esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Information-Theoretic Large-Scale Semi-Supervised Metric Learning via Proxies
### Notations and Problem Definition  
Given a dataset, \(\mathbf{X}=[\mathbf{X}_{l},\mathbf{X}_{u}]\in\mathbb{R}^{d\times n}\) composed of the labeled data \(\mathbf{X}_{l}\in\mathbb{R}^{d\times n_{l}}\) and the unlabeled dataset \(\mathbf{X}_{u}\in\mathbb{R}^{d\times n_{u}}\), where \(n_{l}\) and \(n_{u}\) denote the number of labeled instances and unlabeled instances, respectively, in the semi-supervised setting, \(n_{l}\) is usually far smaller than \(n_{u}\). \(d\) is the dimensionality of the feature. For the labeled dataset \(\mathbf{X}_{l}\), suppose that each instance is associated with the class label, i.e., \(\mathbf{X}_{l}=\big{\{}(\mathbf{x}_{1},\mathbf{y}_{1}),(\mathbf{x}_{2}, \mathbf{y}_{2}),\cdots,(\mathbf{x}_{n_{l}},\mathbf{y}_{n_{l}})\big{\}}\), with \(\mathbf{y}\in\left\{0,1\right\}^{C}\), \(C\) is the number of the classes. Pairwise constraint sets \(\mathcal{S}\) and \(\mathcal{D}\) can be extracted from \(\mathbf{X}_{l}\):  
\[\begin{split}&\mathcal{S}=\big{\{}\big{(}\mathbf{x}_{i},\mathbf{x}_{ j}\big{)}\mid\mathbf{x}_{i}\text{ and }\mathbf{x}_{j}\text{ are semantic similar}\big{\}}\\ &\mathcal{D}=\big{\{}\big{(}\mathbf{x}_{i},\mathbf{x}_{j}\big{)} \mid\mathbf{x}_{i}\text{ and }\mathbf{x}_{j}\text{ are semantic dissimilar}\big{\}}.\end{split}\]  
Semi-supervised metric learning aims to learn a Mahalanobis matrix \(\mathbf{M}\) from \(\mathbf{X}\), such that data can be transformed into a new space, where the semantic similarity between data points can be diametrically estimated from their distances in the transformed space.  
A typical formulation of metric learning is to require the pairwise distance from \(\mathcal{S}\) to be smaller than \(l\) and that of the dissimilar set to be larger than \(u\).  
\[\begin{split}& d_{\mathbf{M}}^{2}\big{(}\mathbf{x}_{i},\mathbf{x}_{j}\big{)}<l,\ \forall\big{(}\mathbf{x}_{i},\mathbf{x}_{j}\big{)}\in\mathcal{S}\\ & d_{\mathbf{M}}^{2}(\mathbf{x}_{i},\mathbf{x}_{k})>u,\ \forall(\mathbf{x}_{i},\mathbf{x}_{k})\in \mathcal{D}\end{split} \tag{3}\]  
The general form of a semi-supervised metric learning framework can be constructed as:  
\[\min_{\mathbf{M}}\ell^{l}(\mathbf{M},\mathcal{S},\mathcal{D})+\lambda\ell^{u}( \mathbf{M},\mathbf{X}_{u})+\mu\mathcal{R}(\mathbf{M}), \tag{4}\]  
where \(\ell^{l}\) and \(\ell^{u}\) denote the pairwise loss and the unlabeled loss, respectively. \(\mathcal{R}\) is the regularization term concerning the structural information of the metric. \(\lambda\) and \(\mu\) are two hyper-parameters that control the weight of the \(\ell^{u}\) and \(\mathcal{R}\).
## 2 Results and Discussion
### General workflow
#### 2.1.3 Data pre-processing  
R-based XCMS [38], Java-based Mzmine, and commercially available MarkerView [39; 40] were used for pre-processing in current study. Because Mzmine took almost one week for analyzing a relatively small dataset, suggesting time consuming calculations, XCMS and MarkerView were preferred.  
Each chromatogram was manually inspected on the presence of repetitive noise, artifacts and solvents inclusions. Each blank and QC chromatograms were used for ruling out data that do not belong to samples. The peak detection algorithm used by XCMS is based on cutting the LC/MS data into slices a fraction of a mass unit (0.1 _m/z_) wide and then operating on those individual slices in the chromatographic time domain. Within each slice, the signal is determined by taking the maximum intensity at each time point in the slice. After identifying peaks in individual samples, those peaks must then be matched across samples to allow calculation of retention time deviations and relative ion intensity comparison. XCMS incorporated a peak-matching algorithm that takes into account the two-dimensional, anisotropic nature of LC/MS data. The XCMS retention time alignment algorithm simultaneously corrects the retention times of all samples in a single step. After XCMS parameter optimization, the group bandwidth was set to 30, the minimum fraction was set to 0.5, the minimumsample parameter was set to 1, the width of overlapping _m/z_ values was set to 0.5, and the maximum number of groups in a single _m/z_ slice was set to 0.5.  
