esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Potential Methodological Factors  
One likely explanation lies in the type of outcomes assessed. Prior studies primarily focused on isolated motor responses--such as strength, joint mobility, or spasticity--which may be more directly affected by peripheral stimulation. In contrast, our study evaluated motor skill performance, a more complex construct that integrates sensory, motor, and cognitive processes [23; 26]. This added complexity may reduce sensitivity to short-term neuromodulatory effects from a single rPMS session.  
While rPMS has been shown to increase corticomotor excitability, this physiological response does not always result in improved behavioral outcomes, especially in tasks requiring multi-joint coordination, visuomotor processing, and cognitive engagement [27; 28]. The translation of increased excitability into functional performance may depend on the intensity, frequency, and repetition of stimulation sessions, as well as the timing of subsequent task practice [29; 30]. It is also possible that isolated gains in neural excitability are insufficient to drive measurable behavioral change without concurrent task-specific training or consolidation over time. This distinction may explain why protocols that produce immediate changes in simple motor outputs (e.g., muscle activation or strength) may not be adequate for enhancing more complex motor behaviors in a single session.  
Stimulation parameters may also have played a role. Although we followed established protocols [15; 16], using 20 Hz frequency and 10% above MT, other studies have reported stronger effects with higher intensities. For instance, stimulation at 20-50% above MT has been shown to increase corticomotor excitability in neurotypical adults [17; 31]. The relatively conservative stimulation dose used here may have been insufficient to elicit measurable effects. Likewise, a 15-min session may not be adequate to influence integrated motor behaviors. Future studies should explore whether increased intensity, longer duration, or patterned protocols yield more robust outcomes.
# Federated Learning-Based Analysis of Human Sentiments and Physical Activities in Natural Disasters
## 2 Related Work
### Federated Learning (FL)  
The recent research on FL primarily emphasized the challenge of optimizing a global model with non-IID (non-independent and non-identically distributed), imbalanced, and widely dispersed data, and ensuring privacy and communication performance [1]. In an FL context, [PERSON] et al. [2] proposed FedAvg, a global optimization approach that efficiently combines the parameters of local models to cope with unbalanced and non-IID data forms. To reduce the number of communication rounds, the framework selects a subset of clients rather than all participants in each iteration. One of the key shortcomings of FedAvg is its inability to handle varied data. [PERSON] et al. [3] presented FedProx, a revamped version of FedAvg that guarantees convergence in heterogeneous networks to handle diverse data sources in the FL context. To overcome this problem, one proximate factor was subsequently introduced into the optimization problems of the model to account for the heterogeneity caused by incomplete data. MOCHA was presented by [PERSON] et al. [4] and has shown promising results in coping with statistical hurdles associated with FL by using multitasking, instead of training a single global model, as is the case with existing approaches, multiple global models were trained for every node. A major part of the literature is concerned with the integrity of model updates. There are two types of privacy in FL: local and global [5]. The first aims to safeguard the local model parameters, whereas the second preserves the parameter privacy of global models.  
[PERSON] et al. [6] proposed a safe multiparty computation framework to protect the model on node upgrades from the aggregator model, whereas the aggregator upgrades with the final parameter to node models. [PERSON] et al. [7] introduced a differential privacy approach to FL to safeguard model updates.  
For training, in contrast to conventional ML methods, FL does not require the transmission of data from client nodes to a centralized server. Local learning models were implemented on end devices, and their parameters were transferred to the edge/cloud server for global model aggregation through edge communication. After aggregation, the global model parameters were then transferred to the end devices [8]. The implementation of FL is fraught with difficulties, including the need for a significant amount of computational power and backup energy to compute a local training model. Training a local model to enhance learning accuracy requires a large volume of data [9]. However, for computationally restricted devices with limited datasets, it is challenging to develop local models with enhanced accuracy. We suggest that this is an interesting research subject that should be pursued to enhance global model efficiency.
# Hub-and-Spoke Network Design Considering Congestion and Flow-Based Cost Function
## 3 Mathematical Formulation of the Model
### Formal Definition and Modeling Assumptions  
We can formally define an HSLTP as follows. Let G = (N, A) be a connected directed graph, where N = {1, 2,..., n} represents the set of nodes and A represents the set of arcs.  
