esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Characterizations of the GN/PSt Composites  
During HIPE, water was dispersed in an organic continuous phase consisting of St and DVB, forming a water-in-oil emulsion stabilized by the lipophilic surfactant, Span 80, co-Pickering-surfactant and GN nanosheets. The organic phase, St and DVB, was then polymerized to form the skeleton of the porous material [48], and the water phase was removed from the pore cavities to give the GN/PSt porous materials with high porosities and open pore structures (Figure 2). As seen in the SEM images in Figure 2a, the GN/PSt composites contained interconnected polymer walls that formed large spherical pores (Figure 2a), and many smaller pores were present within the polymer skeleton (Figure 2b) [49]. GN randomly penetrated into the matrix of the porous materials, resulting in a rougher microstructure and lower surface energy (Figure 2c-f) [50]. Similar interconnected porous structures were seen in the GN\({}_{0}\)/PSt, GN\({}_{2}\)/PSt and GN\({}_{4}\)/PSt composites, which should be advantageous for the better transfer of substances through the porous materials when the monoliths are used to separate oil/water mixtures. Meanwhile, a more closed-cell structure was seen in the GN/PSt composites prepared with 8% and 10% GN (Figure 2g-j) because the GN agglomerated and was harder to disperse at these high concentrations and such a closed cell structure is not conducive to the transfer of matter through the materials.  
Moreover, the Raman spectra of GN and the GN4/PSt composite are shown in Figure S1. Two obvious peaks corresponding to D and G bands appeared at approximately 1358 cm\({}^{-1}\) and 1586 cm\({}^{-1}\), respectively. The G band was generally associated with the E\({}_{26}\) phonon of the C sp\({}^{2}\) atom, while the D band arose from the activation of the first-order scattering process of the sp\({}^{3}\) carbon atom in graphene sheets [51]. In the GN4/PSt composite, the same characteristic peaks as GN clearly appeared. This result demonstrated that the composite was successfully prepared. Furthermore, the intensity ratio of D bands to G bands (ID/IG) is usually adopted to evaluate the defects of graphene sheets. The ID/IG of GN used in this experiment was approximately 0.1071, and it increased substantially to 0.1897 for the GN4/PSt composite. The higher ID/IG of GN4/PSt resulted from a decrease in the crystalline sp\({}^{2}\) domains of GN, which may be caused by the intercalation of monomer polymerization in the GN layers [52].  
Figure 2: SEM images of the GN\({}_{0}\)/PSt (**a**,**b**), GN\({}_{2}\)/PSt (**c**,**d**), GN\({}_{4}\)/PSt (**e**,**f**), GN\({}_{8}\)/PSt (**g**,**h**) and GN\({}_{10}\)/PSt (**i**,**j**) composites.
## 4 Precoding Schemes
### Precoding Schemes for VLC
#### 4.2.2 Minimum Mean Squared Error (Mmse)  
Although ZF precoding completely cancels the interference, it may provide a low performance, especially at low SNR, since the resulting channel gains do not consider criteria such as fairness of BER. At this point, the MMSE precoding minimizes the error between the received and legitimate transmitted signal, which results in minimizing the BER. Assuming CoMP among optical transmitters, MMSE precoding in a MU-MISO VLC system subject to the constraints given by the optical channel is proposed in [118]. Moreover, this work considers both perfect and uncertain CSI given by the mobility of the users. In [163], MMSE precoding is derived considering the optical and power constraints for maintaining stable brightness. Instead of using the traditional pseudo-inverse methodology, the precoding vectors are determined by solving a convex optimization problem [164]. It is shown that MMSE precoding outperforms ZF precoding when considering the optical constraints. Focusing on a single-user MIMO optical link, MMSE precoding applyingsingular value decomposition at the transmitters and maximum likelihood detection on the receiver side is analyzed in [165]. After that, an iterative method for calculating the MMSE precoder is derived. This iterative approach is a practical solution assuming a static scenario, since the complexity of this algorithm requires one to converge to a practical solution. Interestingly, this work shows that the MMSE precoding provides a poor BER when the illumination requirements are close to the lower or upper bound of the dynamic range of the optical transmitters. In this case, these techniques can be classified as multi footprint.