After processing and peak picking, mass spectral features were retrieved from XCMS as a TXT file. The dataset was normalized using Euclidean norm by scaling each sample-vector to unit vector norm, which can be interpreted geometrically as a projection of the samples x to a hyper-sphere with the length of this sample vector scaled to one. After normalization, the ratios between masses are the same as before, but the intensity of the sample is removed. Since unit variance unifies the influence of each variable, vector normalization is closely related to correlation analysis. Because unmodified PCA is a linear method and vector norm projects to a curved hyperspace, theoretically, the normalized data could be linearized by transforming from Cartesian to spherical coordinates, where the angles have hierarchical orders. However, this does not affect the results very much. In practice, peaks were normalized to the total absolute area of all detected metabolites in each sample using an in-house written R script.
# A Tunable Self-Offloading Module for Plantar Pressure Regulation in Diabetic Patients
###### Abstract  
Plantar pressure plays a crucial role in the pathogenesis of foot ulcers among patients with diabetes and peripheral polyneuropathy. Pressure relief is a key requirement for both the prevention and treatment of plantar ulcers. Conventional medical practice to enable such an action is usually realized by means of dedicated insoles and special footwear. Another technique for foot pressure offloading (not in medical practice) can be achieved by sensing/estimating the current state (pressure) and, accordingly, enabling a pressure release mechanism once a defined threshold is reached. Though these mechanisms can make plantar pressure monitoring and release possible, overall, they make shoes bulkier, power-dependent, and expensive. In this work, we present a passive and self-offloading alternative to keep plantar pressure within a defined safe limit. Our approach is based on the use of a permanent magnet, taking advantage of its non-linear field reduction with distance. The proposed solution is free from electronics and is a low-cost alternative for smart shoe development. The overall size of the device is 13 mm in diameter and 30 mm in height. The device allows more than 20-times the tunability of the threshold pressure limit, which makes it possible to pre-set the limit as low as 38 kPa and as high as 778 kPa, leading to tunability within a wide range. Being a passive, reliable, and low-cost alternative, the proposed solution could be useful in smart shoe development to prevent foot ulcer development. The proposed device provides an alternative for offloading plantar pressure that is free from the power feeding requirement. The presented study provides preliminary results for the development of a complete offloading shoe that could be useful for the prevention/care of foot ulcers among diabetic patients.  
plural pressure; passive device; self-offloading; diabetic foot; tunability Article
## 5 Architecture, Optimization, and Technical Challenges in LLM4 Rec
### Architecture and Design of LLM4 Rec  
The architecture of LLM4 Rec systems as seen in Figure 2 integrates the core functionalities of LLMs with specific design considerations for recommendation tasks. Integrating user and item representations is essential for accurate predictions in LLM4 Rec.  
* Latent Representations:User embeddings are derived from interaction histories, while item embeddings incorporate features like product metadata and textual reviews [81].
* Attention-Based Integration: Models like XLNet4 Rec capture sequential user behaviors and align them with item attributes for enhanced personalization [34].  
Traditional embedding techniques focus only on static features, while LLM-based methods dynamically adjust embeddings to capture evolving interactions. To handle the unique requirements of recommendations, LLM vocabularies are extended to  
* User and Item IDs: By incorporating IDs into the token vocabulary, models can directly associate embeddings with specific users and items [6];
* Dynamic Tokenization: Frequent updates to embeddings enable real-time adaptation to new users or products [82]. Attention mechanisms enhance LLM4 Rec by capturing dependencies between user interactions and item attributes:
* Self-Attention:Captures temporal user behaviors by analyzing sequences of past interactions [6].