**Parameters:** For each \(i\in N,D_{i}\geq 0\) represents the cost of having a domestic facility, and \(F_{i}\geq 0\) denotes the cost of upgrading to a hub facility. In our model, we use \(P_{ij}\) to demonstrate the potential demand of flow from i to j and \(C_{ij}\) to represent the unit transportation cost of passing hub link i-j. The cost of establishing a hub link of k-l is shown as \(l_{kl}\), while \(U_{mnij}\) is the utility value of transporting the demand from m to n, through i to j, which is zero (0) if there is no (or not close enough) facility in i and j. Then, \(0<\alpha<1\) is a discount factor of using the hub link. The assigned capital for facility establishment is also expressed as \"Budget\", while B represents the unit lost demand cost.  
**Decision variables:**\(s_{i}\) is a binary decision variable and takes 1 if we decide to locate a domestic facility at point \"i\", and similar to that is \(h_{k}\): a binary decision variable that takes 1 if we decide to upgrade a hub facility at point \"k\". The binary decision variable \(y_{kl}\) states whether we decide to establish a link between hub nodes k and l (1) or not (0). The variables \(x_{ijkl}=1,i\
eq j,k\
eq l\), if the optimal path from i to j passes through the hubs k and l, but 0 otherwise; \(a_{ijk}=1,i\
eq j,k\
eq i,j\) if in the optimal path from i to j, and node i (non-hub) is directly connected to hub k, but 0 otherwise. \(b_{ijk}=1,i\
eq j,k\
eq i,j\) if in the optimal path from i to j, and node k(hub) is directly connected to node j (j is non-hub), but 0 otherwise. \(e_{ij}=1,i\
eq j\) if the optimal path from i to j passes through the (i, j) link in which either one or none of i and j is a non-hub. Also, \(w_{ij}\) denotes the flow from i to j (this is obtained by solving the attractiveness function of facilities). Just as a note, i, j, k, l, m, n, o, d can be used interchangeably.
# Sea Ice Detection Based on Differential Delay-Doppler Maps from UK TechDemoSat-1
## 3 Experimental Results
### Overall Experimental Results
As shown in Figure 13a, the change of sea ice coverage mainly happened in the area of A, B and C, which are marked with blue rectangles. Sea ice in these areas can be recognized as young ice or first-year ice which includes both closed ice and open ice, while sea ice in area D marked with magenta rectangle can be regarded as multi-year ice (only include closed ice) [48]. The results show that the sea ice detection probability of closed ice and open ice in the area of A, B and C is 100% and 97.56%, respectively, while the sea ice detection probability of D (multi-year ice) is 98.74%. Generally speaking, first-year ice is relatively smoother [49] and DDMs collected over first-year ice show less spreading, which benefits for sea ice detection. The surface of multi-year ice may be rougher, likely increasing false detection probability. Although the proposed methods hold great potential for monitoring sea ice, a small quantity of false detection still occurs around the open drift ice and multi-year ice areas. The received signals reflected from both sea ice and water may lead to false detection. Thus, more in-depth research into the relationship between reflected signals and sea ice concentration in needed in the future. Having a good knowledge about sea ice concentration will benefit sea ice detection a lot.
## 4 Numerical Simulations  
The finite element method (FEM) stands as a robust numerical approach widely employed to simulate intricate physical phenomena. COMSOL Multiphysics software harnesses the capabilities of the FEM to tackle a diverse array of multiphysics challenges [49]. Within the COMSOL framework, the FEM partitions a complex simulation domain into smaller, more manageable elements, facilitating the precise representation of complex geometries and material properties. These elements are interconnected at nodes, forming a mesh that discretizes the entire domain.
# Blind Recognition of Forward Error Correction Codes Based on a Depth Distribution Algorithm
###### Abstract  
Forward error correction codes (FEC) are one of the vital sections of modern communication systems; therefore, recognition of the coding type is an important issue in non-cooperative communication. At present, the recognition of FEC codes is mainly concentrated in the field of semi-blind identification with known types of codes. However, based on information asymmetry, the receiver cannot know the types of channel coding previously used in non-cooperative systems such as cognitive radio and remote sensing of communication. Therefore, it is important to recognize the error-correcting encoding type with no prior information. Although the traditional algorithm can also recognize the type of codes, it is only applicable to the case without errors, and its practicability is poor. In the paper, we propose a new method to identify the types of FEC codes based on depth distribution in non-cooperative communication. The proposed algorithm can effectively recognize linear block codes, convolutional codes, and Turbo codes under a low error probability level, and has a higher robustness to noise transmission environment. In addition, an improved matrix estimation algorithm based on Gaussian elimination was adopted in this paper, which effectively improves the parameter identification in a noisy environment. Finally, we used a general framework to unify all the reconstruction algorithms to simplify the complexity of the algorithm. The simulation results show that, compared with the traditional algorithm based on matrix rank, the proposed algorithm has a better anti-interference performance. The method proposed is simple and convenient for engineering and practical applications.  
