esa-sceva/satcom-chunk-collection huggingface dataset:
# Two-Dimensional Human Pose Estimation with Deep Learning: A Review
## 4 Top-Performing Models on Benchmark Datasets
### Top-Performing Models on the COCO Test-Dev Dataset  
On the COCO test-dev dataset, ViTPose (ensemble) achieved an average precision (AP) of 81.1, setting a new state-of-the-art record on this benchmark [35]. ViTPose employs a pure, non-hierarchical Vision Transformer (ViT) as its backbone to extract high-dimensional feature representations from input images. Specifically, the input image is divided into fixed-size patches and processed by a standard ViT encoder for feature extraction. The backbone adopts a unified and streamlined design that eliminates convolutional modules and complex components, resulting in high scalability and architectural simplicity. In the decoding stage, ViTPose utilizes a lightweight head to transform transformer-derived features into keypoint heatmaps. This decoder is both structurally simple and computationally efficient, allowing compatibility with inputs of varying resolutions. This flexibility enables multi-task joint training and inference, improving both the practicality and scalability of the system.  
ViTPose's training strategy fully exploits the transferability of large-scale pretrained models. The Vision Transformer (ViT) backbone is initially pretrained on general-purpose datasets (e.g., ImageNet) and subsequently fine-tuned on task-specific datasets such as MS COCO, resulting in substantial improvements in keypoint detection accuracy. ViTPose also supports flexible input resolutions, where higher-resolution inputs generally lead to better performance. To reduce training costs, the framework allows for the partial freezing of transformer layers, fine-tuning only essential components. This enables efficient adaptation to resource-constrained environments without significant degradation in performance. As shown in Figure 11, the framework of ViTPose is depicted.  
The core innovation of ViTPose lies in its structurally minimalist yet highly effective design. By employing a purely transformer-based backbone, ViTPose achieves high-precision pose estimation without relying on convolutional layers or complex architectural modules. The framework is inherently scalable, allowing for flexible adjustments in network depth and width to accommodate diverse task requirements. ViTPose further improves generalization through joint training across multiple tasks and datasets. A particularly notable contribution is the introduction of a learnable knowledge token, which enables efficient knowledge transfer from large-scale to lightweight models. This significantly enhances the performance of compact models in real-world deployment scenarios. As shown in Table 4, the performance of the models on the COCO test-dev dataset is compared.  
Figure 11: **(a)** Framework of ViTPose. **(b)** Transformer block. **(c)** Classic decoder. **(d)** Simple decoder. **(e)** Decoders for multiple datasets [35].
# Hybrid Impedance-Admittance Control for Upper Limb Exoskeleton using Electromyography
## 4 Materials and Methods
### Calibration
#### 4.6.2 Control Coefficients  
We set \(k_{g}\) to 9.8 because it is the approximate gravity value. We carried out experiments to evaluate the angle reported for the robotic arm to reach and the servo motor's feedback using loads with different masses. \(k_{e}\) defines gain in the normalized electromyographic signal. We defined its value to 1.25 experimentally based on trial and error with visual feedback. It must be individually adjusted for each user, taking into account the user's strength (i.e., the necessary force that the user applies to move the load). \(k_{c}\) is the coefficient related to the interference with the environment and the change in position. At this point in the research, we defined its value to 0.10 based on trial and error to become more consistent with the values presented by the measuring equipment. The automatic definition of this coefficient is a work to be developed in future applications.
## 1 Introduction  
Hand movements are used to perform daily living activities. Unfortunately, for people suffering medical conditions (e.g., rheumatoid arthritis [1; 2], stroke [3], systemic sclerosis [4; 5]) or accidents, their hand functionality might have been compromised; hence, they might require traditional physical rehabilitation to restore their hand motor functions. According to [6], hand therapy seeks to increase the range of motion (ROM) and muscle strength, so that people might achieve optimal functioning. Specifically, ROM is \"the arc of motion that occurs at a joint or a series of joints\" [7].  