* Cross-Attention: Combines textual metadata with user interactions, as seen in models integrating product reviews with user activity [72].  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Aspect** & **Discriminative Paradigm** & **Generative Paradigm** \\ \hline Task Focus & Classification, ranking, prediction & Open-ended recommendation, content generation \\ \hline Adaptability & Requires domain-specific fine-tuning for best results & Supports zero-shot/few-shot scenarios using pre-trained knowledge \\ \hline Key Strengths & High-precision and effective in structured, supervised environments & Flexible, can handle multimodal and conversational settings \\ \hline Training Dependency & High-needs labeled interaction data for fine-tuning & Moderate; can be used out-of-the-box or lightly fine-tuned \\ \hline Evaluation Metrics & NDCG, Recall@K, CTR, AUC & BLEU, ROUGE, Diversity, Human Evaluation, CTR \\ \hline Use Cases & Sequential recommendation, CTR prediction, ranking & Conversational agents, narrative-driven recommenders, explanations \\ \hline Examples & BERT4 Rec [6], PALR [79] & GPT4 Rec [4], RecMind [80] \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of discriminative and generative paradigms in LLM4 Rec. Source: Author’s own design.  
The following prompts enable LLMs to adapt to recommendation tasks without retraining:  
* Soft Prompts: Embedding prompts are injected into the input sequence, improving task-specific adaptability [83].
* Hard Prompts:Predefined natural language queries guide LLMs in generating recommendations or explanations [69; 84].
# AI-For-Mobility--A New Research Platform for AI-Based Control Methods
### Vehicle Application Layer  
The top layer deals with the vehicle application and is used to generate trajectories or paths. Motion planning is performed on the basis of environmental information. For this purpose, the AFM is equipped with various perception sensors such as cameras, lidar or radar sensors. By means of advanced machine learning methods, the driving environment  
Figure 1: The AFM test vehicle.  
Figure 2: The DLR-SR scalable vehicle dynamics control architecture and its applications, fully addressed by the AFM, extended from [4].  
is recorded and processed in real-time. The resulting motion requirements for the three planar degrees of freedom are passed to the Motion Execution layer.  
**Motion Execution Layer**  
This layer contains the vehicle dynamics control, which calculates appropriate vehicle dynamics quantities to realize a required motion and ensures the safe operation of the vehicle. The AFM's extensive sensor equipment enables development and testing of advanced AI control approaches. The horizontal vehicle motion of the AFM described by the longitudinal (throttle and brake) and lateral (steering) dynamics is controlled by a full by-wire kit. In order to control the vehicle's vertical dynamics, the AFM is in turn equipped with semi-active dampers. The resulting actuator demands are then considered by the architecture layer lying below.  
**Actuator Control**  
The lowest layer deals, for instance, with the actuator control of the steering and braking system. In the AFM, extensive information about the actuators is available for many sub-systems (e.g., by reading the vehicle CAN buses). Due to this feature, the AFM provides a suitable test platform for applications in this layer.  
Note that some applications, such as state estimation methods, may span over multiple layers.  
The AFM concept has been designed as a universal test platform for vehicle dynamics control approaches, which allows all applications of the layer model shown in Figure 2 to be addressed consistently. The holistic capability for testing novel AI-based control methods makes the AFM a unique test platform. Most other vehicle demonstrators do not take all three layers into account, leading to a research gap in the context of vehicle dynamics. Some of these vehicle concepts are discussed in the following section.
# Analysis of 5G Channel Access for Collaboration with TSN Concluding at a 5G Scheduling Mechanism
###### Abstract  
As 5G enters the application field of industrial communications, compatibility with technologies of wired deterministic communications such as Time-Sensitive Networking (TSN) needs to be considered during the standardization process. While consideration of underlying integration architectures and basic resource mapping are already part of the standard, necessary traffic forwarding schemes are currently planned to be deployed in additional interfaces located at the edge of a 5G System. This analysis highlights the extent to which internal 5G mechanisms can be used to execute the traffic forwarding of TSN streams according to the requirements of the TSN control plane. It concludes with the recognition that a static prioritization of logical channels is not appropriate for the treatment of TSN streams over the 5G air interface. A novel prioritization mechanism of logical data channels is derived, which enables the execution of TSN-compliant traffic shaping over 5G RAN. Subsequently, a proof of concept is implemented and simulated for evaluation.  