d 202111 June 20211 June 20211 June 20211 June 20211 June 20211
###### Abstract  
Gamma-ray bursts (GRBs) are intense and short-lived cosmic explosions. Miniaturised CubeSat-compatible instruments for the study of GRBs are being developed to help bridge the gap in large missions and assist in achieving full sky coverage. CubeSats are small, compact satellites conforming to a design standard and have transformed the space industry. They are relatively low-cost and are developed on fast timescales, which has provided unparalleled access to space. This paper focuses on GMOD, the gamma-ray module, onboard the 2U CubeSat EIRSAT-1, launched on December 1 st 2023. GMOD is a scintillation-based instrument with a cerium bromide crystal coupled to an array of sixteen silicon photomultipliers, designed for the detection of GRBs. The characterisation of GMOD in the spacecraft, along with the validation of an updated spacecraft MEGAlib model is presented and this approach can be followed by other CubeSats with similar science goals. The energy resolution of the flight model is 7.07% at 662 keV and the effective area peaks in the tens to hundreds of keV, making it a suitable instrument for the detection of GRBs. An investigation into the instrument's angular response is also detailed. The results from this characterisation campaign are a benchmark for the instrument's performance pre-launch and will be used to compare with the detector's performance in orbit.  
CubeSat; gamma-ray instrumentation; gamma-ray bursts; characterisation; EIRSAT-1 Article  
11 June 2024  
19 July 2024  
15 July 2024
# Wavefront Shaping and Its Application to Enhance Photoacoustic Imaging
## 2 Exploiting Scattering of Light  
### Light Scattering in Complex Media and the Memory Effect  
In complex media, such as biological tissue samples, spatial inhomogeneities of the wavelength-scale refractive index cause light to be scattered once for every \(\sim\)100 \(\upmu\)m along its optical path. In general, such multiple-scattering feature, according to [19; 20], can be described with specific parameters, such as'mean free path' (MFP) and 'transport mean free path' (TMFP): the former represents the average distance between two consecutive scattering events, and the latter the effective MFP for forward-propagating light portion. Mathematically:  
\[l_{MPF}=1/\mu_{s} \tag{1}\]  
\[l_{TMFP}=1/[\mu_{s}(1-\cos<\theta>)] \tag{2}\]  
where \(\mu_{s}\) is the scattering coefficient of the medium, and \(\theta\) is the deflection of angle of the scattered light. Typically, the TMFP of most biological tissues varies from 0.1 to 1 mm. Note that the TMFP of biological tissues is wavelength dependent. Some near-infrared (NIR) light can reach a depth of up to several millimeters [15]; visible light that is widely used in many biomedical techniques only has a shallow penetration depth of hundreds of micrometers inside biological tissue, while a good resolution is sought. Therefore, traditionally scattering events have been regarded as an obstacle, and the speckle patterns generated by the coherent light propagation in tissue has been seen as one of the major noise sources in biomedical optical imaging [77; 78]. However, recently, pioneering scientists have begun to explore the feasibility of exploiting scattering and the corresponding speckle patterns based on their deterministic feature within the medium's temporal correlation window [40; 63; 64; 65].