During traditional rehabilitation, people perform repetitive body movements according to physiotherapist advice and assistance, frequently with no visual feedback. Consequently, people might drop the rehabilitation process due to a lack of engagement and the exercises' monotonous nature. Serious games have been developed to assist people in their rehabilitation process. Serious games are defined as \"games that allow the player to achieve a specific purpose using the entertainment and engagement component provided by the experience of the game\" [8]. Specifically, reviews have been published on the use of games for upper limb rehabilitation [9; 10; 11; 12]. These games for upper limb rehabilitation have used sensors (e.g., cameras, electromyography sensors) to detect the body movements employed in physiotherapy; therefore, these movements can be used to control serious games. As mentioned previously, a key point during the rehabilitation process is the participants' engagement. According to [13], the engagement \"refers to sustained involvement with an activity\". In the context of videogames, there is a technique called \"Dynamic Difficulty Adjustment (DDA)\"; according to [14], DDA \"is a technique of automatic real-time adjustment of scenarios, parameters, and behaviors in video games, which follows the player's skill and keeps them from boredom (when the game is too easy) or frustration (when the game is too difficult)\". Based on this, engaging participants in the rehabilitation process could be facilitated by the use of games. Therefore, participants perform their rehabilitation exercises while playing the game and receiving rewards from the experience. Moreover, adjusting the game difficulty on a regular basis is necessary to keep players engaged. Reviews have been published regarding DDA on videogames [14; 15]. Nevertheless, few studies have implemented DDA in their serious games for upper limb rehabilitation. Additionally, the players' engagement can be measured using questionnaires or biosignals (i.e., electrical activity from the human body: EEG signals--electroencephalography signals: electrical activity from the brain; ECG--electrocardiography signals: electrical activity from the heart; electrodermal activity).
# Internet of Nano-Things, Things and Everything: Future Growth Trends
## 6 Challenges and Impediments to IoT
### Need for Secure Data Management and Processing Solutions  
IoT systems are highly diverse in terms of functionality and applications and are also heterogeneous in nature. IoT-based applications are thus almost limitless in nature, ranging from wearable devices to distributed sensor networks. With all the wide-ranging types of data being generated, a unified, efficient and secured data management and processing strategy must be adopted for IoT to be operationally successful.  
It is predicted that by 2020, 44 ZB [95] of data will be generated: 1 ZB is \(10^{21}\) bytes. This vast amount of data that is being generated by both humans and devices needs to be processed and stored efficiently. This will require new high-density storage devices and, potentially, quantum computing.  
The continuous generation of data, by interlinked IoT smart devices, needs to be controlled. The solution to this surfeit of data is yet to mature. Traditional big data, relational database management technologies and NoSQL (originally referring to \"non SQL\" or \"non-relational\") need to be scaled somehow as they are not adequate, if planetary IoT deployment is to be completely successful. Consequently, the present emphasis on just sensor IoT networks, need to be expanded.  
The nature of IoT data and its frequency of generation need to be taken into account. This will require new mathematical studies and models to be built. The type of IoT data is likely to be intermittent, massive, geographically dispersed and often streamed in real-time [96]. This will require a complete overhaul of the present network components that make up the Internet to keep latency and jitter to a minimum. Not only the actual data need to be stored, but also its associated metadata. The metadata typically would consist of object identifiers, time and location of the data, services rendered, and processes occurred.  
The nature of the IoT data will also vary as it traverses the network. It will travel through various types of flexible schema databases, fixed and mobile networks, concentration storage points etc. before reaching its destination via centralized data stores. This again highlights the criticality of successful management and processing of IoT data.  
The newer models of databases will have to be adapted for IoT data to process: the remote storage of data at the \"Things Layer\"; the structure-less data; its non-atomicity; its less rigid consistency; its lower isolation and its lesser durability. This will be necessary, particularly in the drive for data availability and energy efficiency [97]. Also, data management must take on a dual role of offline storage and online-offline operations due to the dynamic nature of IoT-generated data. IoT data that is being generated needs to be summarized online along with the metadata attached. Further, the power requirement for each stage of the data generation and processing cycle needs to be studied more closely to optimize IoT power usage and extend the device longevity.
