esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Human Mobility Modeling  
Humans constantly move to our jobs, universities, schools, or any other destination daily. However, this behavior is nothing new; throughout history, humans have migrated between different territories countless times, as mentioned in Ref. [52]. A significant change between a few decades ago and now is the high usage of smart devices within the population; this growth in usage is mainly due to the high adoption of smartphones in our lives [53; 54].  
This characteristic of the current state in the adoption of smartphones has attracted the interest of scientists in researching human mobility, mainly because now we have the tools to collect accurate data that can help to create models that can mimic our mobility patterns. Nowadays, there are multiple ways to obtain data from human mobility. GPS traces are one of these sources for mobility patterns. Also, CDRs (called detail records) have been used to learn from human mobility, and, finally, from some datasets of connection to an access point like a WiFi [55; 56] point. In this field of research, although there has been significant progress in terms of data sources, the use of synthetic data from mobility models continues to be prevalent. This is because synthetic data remain a convenient method for studying and proposing new mobility models.  
As mentioned in Ref. [57], we can divide human mobility models into nonsocial, social, and hybrid categories. Others [53] divide them into movement-based, linked-based, and network-based. Individual or movement-based mobility models are generated from individual traces of either GPS, CDR, or any other data source that can describe how a human moves without the influence of other humans over his or her patterns. On the other hand, social, network-based, or linked-based models try to model human mobility patterns, considering the different interactions we, as humans, have and how these social interactions can affect our mobility patterns [53]. In this work, we will focus on individual or movement-based mobility models, notably random walk and random waypoint, to generate the connection and disconnection events from an access point. We mainly chose these two models to generate synthetic data because they represent the most dynamic scenarios, since nodes tend to move randomly. This way, we could expose our proposed human-designed heuristics (RelBPA, ReleSEAS) and the RL agent to hostile environments. To develop these data, we used PyMobility [58] as a base for the mobility algorithms and added a layer of code to check the connections and disconnections from the devices. Then, these data were used as input for the EdgeDewSim Extended Simulator.
# LoRa Technology Propagation Models for IoT Network Planning in the Amazon Regions
We fixed the LoRa gateway on the roof of the largest UFPA building, called Mirante do Rio, and used an end node in an unmanned aerial vehicle (UAV) of the drone type, moving for 1.4 km on a predetermined route, maintaining a distance of 50 m from the river bank. Our study presents results of signal coverage with mobility between the gateway and the LoRa end node at 915 MHz, packet reception rate data to quantify how viable the technology is for these types of environments, and the characterization of the channel when considering an LoRa device on a route on the banks of rivers. We analyze the sending of information from the end node to the network (uplink), examining the received values of RSSI and SNR using different spreading factor values. Additionally, we characterize the channel by adjusting models and compare them with the Okumura-Hata suburban model; finally, we propose a new uplink model that considers the vegetation loss present at ITU Recommendation P-833. In this context, the contributions of this work are as follows:  
* Extensive measurement campaigns in coastal environments of rivers;
* Characterization of uplink channels, for three different spreading factors (SF) values, to analyze which SF presents the best results for the environment under study;
* Adjustment of close-in (CI) propagation and floating intercept (FI) models, presenting path loss exponent (PLE) values for the uplink channel;Analysis of received signal strength indication (RSSI) and signal-to-noise ratio (SNR) variation along the route for the uplink channel;
* Analysis of the rate of received packets;
* Comparing adjusted models to classic Okumura-Hata model for suburban environments;
* Uplink channel modeling that considers the attenuation suffered by vegetation, based on the free space model and the ITU-R Recommendation P-833 model;
* Statistical analysis of coverage for LoRa network planning in river cases. From our results, we aim to assist in the planning of LoRa sensor network links in regions along the rivers, and quantitatively verify the signal attenuation resulting from the morphology of the environment and dense vegetation present in the Amazonian coastal zones. Additionally, we will validate the use of technology for sending information securely, and quickly, even with the boat in motion, and support the development of new LoRa applications for use in smart campuses. The rest of this article is organized as follows: Section 2 provides an overview of LoRa technology. Section 3 describes the equipment, the characteristics of the test environment, and the methodology used in the measurement campaigns. Section 4 describes all empirical propagation models used in the present study. Section 5 presents the measured results and the relevant work analysis. Finally, Section 6 presents the conclusions.