###### Abstract  
Periprosthetic osteolysis is a common complication following total ankle arthroplasty (TAA). However, understanding of osteolysis volume and distribution is still evolving, undermining efforts to reduce the incidence of osteolysis via bone remodeling. We obtained data on the characteristics of osteolysis developing within the distal tibia and talus after TAA. Three-dimensional computed tomography (3D-CT) reconstructions of 12 patients who underwent HINTEGRA TAA were performed. We identified 27 volumes of interest (VOIs) in the tibia and talus and used statistical methods to identify the characteristics of osteolysis in the VOIs. The osteolysis volume was significantly larger in the talus than in the tibia (162.1 \(\pm\) 13.6 and 54.9 \(\pm\) 6.1 mm\({}^{3}\), respectively, \(p\) = 0.00). The extent of osteolysis within the peri-prosthetic region was greater than within other regions (\(p\) < 0.05). Particularly, in the talus, the region around the talar pegs exhibited 24.2 \(\pm\) 4.5% more osteolysis than any other talar region (\(p\) = 0.00). Our results may suggest that extensive osteolysis within the peri-prosthetic region reflects changes in stress flow and distribution, which vary according to the design and placement of the fixation components. This is the first study to report 3D osteolysis patterns after TAA. Careful planning of TAA design improvements may reduce the incidence of osteolysis. Our results will facilitate the further development of TAA systems.  
periprosthetic bone cysts; total ankle arthroplasty; computed tomography; osteolysis incidence
## 2 Methods
### SAR Interferometric Coherence
* Different atmospheric conditions \(\gamma_{am}\), _i.e._, delays caused by different water vapor contents at both acquisition times of the SAR images may cause artifacts in the interferogram [22, 82]. * Temporal de-correlation \(\gamma_{temp}\) is caused by changes on the ground in the time between the SAR acquisition dates [22, 62, 74]. The Earth's surface changes by frost and dew cycles, snow and ice cover or melting, respectively. Areas bare of vegetation such as urban areas and rocks have high coherence values even between SAR image pairs separated by three to four years [56, 60, 80, 83], whereas areas covered by vegetation, especially forests [33, 79, 81], lose coherence within a few days, especially due to wind and in the long-term also due to plant growth [36, 86, 87, 88, 89, 90]. Therefore, coherence is also a very good indicator for separating urban and non-urban areas in land cover mapping [79, 83, 84] (regarding the temporal baseline: _cf._ also Section 4).
## 4 Simulation Results
### Photon Propagation Distance  
From the magnitude response and BER results for different water types, various apertures, and FoVs, it is clear that as the transmission distance increases, the overall performance and response of the system deteriorates. We have shown that such behavior of different underwater channels is mainly due to different levels of scattering, which upsurges with the increase in water turbidity. From Figure 11, it is clear that with the increase in scattering events the average distance traveled by a photon also increases as the direction vector changes. Consequently, for each scattering event, the average propagation time also increases. Moreover, the figure also shows the actual number of scattering events for the three water types with the highest proportions in harbor waters. These results have been calculated for the same range of distance over which the BER has been calculated. Thus, the overall results suggest that the increase in separation distance between devices contribute to the increase in scattering events and average photon travel distance.  
Figure 11: Average distance traveled by photon versus no. of scattering events.  
Figure 10: Effect of increasing transmission distance and aperture on the BER performance of harbor, ocean, and coastal waters for 180\({}^{\circ}\) receiver FoV.
## 2 Various Types of Flexible Sensors (Temperature, Pressure, Humidity and Chemical)
### Temperature Sensors
#### 2.4.2 Related Work  
In a report published in 2020, a skin conformable GO/PEDOT: PSS based temperature sensor that can sense a temperature range of 25-100\({}^{\circ}\) was fabricated [38]. The sensor was subjected to 100 bending cycles and its sensitivity remained excellent. The sensor was attached to a robotic arm and used to control the robot as per temperature changes. This demonstrates the real-life application of sensors and their importance in robotics and electronic skins. Humidity interference is a major concern in temperature sensors and researchers are looking for ways to tackle this.  
A textile temperature sensor that can constantly measure body temperatures was fabricated by [PERSON] et al. [39]. The sensor was fabricated on an industrial scale flat-bed knitting machine whereby a metal-based sensing layer was embedded into the knitted layers, as shown in Figure 4. The working mechanism of the sensor is that the metal layer changes its resistance in response to temperature changes. Various metal wires were tested and modelled including nickel, copper and tungsten, with the most promising among these being nickel and tungsten owing to their high availability, sensitivity and high reference resistance. Another textile temperature sensor based on graphene-coated polypropylene textile fibres was reported [40]. The unique selling points of this sensor was its low voltage requirements (1 V) and its washability. The sensor was tested for washability by placing it in a glass beaker filled with water at various temperatures (30-50\({}^{\circ}\)C) with detergents of various types and spinning at various speeds (400, 800 and 1000 rpm) for an hour. SEM was used for morphological analysis. The sensor was subjected to 1000 bending tests at a bending radius of 5 mm and survived the mechanical deformation without significant decrease in sensing ability. Another study by [PERSON] et al. used inkjet printing to develop a flexible resistive-based temperature sensor based on silver ink on Kapton substrate [41]. The sensor can sense temperatures between 20-60 \({}^{\circ}\)C with a voltage range of 0-1 V. This temperature range makes the sensor a good candidate for human temperature sensing.