## 6 Open Issues for Cooperative and Precoding Schemes in VLC
### User-Centric Approaches for Precoding Schemes  
The implementation of precoding schemes in VLC systems typically assumes full connectivity between the optical APs and users. However, in practical scenarios, only the signals from some optical APs are received at each user, due to the small and confined area of coverage provided by each optical AP. Moreover, the received signal may be subject to blocking effects. In this context, traditional clustering from a network-centric point of view is usually useless since it does not consider the distribution of the users [189]. Recently, the user-centric approach has been proposed as a means of achieving optimal clustering in VLC systems [35; 42]. Nevertheless, depending on the resulting clusters, the task of designing a transmission strategy based on either FR or precoding schemes is not straightforward [189]. Specifically, in [106], an alternative scheduling is derived for the use of ZF precoding under user-centric clustering. However, novel scheduling algorithms and precoding schemes are still required to exploit the paradigm introduced by the user-centric approaches in VLC systems.
## 4 Experiment
### Recall and Precision  
The confidence threshold is a critical parameter that directly affects the detection result. A lower threshold increases the probability of detecting the target and the false detection rate. In this experiment, the confidence threshold was sampled from 30% to 90% at 5% intervals to count instances of recall and precision when the distance between the detection result and the labeled sample was less than 30 pixels. As is shown in Figure 13, when the threshold is below 50%, the recall of the proposed method performs best; however, it declines slightly quicker than SBM as the threshold increases. Regarding precision, the proposed method is significantly better than SBM and NCC and is only slightly inferior to YOLOv5 in the threshold range of 30-75%.  
SBM has the highest recall because it only uses edge features, which are sparser than those of other methods. Although it dramatically improves recall, it sacrifices precision, and many false positives appear in the test. As can be seen from Table 2, the false positive rate (FPR) of the proposed method is lower than that of SBM and NCC for most of the range. YOLOv5s and YOLOv5x have lower FPR and TPR at the same time, which means they have less positive outputs.  
The F1-Score is a comprehensive evaluation metric that takes into account recall and precision, and in the proposed method, it is significantly better than in the other four methods, with a relatively significant drop occurring only when the threshold exceeds 90%.
## 3 Test Results and Discussion
### Binder Properties
#### 3.1.3 Chemical Composition  
Determining the chemical composition allows for estimating the pozzolanic oxide content (silicon dioxide (SiO\({}_{2}\)), aluminium oxide (Al\({}_{2}\)O\({}_{3}\)), and iron (III) oxide (Fe\({}_{2}\)O\({}_{3}\))) known to be responsible for the pozzolanic activity that yields hydrates responsible for strength [26]. According to ASTM C618 [28], their cumulative value should be more than 70%. It should be noted that the amorphous portion of this content (Section 3.1.4) is responsible for pozzolanicity. Table 4 shows the oxide composition of the clay samples and limestone. The pozzolanic oxide contents are 94.87%, 95.46%, and 94.7% for MFN, EXW, and BFN, respectively, indicating that the requirement is satisfied. In this case, Fe\({}_{2}\)O\({}_{3}\) contributes less than 1%. This high content confirms that calcining the clay yielded a sufficient amount of target metakaolin (Al\({}_{2}\)O\({}_{7}\)Si\({}_{2}\)). As noted earlier, the calcination of the kaolinite in the raw clay converts it to metakaolin. The loss on ignition (LOI) also satisfies the less than 10% requirement of ASTM C618 [28].  
Figure 4: TGA results of the raw clay samples.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Clay Sample** & **MFN** & **EXW** & **BFN** \\ \hline Kaolinite content & 29.5 & 41.6 & 32.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Determined kaolinite content of the raw clay samples.  