## 1 Introduction  
Kidney cancer represents 2% of the global cases of cancers worldwide [1]. Among kidney cancer histotypes, renal cell carcinoma (RCC) is the most common form (80% of all cases), with 403,262 diagnoses and 170,000 deaths throughout the world in 2018 [2]. In their life, approximately 1 in 69 men and 1 in 116 women will be diagnosed with RCC [3]. Kidney cancer shows a heterogeneous clinical presentation, from small, localized tumors to aggressive metastatic diseases. RCC treatment depends on tumor stage and grade and the overall health status of the patients. Surgery represents the standard treatment for localized RCC, while cytoreductive nephrectomy and systemic treatments (targeted therapies and immunotherapeutic agents) represent the standard of care for patients presenting metastatic RCC (mRCC). Recent evidence suggested that stereotactic body radiotherapy (SBRT) may represent a new option in the treatment of primary local RCC and oligometastatic RCC [4]. Although RCC is curable by surgery, about 20% of patients have metastases and experience disease recurrence during follow-up [5]. Micro-RNAs (miRNA, miR) are 19 to 22 nucleotide-long endogenous small noncoding RNAs involved in different biological functions regulating more of 60% of human protein-coding genes related to cell growth, apoptosis, differentiation, and proliferation [6]. MiRNAs act also as tumor suppressors or oncogenes and can regulate several gene transcripts as well as the expression of other miRNAs. In particular, they are involved in cell proliferation and death, migration, epithelial-mesenchymal transition, tumor invasion and metastasis. MiRNAs can influence angiogenesis, energy metabolism and immune system activation and recruitment, as shown in Figure 1, MiRNAs can bind to a specific sequence at the 3\({}^{\prime}\) or 5\({}^{\prime}\) untranslated region (UTR) of their target genes or messenger RNA (mRNAs) or other coding sequences as well as promoter regions. Indeed, the miRNA interaction with 3\({}^{\prime}\) and 5\({}^{\prime}\) UTR induces the repression and transcription, respectively, of genes [6] (Figure 2) translational repression and mRNA deadenylation and decapping. The interaction with 5\({}^{\prime}\) UTR and coding regions silences gene expression, while miRNA interaction with the promoter region induces transcription. In the last decades, several studies highlighted the role of miRNA in several human cancers, including lymphoma, glioblastoma, hepatocellular, breast, lung, colon-rectal, prostate and thyroid [7; 8]. Due to this role and their profiling expression as markers of tumor origin, miRNAs could play an important role in the diagnosis, treatment, and prognosis of RCC, particularly thanks to their profile of expression. Therefore, this review offers an updated overview of the current knowledge about miRNAs as biomarkers, therapeutic targets, or modulators of response to treatment in RCC patients.
## 4 Case Study
### Virtual Sensor Fault-Detection Performance Metrics
#### 4.3.4 F-1 Score  
The F-1 score is used to prevent results in which precision or recall is biased because precision and recall have a trade-off relationship. The F-1 score is the harmonic mean value of precision and recall and has a value from 0 to 1. The fault-detection performance of the virtual sensor improved as the F-1 score increased. The F-1 score is calculated using the following expression:  
\[\text{F-1 score}=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{13}\]
# Integrated People and Freight Transportation:
###### Abstract  
Increasing environmental and economic pressures have led to numerous innovations in the logistics sector, including integrated people and freight transport (IPFT). Despite growing attention from practitioners and researchers, IPFT lacks extensive research coverage. This study aims to bridge this gap by presenting a general framework and making several key contributions. It identifies, researches, and explains relevant terminologies, such as cargo hitching, freight on transit (FoT), urban co-modality, crowd-shipping (CS), occasional drivers (OD), crowdsourced delivery among friends, and share-a-ride, illustrating the interaction of IPFT with different systems like the sharing economy and co-modality. Furthermore, it classifies IPFT-related studies at strategic, tactical, and operational decision levels, detailing those that address uncertainty. The study also analyzes the opportunities and challenges associated with IPFT, highlighting social, economic, and environmental benefits and examining challenges from a PESTEL (political, economic, social, technological, environmental, and legal) perspective. Additionally, it discusses practical applications of IPFT and offers recommendations for future research and development, aiming to guide practitioners and researchers in addressing existing challenges and leveraging opportunities. This comprehensive framework aims to significantly advance the understanding and implementation of IPFT in the logistics sector.  