## 2 System Model and Problem Formulation
Moreover, the rate \(R_{0}\) is composed of the rates to transmit each \(W_{k,c}\) and can be expressed as \(R_{0}=\sum\limits_{k=1}^{K}\alpha_{k}\), where \(\alpha_{k}\) is the rate to transmit the common part of the \(k\)th message, \(W_{k,c}\). The energy harvested at the EH module of the \(k\)th PS user can be expressed as follows:  
\[\mathrm{EH}_{k}^{PS}=\varsigma_{k}^{PS}(1-\theta_{k})\left(\sum\limits_{i=0}^ {K}\left|\mathbf{q}_{k,PS}^{H}\mathbf{p}_{i}\right|^{2}+\sum\limits_{g=1}^{G} \left|\mathbf{h}_{k,PS}^{H}\mathbf{e}_{g}\right|^{2}+\sigma_{k}^{2}\right), \forall k, \tag{3}\]  
where \(\varsigma_{k}^{PS}\) is the EH efficiency at the \(k\)th PS user. Moreover, at the \(g\)th EH user, the harvested energy can be expressed as  
\[\mathrm{EH}_{g}^{EH}=\varsigma_{g}^{EH}\left(\sum\limits_{i=0}^{K}\left| \mathbf{q}_{g,EH}^{H}\mathbf{p}_{i}\right|^{2}+\sum\limits_{j=1}^{G}\left| \mathbf{h}_{g,EH}^{H}\mathbf{e}_{j}\right|^{2}\right),\forall g, \tag{4}\]  
where \(\varsigma_{g}^{EH}\) represents the EH efficiency at the \(g\)th EH user, and \(\mathbf{h}_{g,EH}\in\mathbb{C}^{N\times 1}\) and \(\mathbf{q}_{g,EH}\in\mathbb{C}^{M\times 1}\) are the channel vectors from the PB and BS to the \(g\)th EH user, respectively. Figure 2: Proposed RSMA transmission scheme. We seek to optimize the beamforming vectors, \(\mathbf{p}_{0}\), \(\left\{\mathbf{p}_{k},\mathbf{e}_{g}\right\}\); the common rate variables, \(\alpha_{k}\); and the PS ratios, \(\theta_{k}\), which together achieve a minimum rate requirement, and at the same time, they can harvest a required minimum EH for future use. The minimization of the sum transmission power of the PB and BS can be formulated as follows:  
\[\min_{\mathbf{p}_{0}\left\{\mathbf{p}_{k}\alpha_{k}\theta_{k} \mathbf{e}_{g}\right\}} \sum_{i=0}^{K}\left\|\mathbf{p}_{i}\right\|^{2}+\sum_{g=1}^{G} \left\|\mathbf{e}_{g}\right\|^{2}\] (5a) s.t. \[\alpha_{k}+R_{k}\geq\chi_{k},\,\forall k \tag{5b}\] \[\sum_{i=1}^{K}\alpha_{i}\leq R_{k,0},\,\forall k\] (5c) Figure 1:0 (5d) Figure 1:1 (5e) Figure 1:2 (5f) Figure 1:3  
Constraint (5b) ensures that the \(k\)th PS user achieves a minimum rate requirement, denoted as \(\chi_{k}\). Constraint (5c) is set to guarantee that the common stream can be decoded by all PS users. Constraints (5d) and (5e) ensure that each \(k\)th PS user and \(g\)th EH user can harvest a minimum EH requirement, denoted as \(\epsilon_{k}^{PS}\) and \(\epsilon_{g}^{EH}\), respectively. Solving the power minimization problem (5) is challenging because of its non-convex constraints (5b)-(5e). In the following, a near-optimal solution is proposed based on DL techniques and the SDR method.