Comparing these values to those observed in the literature [3,6,27], these clay samples obtained in South Africa seem to be relatively higher in SiO\({}_{2}\) content, at \(\geq\)70% compared to an average of about 50% (clay samples from India, North/South America, South Asia) reported in the literature. Conversely, the Al\({}_{2}\)O\({}_{3}\) content seems lower, at about 17-23% compared to the 40% reported in the literature. This implies that the clay contains some 2:1 clay minerals and not just 1:1 structures (such as kaolin); that is, an octahedral alumina sheet is sandwiched between two tetrahedral silica sheets, contributing to more silica content. The mineral composition detailed in the next section confirms this. As noted earlier, due to varying geological settings and weathering conditions from one country or region to another, clay soil has varying mineral chemical signatures, and Al\({}_{2}\)O\({}_{3}\) is key to the hydrates and strength of LC\({}^{3}\) composites; it is suspected that the use of South African clay for cementitious composites may be lagging in performance. By implication, it can be said that the structural composition of the selected South African clay has more SiO\({}_{2}\) components than Al\({}_{2}\)O\({}_{3}\).
# An Interface Pressure Detection Method of Cable Silicone Rubber-XLPE Based on Nonlinear Ultrasound
## 3 Materials and Methods
### Comparison of Simulation and Experimental Results  
The theoretical values of the interfacial nonlinear coefficient were compared with the experimental values, as shown in Figure 17. As the pressure at the silicone rubber-XLPE interface increased, the theoretical and experimental values followed the same general trend. It can be seen that the experimental value of the nonlinear coefficient was smaller than the simulated value, which may be attributed to the fact that the pressure at the interface of silicone rubber-XLPE was smaller than the pressure value displayed by the pressure transducer, resulting in a smaller change in the fundamental and second harmonic amplitudes. The nonlinear coefficient showed approximately the same trend as the pressure variation when ultrasound was incident from the silicone rubber surface and the XLPE surface, respectively. This indicates that for contact interfaces made up of different materials, the surface from which the ultrasound is incident has less influence on the results of the interfacial nonlinear coefficient.
## 3 Materials and Methods
### Elemental Content Determination
#### 3.3.2 Analytical Determinations  
The determination of the amount of Ca, Mg, Cu, Ni, Fe, Mn, Na, K, and Zn in ALF and RCV seeds and sprouts samples was carried out by flame atomic absorption spectroscopy (F-AAS) NOVAA 300 model with Air-C\({}_{2}\)H\({}_{2}\) flame, while the determination of Se was done using a graphite furnace atomic absorption spectrophotometer (GF-AAS) Model Zeenit 650 equipped with Zeeman background correction facility. As the radiation sources, hallow cathode lamps of the different metals were used. The analytical measurements were based on time-averaged absorbance. Resonance lines at 422.7, 285.2, 324.8, 232.0, 248.3, 279.5, 589.0, 766.5, and 213.9 nm were employed for Ca, Mg, Cu, Ni, Fe, Mn, Na, K, and Zn. Lamp intensity (4-6 mA) and band pass (0.2-0.5 nm) were used. Air/acetylene flow rates were between 0.9-1.1 L/min for all metals. The digested samples were directly injected into the spectrophotometer. External calibration curves were set up for each of the elements. Calibration solutions were prepared by successive dilution of a high purity multielement calibration standard solutions (1000 mg/L of each element in HNO\({}_{3}\) 65%, ICP Multi-Element Standard Solution IV CertiPUR(r) and 10 mg/L of each element in HNO\({}_{3}\) 65%, ICP Multi-Element Standard Solution XXI CertiPUR(r), Merck KGaA Frankfurter, Darmstadt, Germany). Ultrapure water with a maximum resistivity of 18.2 M\(\Omega\)/cm was used for dilution. All the obtained calibration curves were characterized by a high correlation coefficient (R\({}^{2}\) > 0.995) and the value of standard deviation <10%. All determinations were performed in two independent replicates.
# Multi-Controller Deployment in SDN-Enabled 6G Space-Air-Ground Integrated Network
## 1 Introduction
### In SDN-Enabled SAGIN Architecture Design  
The SDN multi-controller deployment architecture can be divided into two types: a flat design and layered design. In [22; 23], a cross-domain SDN network architecture for a multi-layer SAGIN is proposed. The data plane includes satellites, aircraft, and ground networks [24]. The control plane is divided into two layers, consisting of the main controller layer and the secondary controller layer. The secondary controller layer is deployed in the space-based network, air-based network, and ground-based network, respectively, and cross-network communication is realized through the main controller. It is a layered design. [PERSON] in [21] and [PERSON], [PERSON], et al. in [25] also used a multi-controller hierarchical design method to propose a software-defined space-air-ground integrated vehicle network to achieve flexible, reliable and scalable network resource management. [PERSON], [PERSON], et al. in [26] use a flat multi-controller deployment design in the software-defined satellite network. The control plane is composed of geostationary satellites, and the data plane includes low-orbit satellites, space-based networks and ground networks. This solution avoids dynamic changes in the network topology, but brings tremendous pressure to the geostationary satellite.