5G; TSN; Industry 4.0; real-time traffic; industrial communication; scheduling Article
## 7 Fault Location Method Based on Similarity of Voltage Signals
### Post Fault Traveling Waves
#### 8.2.1 Post Pole to Ground Fault  
Pre and post fault voltage values of zero and positive sequence components of pole to ground faults are observed. Difference between pre and post fault voltage values are evaluated to determine the effect of grounding resistance on fault.  
\[\left\{\begin{array}{c}\Delta u_{fa0}=\ u_{fa0}=\frac{\sqrt{2}U_{d_{+}}Z_{0 \Sigma}}{Z_{1\Sigma}+Z_{0\Sigma}+2K_{g}}\\ \Delta u_{fa1}=\ u_{fa1}-u_{a1}=\frac{\sqrt{2}U_{d_{+}}Z_{1\Sigma}}{Z_{1\Sigma }+Z_{0\Sigma}+2K_{g}}\end{array}\right. \tag{70}\]  
and  
\[\frac{\Delta u_{fa0}}{\Delta u_{fa1}}=\frac{Z_{0\Sigma}}{Z_{1\Sigma}} \tag{71}\]  
where \(Z_{0\Sigma}=\ Z_{s}+Z_{m}\), \(Z_{1\Sigma}=Z_{s}-Z_{m}\). \(Z_{s}\) and \(Z_{m}\) are the self-inductance and mutual inductance, respectively, of the dc-link and;  
\[Z_{0\Sigma}>Z_{1\Sigma} \tag{72}\]  
\[\frac{\Delta u_{fa0}}{\Delta u_{fa1}}>1 \tag{73}\]  
Hence, it is concluded that single pole to ground fault analysis is independent of grounding resistance.
## 3 Research Method
### Data Reconciliation Principle
Here, the correction matrix of the covariance matrix is also given as follows:  
\[\mathbf{S_{v}}=\mathbf{S_{x}}\mathbf{J_{F,x}^{T}}\mathbf{S_{F}}^{-1}\mathbf{S_{x}}\mathbf{J_{F,x}} \tag{7}\]which should be used to modify the covariance matrix \(\mathbf{S}_{\mathbf{x}}\). The linearization method usually expands the nonlinear constrained equation according to [PERSON]'s formula and retains the first derivative term. However, in practical engineering applications, the expanded higher-order derivative term cannot be simply ignored by Jacobian matrix linearization, and the residual of constraint equation cannot be eliminated by a simple correction. Thus, the data reconciliation model established in this paper adopts the iterative algorithm framework developed by [PERSON] et al. [37]. The correction vector iteration format is constructed as follows:  
\[\mathbf{v}^{(k)}=\left\{ \begin{array}{cc}\mathbf{0},&k=0\\ \mathbf{v}^{(k-1)}+\Delta\mathbf{v}^{(k)},k\geq 1\end{array} \right. \tag{8}\]  
\[\Delta\mathbf{v}^{(k)}=\mathbf{S}_{\mathbf{x}}\mathbf{J}_{\mathbf{F}\mathbf{x}}^{(k)}\Big{[}\mathbf{S}_{ \mathbf{F}}^{(k)}\Big{]}^{-1}\mathbf{F}^{(k)} \tag{9}\]  
where \(k\) is the number of iterations. Before the iterative computation begins, the updated Jacobian matrix \(\mathbf{J}_{\mathbf{F}\mathbf{x}}^{(k)}\) is recalculated by numerical differentiation, and the covariance matrix of the equation residual vector \(\mathbf{S}_{\mathbf{F}}^{(k)}\) is recalculated by Equation (6), then the iterative process continues. The covariance matrix of the correction vector for the iterative calculation process can be solved as follows:  
\[\mathbf{S}_{\mathbf{v}}^{(k)}=\left\{ \begin{array}{cc}\mathbf{0},&k=0\\ \mathbf{S}_{\mathbf{v}}^{(k-1)}+\Delta\mathbf{S}_{\mathbf{v}}^{(k)},k\geq 1\end{array} \right. \tag{10}\]  
\[\Delta\mathbf{S}_{\mathbf{v}}^{(k)}=\mathbf{S}_{\mathbf{v}}^{(k)}\Big{[}\mathbf{f}_{\mathbf{F}\mathbf{x}} ^{(k)}\Big{]}^{T}\Big{[}\mathbf{S}_{\mathbf{F}}^{(k)}\Big{]}^{-1}\mathbf{S}_{\mathbf{v}}^{(k) }\mathbf{J}_{\mathbf{F}\mathbf{x}}^{(k)} \tag{11}\]  
And the iterative process is terminated when the incremental norm of the correction vector is too small:  
\[\parallel\Delta\mathbf{v}^{(k)}\parallel<\varepsilon \tag{12}\]  
where \(\varepsilon\) is the convergence limit. In this way, the optimizing calculation of data reconciliation for the initial value of variables is completed.