# Design and Implementation of Adam: A Humanoid Robotic Head with Social Interaction Capabilities
## 1 Introduction  
Different shapes, sizes, and capabilities have been associated with robotic platforms used in social interactions. In a large part of the conducted research, robots have been humanoid [1, 2, 3, 4, 5] but other works used animal- or toy-like robots [6, 7, 8] or robots that are not meant to look like human-familiar shapes [9, 10]. A wide array of applications has emerged [11, 12]; these applications include telepresence [13, 14, 15], education [16, 17, 18, 19], care, assistance [20, 21], reception [22], and children's companionship [23, 24]. The use of robots in these social contexts has benefited enormously from advancements in research across several domains, such as computer vision [25, 26], natural language processing, conversational systems [21, 27], and expression and gesture recognition and generation [28, 29].  
From a hardware point of view, additive manufacturing has facilitated the design, implementation, and modification of robotic platforms due to the flexibility and customizability that it enables [30]. In addition, the advances in actuator manufacturing have allowed us to perform precise actions in social robots like displaying biologically inspired motions using motors of different types and muscle-like actuators [31, 32].  
In this paper, the design and implementation of the Adam platform are detailed. This platform consists of a humanoid robotic head that can perform human head-like motions and display expressions due to its 7 degrees of freedom. This platform is also equipped with sound acquisition, processing, and generation capabilities, used in conjunction with ChatGPT to engage in conversations. This 3D-printed platform has been designed to be used in social interaction contexts, like in education and visitor reception. It is easilyreproducible and modifiable, allowing for improvements in design or the addition of capabilities. The design is planned for initial mounting on a human-like upper body, with the aim of subsequently adding other body parts to develop a multipurpose modular humanoid robot.  
This paper is organized as follows. Section 2 shows recent work that has been conducted in the domain of human-humanoid robot interaction. Section 3 shows the mechanical, aspects of the robot design and implementation. Section 4 shows the electronics and interaction control of the robot. Section 5 concludes the paper and shows work that will follow.
# Evolution of System Embedded Optical Interconnect in Sub-Top-of-Rack Data Center Systems
## 4 ThunderValley2--Second-Generation Optically Enabled Data Center System  
The ThunderValley2 platform was adapted from an existing 2U (89 mm) high, 19\({}^{\prime\prime}\) (482.6 mm) wide OneStor system enclosure (Figure 7a) and included two optically enabled 12G SAS switch controller modules and an electro-optical midplane with a full aggregate bandwidth capacity of 2.3 Tb/s. Additionally, it provides the option to optically plug 24 conventional 2.5\({}^{\prime\prime}\) disk drives to the midplane (Figure 7b). The system architecture is shown in Figure 7c. As with the LightningValley system, the ThunderValley2 system supports two controller daughters, but it also allows 24 bidirectional 6 Gb/s SAS channels to be conveyed optically from the expander chip of each controller daughtercard to each of the 24 2.5\({}^{\prime\prime}\) disk drives across a fiber-optic flexplane attached to the midplane. An optical interface card is present on each disk drive to provide the opto-electronic signal conversion.  
Figure 6: Insertion loss measurements of complete end-to-end link from BOA1 to BOA2.  
The ThunderValley2 platform was validated, with 6 Gb/s SAS data traffic successfully transmitted along the internal optical links, and has been described in previous work [5]. In this section we focus on the optical interconnect and interface technology and performance.
## 5 Experimental Results
### Non-Linear Load (Disturbance)  
A single-phase non-linear load is connected to the VC-VSC, consisting of a series resistor of 1 \(\Omega\) followed by a diode bridge with a 22 \(\Omega\) resistor and a 2.2 mF capacitor in parallel (based on IEC-62040-3 Annex E [38]. The load current total harmonic distortion (THD) is 101.5%. For this case, the controllers include a feedforward compensator.  