## 5 Computational Experiments, Results, and Discussion
### Results for Large-Sized Instances
For \(\Delta\overline{HV\%}\), Figure 11 shows the main effects plots, while Figure 12 shows the interaction plots. As shown in Figure 11, the mean value of \(\Delta\overline{HV\%}\) became slightly below 0 at higher \(|\mathcal{J}|\) and \(LL\) values. Meanwhile, the effect of the other three factors remained in the positive domain despite being significant at 95% confidence, based on the analysis of variance (ANOVA) results. Meanwhile, the interaction plots of Figure 12 show that higher values of \(|\mathcal{W}|\) combined with higher values of \(|\mathcal{J}|\), as well as higher values of \(LL\) combined with higher values of \(|\mathcal{J}|\), resulted in larger negative values for the response. Accordingly, it can be concluded that the developed MOSS demonstrated better performance compared to NSGA-II for cases with low and intermediate density, while this situation was reversed for more dense cases. \begin{table}
\begin{tabular}{l c} \hline \hline
**Factor** & **Selected Levels** \\ \hline \(|\mathcal{W}|\) & 10, 20 \\ \(|\mathcal{J}|\) & 20, 40 \\ \(LL\) & 0.5, 0.9 \\ \(PLJ\) & 30\%, 50\% \\ \(PBM\) & 30\%, 50\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Selected levels of the structural parameters used in generating large DMOSP instances.
# Sensitivity Estimation for Differentially Private Query Processing
###### Abstract  
Differential privacy is a robust framework for private data analysis and query processing, which achieves privacy preservation by introducing controlled noise to query results in a centralized setting. The sensitivity of a query, defined as the maximum change in query output resulting from the addition or removal of a single data record, directly influences the magnitude of noise to be introduced. Computing sensitivity for simple queries, such as count queries, is straightforward, but it becomes significantly more challenging for complex queries involving join operations. In such cases, the global sensitivity can be unbounded, which substantially impacts the accuracy of query results. While existing measures like elastic sensitivity and residual sensitivity provide upper bounds on local sensitivity to reduce noise, they often struggle with either low utility or high computational overhead when applied to complex join queries. In this paper, we propose two novel sensitivity estimation methods based on sampling and sketching techniques, which provide competitive utility while achieving higher efficiency compared to existing state-of-the-art approaches. Experiments on real-world and benchmark datasets confirm that both methods enable efficient differentially private joins, significantly enhancing the usability of online interactive query systems.  
differential privacy; sensitivity; join query; approximate query processing Article
# Deploying CPU-Intensive Applications on MEC in NFV Systems: The Immersive Video Use Case
## Overall Architecture of the Proposed ME System
### ME App On-Boarding and Instantior Example in Crowded Event Scenarios
#### 3.2.2 ME App Instantiation
The MEAO interaction with the NFVO can thus be summarized by the following steps:  
* MEAO invokes the instantiation of the NS, including the ME app VNF;
* MEAO subscribes to NFVO notifications, related to such NS;
* MEAO queries the NFVO to retrieve the required information, related to this NS. The notification originated by the NFVO at the end of the NS instantiation process is the trigger to start the third step of the above procedure. The MEAO uses the information retrieved from the NFVO to complete the ME app instantiation. In the example shown in Figure 6, once the MEAO has retrieved the dB Connection Point IP address, it can create the corresponding DNS and traffic routing rules. Such information is sent to the Element Management System of the SGWLBO VNF via Mm3* [10]. In this way, the ME platform can set the DNS server included in SGWLBO, to properly forward the user data traffic to the corresponding i-EVS instance.