviation; skew is skewness; se is the standard error. The calculated T-statistic = \(-22.029\), \(p\)-value = \(9.66\times 10^{-40}\). **Making a Decision:**  
Comparing the test statistic with the critical value(s), it can be decided to reject the null hypothesis. We conclude that there is evidence that the D-UCB algorithm has significantly lower average network regret than the MAB algorithm. Similarly, for the regret of MAB vs. D-UCB with 40 arms, the result is shown in Table 3, The calculated T-statistic = \(-20.953\), \(p\)-value = \(5.611\times 10^{-38}\). Figure 5: (**a**) Average EE compared with different methods. (**b**) Average SE compared with different methods. (3) Online Learning Performance  
In Figure 6, the learning dynamics of energy efficiency (EE) and spectrum efficiency (SE) concerning the maximum power (\(P(t)\)) are illustrated. The results demonstrate an increasing trend in both EE and SE as \(P(t)\) rises. Remarkably, the TD3 RL-based optimal resource allocation algorithm exhibits the capability to learn and converge towards the optimal solution within a finite time, even in the presence of a dynamic environment.
# A Highly Integrated Millimeter-Wave Circularly Polarized Wide-Angle Scanning Antenna Unit
## 1 Introduction
The entire antenna unit directly integrates the filter and matching load, avoiding the use of corresponding independent components and the entire antenna array can be manufactured using simple and cost-effective PCB (Printed Circuit Board) technology.
# Characterisation of Dust Particles Deposited on Photovoltaic Panels in the United Arab Emirates
## 3 Results and Discussion
### Dust Particle Morphology (SEM Characterisation)
#### 3.3.5 Dust GT-10  
The SEM images presented in Figure 8 depict the characteristics of dust sample GT-10. It is noteworthy that all the observed particles are either small or medium sized. The images indicate that the dust particles exhibit variations in size and shape, with a predominantly regular distribution in shape and smooth surface texture. However, a small fraction of the particles has an irregular shape.  
The particles can be broadly categorised into oval, elliptical, square, and rectangular shapes, resembling stones with smooth surfaces. On closer examination at higher magnification, slight roughness is discernible. The particle dimensions are smaller than those of GS-10, and no cavities are observed on the surface or among the particles.  
The particle size distribution ranges from 20 to 100 \(\upmu\)m, with a Gaussian distribution centred around 50 \(\upmu\)m.  
Based on the findings of previous studies and the current investigation carried out, it is evident that the different sites in UAE harbour dust particles with unique characteristics and morphology. These features have significant impact on shading caused by the dust particles on the PV surface as well as the power output.  
The SEM analysis showed that the size and shape of the particles varied considerably among the sites in the range of 0.01-300 \(\upmu\)m, and the shapes were irregular with a high proportion having spherical, square and rectangular particles. Such variability in size and shape can lead to significant differences in the behaviour of dust on the PV modules, with some particles having a more profound shading impact and, consequently, the power output. In another study [12], SEM image analyses showed that the dust particles varied in the range of 10-50 \(\upmu\)m, and the smallest particle blocked more sunlight and ultimately reduced the efficiency of solar panels. Several factors could influence these characteristics such as local soil, traffic and industrial pollution, construction around sites, wind speed and relative humidity.  
Figure 7: SEM images of dust sample GS-10 at three different magnifications.
# Paneth Cells and Lgr5+ Intestinal Stem Cells in Radiation Enteritis
## 2 The Structure and Functions of the Small Intestine
### Other Derivatives of the ISCs  
The other IECs constitute less than 1% of the population of cells in the epithelium of the small intestine. These cells include enteroendocrine cells, tuft cells, M-cells, and cup cells [10; 17; 18; 46; 48; 84; 85]. Common to all the other cells except for the cup cells is that they are found closer to the villous side of the intestinal crypt where they can sample nutrients and gut microbiota and can de-differentiate and replace Lgr5+ ISCs. The average lifespan of all the other IECs is around 5 days but may be shortened following injury [48; 75]. Both the M-cells and tuft cells actively participate in innate defense in the gut [86]. The tuft cells have pathogen recognition receptors whereas the M-cells selectively allow bacteria and allogeneic particles to pass through them unprocessed (transcytosis) to underlying elements of the adaptive immunity [15; 49; 64]. Table 1 summarizes the estimated population density and some of the functions of the derivatives of the Lgr5+ ISCs.  
The proliferation and differentiation of the Lgr5+ ISCs is regulated by niche factors which are jointly produced by Paneth cells, other IECs, the cells that reside in the lamina propria adjacent to the base of the intestinal crypt, gut microbiota, and the enteric nervous system [11; 16; 42; 45; 58; 80]. The cells in the lamina propria which secrete the niche factors include macrophages, dendritic cells, lymphocytes, endothelial cells, fibroblasts, leucytes, and myofiboblasts [7; 49; 80; 87]. The niche factors determine the type of division which the Lgr5+ ISCs need to undergo, i.e., symmetrical versus asymmetrical based on the physiological status and the stress level. Similarly, differentiation of the Lgr5+ ISCs onto either the absorptive or secretory lineages depends on the strengths of among others Wnt and Notch ligand.