The results of the disturbance rejection are depicted in Figure 17. Due to the nature of the non-linear load proposed, the voltage control loop needs to respond fast every half  
\begin{table}
\begin{tabular}{l l l l l l} \hline
**Controller** & \(\mathbf{V_{150 Hz}}\) **(V/V)** & \(\mathbf{V_{250 Hz}}\) **(V/AV)** & \(\mathbf{V_{350 Hz}}\) **(V/V)** & \(\mathbf{V_{450 Hz}}\) **(V/V)** & \(\mathbf{V_{550 Hz}}\) **(V/V)** \\ \hline PR & 1.393 & 1.389 & 1.396 & 1.189 & 1.060 \\ PRHC & **1** & **1** & **1.270** & 1.673 & 2.254 \\ \(\alpha=0.75\) & 1.067 & 1.197 & 1.372 & 1.546 & 1.929 \\ \(\alpha=1.5\) & 1.128 & 1.278 & 1.322 & **1.213** & **1.004** \\ \hline \end{tabular}
\end{table}
Table 6: Multi-harmonic setpoint response for all four controllers.  
Figure 16: FFT for the tracking voltage time response in Figure 15.  
Figure 15: Tracking voltage time response. The orange line in the right figure depicts the voltage reference on the tracking error plot.  
period of the voltage fundamental component reference. This is reflected in Figure 17, where the PRHC controller and FPR with \(\alpha\) equal to 0.75 show the best time evolution.  
Table 7 provides specific values and highlights the best option for each component. The voltage tracking transient and the error response are depicted when the non-linear load is connected.  
The FFT for the disturbance and the voltage error are computed; see Figure 18. The FFTs show the FPR with \(\alpha=0.75\) starts to be better at high frequencies.
## 7 Evaluation
### Experimental Setup  
The results were collected on different farm machinery (Cultivator, Rotavator and Laser-leveler) in five different regions of Punjab (Bahawalpur, Layyah, Okara, Mandi Bahauddin and Lahore) of Pakistan. The field shapes were regular and irregular ranging in area from 0.28 to 4.8 hectares. All the studies were performed in dry conditions. The IoT modules were mounted on the Farm Implements collecting 10 samples per second of vibration, gyro, humidity, pressure and light sensors. The sensors data was sent to the android smartphone (Samsung Galaxy running android version 8 or higher) using Bluetooth. The smartphone was used in screen-on mode. The smartphone app accumulates the sensors data and add GPS latitudes and longitudes to it. The accumulated data from the android app was sent to the AWS (Amazon Web Services) cloud using Wi-Fi, where the AI algorithms were applied to them. The AI algorithms were run on a t2.micro EC2 Linux instance (Elastic Computer Cloud) of AWS. EC2 is a compute virtual server and AWS is the cloud web service of Amazon. The outcome of AI modules was stored in RDS (relational data store) and were forwarded to android app as well.
## 6 Discussion on the Results
### Practical Implementation Considerations  
In addition to its structural efficacy, the ACPH system, initially designed as a modular hinge for precast joints, provides many pragmatic benefits for actual construction and post-seismic rehabilitation. The fundamental elements of the system, including the energy-dissipating steel plates and bolted end-plate assemblies, are constructed from normal carbon steel (Q235 and Q345) and can be manufactured with regular machining techniques without the need for specialist materials or specific equipment. This simplicity improves the system's scalability and facilitates its incorporation into both precast production operations and in situ retrofit processes.  
The installation of the ACPH in precast frames may be integrated into current workflows by affixing the hinge assembly at the beam ends before lifting, using bolted or welded connections to link with the column face. In contrast to wet joints or grouted sleeves, the ACPH needs neither curing time nor precise grout injection, hence minimizing construction delays and reliance on on-site manpower. Subsequent to a seismic incident, the interchangeable steel plates may be accessible and replaced without invasive demolition. The device's accessible design enables post-earthquake examination, enabling maintenance staff to see assess deformation or yielding in the steel plates.  
From a long-term durability perspective, it is essential to consider protection against corrosion and fatigue, especially in external or industrial settings, as also emphasized in the PCI Design Handbook [1]. This may be mitigated by galvanization or protective coatings, as well as by integrating gaskets or sealants at the beam-column junction to avert moisture infiltration. The ACPH's mechanical simplicity, modular design, and replaceability provide considerable benefits for life-cycle cost and resilience-oriented asset management. The concept of replaceable yielding elements as a strategy for resilience and maintainability aligns with recommendations in FEMA P-58 [37], where component-level substitution is prioritized to minimize recovery time.