## 3 Results
### Measured Results with the Prototype of a STEG Flat Panel System with Buried Heat Sink  
Looking at the data presented in Figure 13, we observe that the performance of the system with the heat sink buried at the depths of 10 cm and 40 cm is practically the same, so we installed the prototype with the heat sink buried at a depth of 10 cm, since this provides a cheaper, lighter, and much easier installation. In our prototype we used a heat sink with \(200\times 127\) mm\({}^{2}\) area, with 16 fins, that presents a thermal resistance \(R_{th}=0.3\,^{\circ}\)C W\({}^{-1}\).  
Since the supercapacitor is charged both when \(T_{plate}>T_{depth}\) and \(T_{plate}<T_{depth}\), we cannot determine when it is being charged with \"forward\" or \"reverse\" energy. To be able to distinct when the supercapacitor is being charged with \"forward\" or \"reverse\" energy, we installed two AD590 temperature sensors (from Analog Devices), one on each side of the TEG, so we can correlate the charge of the supercapacitor with the sense of the heat flow through the TEG.  
Figure 12: Sum of the “forward” and “reverse” integrals of \(\Delta T^{2}\) with respect to time (for \(|T_{plate}-T_{depth}|\geq 1\,^{\circ}\)C), at various depths, for 205 days (from 23 June 2015 to 13 January 2016), in Mataro.  
Figure 13: Sum of the “forward” and “reverse” integrals of \(\Delta T^{2}\) with respect to time (for \(|T_{plate}-T_{depth}|\geq 1\,^{\circ}\)C), at various depths, for 32 days (5 July to 5 August 2015), in Campinas.  
The number of cycles (\(N\)) in the microcontroller is counted in two distinct registers: one counts \(N\) when \(T_{plate}>T_{depth}\) and the other when \(T_{plate}<T_{depth}\). In Figure 14 we present the graph of the energy stored in the supercapacitor during 32 consecutive winter days in Campinas.  
From Figure 14 we can observe that, as predicted from the integrals of \(\Delta T^{2}\) (Figure 10), the partial part of thermal energy converted to electrical which was stored in the soil is very important. Observing the graph of Figure 14 it is interesting to notice that, in a few days, the amount of \"reverse\" energy is equal or even higher than the \"forward\" energy.  
In Campinas, the maximum energy supplied by the harvesting system and stored in the supercapacitor in one day (during the 32 days period) occurred on a clear day (12 August 2015,) and the energy stored in the supercapacitor in this day was 34.11 J. It is worth to notice that even during rainy and cloudy days, it was always possible to collect a good amount of energy (the minimum value of energy stored in the supercapacitor in one day was 4.1 J). The daily average energy stored in the supercapacitor during the 32 days period was 24.38 J per day.
## 6 Conclusions  
In this paper, we presented a distributed data-gathering scheme using an AUV over a UWSN. The proposed scheme aims at energy-constrained applications where energy consumption is the most critical parameter within an acceptable latency. In the absence of global information such as residual energy and amount of traffic to be collected, the proposed scheme organizes the network into deterministic neighborhoods, where CHs manage relay nodes, substituting them with new ones based on the residual energy at MNs. It is evident from the simulation results that the proposed scheme results in a significant reduction in energy consumption compared with the conventional AUV-based scheme (where the AUV visits only cluster heads) and the Domingo routing protocol. In addition, even though the proposed scheme produces slightly more protocol overhead than the conventional AUV-based scheme, the proposed scheme leads to more uniform energy consumption over the network, thereby increasing the lifetime of the network.  
In a deep sea networking environment, where AUV is necessary to extend the operational time of sensor nodes, the realization of an optimal deterministic neighborhood for AUV-employed data gathering is quite a challenging task because of adverse channel conditions and mobility of medium. Especially, for networks with large coverage, this task becomes more challenging owing to the variation in communication probabilities and network topologies. The proposed scheme is capable of controlling the topological changes locally by communicating such changes within the neighborhood only, thereby eliminating the requirement for global topology control over the network. This may reduce the energy consumption of sensor nodes caused by the topological changes. Thus, the proposed scheme is expected to perform well under realistic underwater networking scenarios.  
In future, we will analyze and compare the proposed scheme with the existing schemes in terms of communication overhead which may be generated due to frequent network reconfiguration that results from medium mobility due to water currents and other underwater activities. We will also conduct sea experiments for different sizes of network and AUV parameters to find out the impact of different environment conditions on identified performance parameters. Furthermore, we will also look into protocol parameters such as the mobility model of the AUV, neighborhood size, and tour lengths, which are necessary for designing effective AUV employed data-gathering schemes for time-critical scenarios.