## 4 Discussion
Similarly, the ramp all-out test, though correlated with CP at a group level, displays wide limits of agreement, making W\({}^{\prime}\) estimates from this protocol unreliable for monitoring individual performance changes [23]. The time constant for oxygen uptake kinetics in active to well-trained adults generally falls within the range of 20 to 35 s [24]. A 90 s test duration allows for three or more VO2 time constants to pass, which is sufficient to achieve a near-constant provision of aerobic energy. In contrast, [PERSON] [9] (2007) required a longer-duration test to assess CP because of their mathematical approach of averaging power over 30 s intervals rather than the need for a longer test to fully mobilize aerobic energy production. The exponential approach appears equally applicable to both men and women (\(p\) = 0.28), and body size (height: \(p\) = 0.10, weight: \(p\) = 0.46) does not seem to influence the accuracy of CP parameter estimation. Whether the test involves the traditional three-minute duration or the shortened 90 s all-out exercise test, the maximum-effort nature of the protocol is inherently uncomfortable for most participants. Less than a handful of athletes in our sample agreed to perform the 3 MT on a separate occasion when asked, highlighting the aversion to repeated testing. However, in contrast, in a separate study [25], which required participants to perform an interval workout session followed immediately by a 2 min all-out exercise test, 31 participants completed more than three sessions each, with an average of 6.55 \(\pm\) 2.94 sessions per participant. This result demonstrates that the shorter-duration test protocol is not only physically and mentally more tolerable but also one that athletes are more willing to repeat. Therefore, the shorter-duration all-out test holds great promise as a feasible and effective fitness-monitoring tool for both amateur and elite athletes alike. The Critical Power Test comparison table (Table 1) illustrates the key differences between the traditional CP test, the 3 MT, and the newly proposed shortened test using the exponential fit. As shown, the shortened test offers comparable accuracy to the 3 MT but with significantly lower fatigue levels and greater ease of repetition. This makes the 90 s test a more practical option for athletes who need to assess their fitness regularly without the time commitment and exhaustion associated with the longer tests.
## 1 Introduction  
The main vision for 5G is not only to provide more synergistic, pervasive, and ubiquitous broadband access with a higher capacity and throughput at a more affordable cost, but also to entirely embrace new technological challenges that span far beyond, to transform new industries, enable new inventive technologies, and empower new types of user experiences [1]. However, one of the hurdles that always plagues the 5G rollout is the scarcity of radio resources, particularly spectral bandwidth, which become even more limited as 5G is anticipated to connect everyone and everything to facilitate the deployment of the massive Internet of Things (IoT) [2]. The conservative orthogonal multiple access (OMA) used in 4G has reached its spectral performance asymptote where any enhancement of bandwidth utilization beyond this point will not improve the performance to any further extent. Recently, the emergence of non-orthogonal multiple access (NOMA) has boosted the performance asymptote of spectral efficiency to another new height, making NOMA a viable and promising multiple access candidate for 5G [3].  
In principle, NOMA can concurrently support the transmissions of multiple users in a single resource block. In a downlink power-domain NOMA system, multiple signals destined to different users are multiplexed using superposition coding (SC) at the base station (BS) with different transmission power levels on a non-orthogonal basis. Usually, the stronger users (with higher channel gains) are allocated with lower power levels whereas the weaker users (with lower channel gains) can transmit with higher powers. With such a power allocation strategy, the stronger users can suppress the interfering signals using a successive interference cancellation (SIC) receiver, which first decodes the dominant interfering signals and subtracts them from the superposed signal. For weaker users, since the powers assigned to the stronger users are low, the interfering signals are negligible and can be treated as noise.
# Noncoherent Channel Combining for GNSS Signal Tracking with an Adaptive Antenna Array
## 5 Conclusions  
In this paper, the noncoherent channel combining method was proposed for GNSS signal tracking with an adaptive antenna array. The proposed method adopts a two-stage processing architecture, which is implemented as follows. First, interference suppression  
Figure 5: Code and carrier discriminator outputs. (**a**) Code discriminator. (**b**) Carrier discriminator.  
Figure 6: Code phase and carrier phase tracking precision under different \(C/N_{0}\) conditions. (**a**) Code phase. (**b**) Carrier phase.  
is performed based on the projection technique to suppress interference. Then, noncoherent channel combining is implemented at the correlation level to exploit the useful signal power. The proposed method was compared with several representative blind beamforming methods through both theoretical analysis and simulations. The analysis and results showed that the proposed method can achieve better signal tracking precision than conventional methods. The proposed method actually represents a unified processing architecture and can be further extended. Therefore, it deserves further investigation in future work: (1) More GNSS signal components can be taken into account to achieve joint reception with an adaptive antenna array. (2) Closed-loop sequential processing can be extended to open-loop batch processing for more robust signal tracking.  
Conceptualization, S.L. and F.W.; methodology, S.L.; software, S.L. and C.M.; validation, H.L. and C.M.; formal analysis, S.L.; investigation, S.L.; resources, H.L. and X.T.; data curation, S.L.; writing--original draft preparation, S.L.; writing--review and editing, X.T. and F.W.; supervision, F.W. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the National Natural Science Foundation of China (Grant No. U20A0193).  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflict of interest.
## 4 Case Study
### Negotiation Framework  
In this case study, there are four agents. Subsystems 1 and 4 have only one neighbour, Subsystems 2 and 3, respectively, because coupling only exists with them. On the other hand, Subsystems 2 and 3 have two neighbours each. Then, the low-level control layer FL-DMPC provides agent \(i\) the following control sequences \(U_{1}=\big{[}U_{1}^{f_{1}}(t)\big{]}\), \(U_{2}=\big{[}U_{2}^{f_{1}}(t),U_{2}^{f_{2}}(t)\big{]}\), \(U_{3}=\big{[}U_{3}^{f_{1}}(t),U_{3}^{f_{2}}(t)\big{]}\), and \(U_{4}=\big{[}U_{4}^{f_{1}}(t)\big{]}\), obtained by performing  
Figure 3: Schematic diagram of the eight-coupled tanks plant with the proposed subsystems.  
pairwise negotiation among agents. In particular, \(M_{1}=1,M_{2}=2,M_{3}=2\), and \(M_{4}=1\) in (Equation (1)).  
Due to the particular configuration of the eight coupled tanks system, negotiation is not required in the upper layer for Subsystems 1 and 4, and therefore \(U_{1}^{f}(t)=U_{1}^{f_{1}}\) and \(U_{4}^{f}(t)=U_{4}^{f_{1}}\). Negotiation is only needed for Agents 2 and 3, and the state is defined as:  
\[s_{t}\equiv[U_{2}(t),U_{3}(t)].\]  
The actions \(a_{t}\) provided by the deep neural network are:  
\[a_{t}\equiv[\phi_{2},\phi_{3}]=\zeta(s_{t};\theta)\]  
where \(\phi_{2}=\left[\alpha_{2}^{1}\right]\) and \(\phi_{3}=\left[\alpha_{3}^{1}\right]\). Note that only one weighting coefficient is obtained for each subsystem because the other ones are directly obtained from:  
\[\alpha_{2}^{2}=1-\alpha_{2}^{1};\alpha_{3}^{2}=1-\alpha_{3}^{1}\]  
Then,  
\[U_{2}^{f}(t)=U_{2}^{f_{1}}(t)\cdot\alpha_{2}^{1}+U_{2}^{f_{2}}(t)\cdot\alpha_{ 2}^{2} \tag{20}\]  
\[U_{3}^{f}(t)=U_{3}^{f_{1}}(t)\cdot\alpha_{3}^{1}+U_{3}^{f_{2}}(t)\cdot\alpha_{ 3}^{2} \tag{21}\]  
Therefore, considering that the final sequence for each agent is \(U_{i}^{f}=[u_{i}^{f}(t),u_{i}^{f}(t+1),\ldots,u_{i}^{f}(t+H_{p-1})]\), with \(H_{p}=5\) the prediction horizon for each local MPC controller and manipulated variable, \(q_{s}\), to be applied to the system is selected as the first value of the sequences, usually an MPC framework:  
\[q_{1}=u_{1}^{f}(1),q_{2}=u_{2}^{f}(1),q_{3}=u_{3}^{f}(1),q_{4}=u_{4}^{f}(1)\]  
Figure 4 displays the implemented PG-DMPC algorithm within the control architecture performed in the case study, where PG-DMPC receives local decisions from a FL-DMPC and gives global decisions according to the control objective.