integrated people and freight transportation; cargo hitching; freight on transit; literature review; opportunities and challenges +
Footnote †: journal:
# Patch-Wise-Based Diffusion Model with Uncertainty Guidance for Low-Light Image Enhancement
## 2 Related Work
### Learning-Based Method for LLIE  
Recent advancements in deep learning have significantly enhanced the performance of low-light image enhancement (LLIE) methods based on deep learning techniques [29, 30, 31, 32]. These methods can be categorized into supervised learning, semi-supervised learning, reinforcement learning, and unsupervised learning, each employing different learning strategies [33, 34, 35, 36]. For an end-to-end supervised learning approach, LLNet [37] introduced a deep autoencoder-decoder framework that identifies signal features in low-light images and adaptively brightens them. LLNet was the first to integrate deep learning networks into LLIE, paving the way for the combined application of deep learning and LLIE. Building on this, MBLLEN [38] developed modules for feature extraction, enhancement, and fusion to improve feature representation capabilities and thereby enhance LLIE performance. Several studies, such as those by [39, 40], have proposed encoder-decoder networks for image edge enhancement and multi-exposure fusion. To address image noise, refs. [41, 42] introduced a low-frequency noise suppression network based on frequency decomposition and enhancement, effectively reducing low-frequency noise in low-light images and restoring details in the high-frequency layers. LLIE methods based on semi-supervised learning reduce reliance on extensive datasets [43]. For instance, ref. [44] introduced a semi-supervised Deep Recurrent Band Network (DRBN), which first recovers the linear band representation of the enhanced image using supervised learning, and then recombines the bands through a linear transformation based on unsupervised adversarial learning, yielding an improved band representation. RetinexDIP [45] created a unified deep zero-reference framework, RetinexDIP. RetinexDIP was based on a newly proposed \"generative\" strategy. Regarding the application of reinforcement learning in LLIE, [PERSON] developed Deep-Exposure [46], which employs a reinforcement learning strategy to divide the image into sub-images based on exposure, localize each sub-image, incrementally improve the image through a reward evaluation function, and ultimately fuse the locally exposed sub-images to achieve optimal image enhancement [47, 48]. Finally, ref. [49] proposed a self-supervised image enhancement network that is trained using only low-light images. ZeroDCE [50] estimates the luminance profile of the input image during training without the need for any paired or unpaired data.
## 6 Key Challenges and Mitigation Strategies  
In this section, we identify the key challenges of the synergy of IoT, LLM, and FL, and suggest potential mitigation strategies based on relevant techniques found in the open literature. Table 6 summarizes the main challenges and mitigation methods, as elaborated on next.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Challenge** & **Description** & **Mitigation Strategies** & **Trade-Offs/Notes** \\ \hline Resource Constraints (Compute, Memory, Energy) & Severe limitations on many IoT devices conflict with LLM computational demands [3; 24]. & Model compression [62]; split computing [3; 18]; PEFT [16]; adaptive distribution. & Accuracy loss (compression); latency/sync needs (split); limited adaptivity; orchestration complexity (adaptive). \\ \hline Communication Overhead & High cost of transmitting large model updates frequently over constrained IoT networks [10; 111]. & PEFT [16]; update compression [111]; reduced frequency [9]; asynchronous protocols [10; 112]. & Smaller updates limit model changes; info loss risk (compression); slower convergence (frequency); staleness issues (Async). \\ \hline Data Heterogeneity (ImD) and Fairness & Non-IID data hinders convergence and fairness [44; 113]; bioases can be amplified [37]; decentralized bias mitigation is hard. & Robust Aggregation (e.g., FedProx) [44]; PFL [73; 74]; fairness-aware algorithms; synthetic/public data augmentation [103; 104]. & Complexity (PFL); potential avg. accuracy reduction (fairness); privacy concerns with data augmentation. \\ \hline Privacy and Security Risks & Balancing privacy vs. utility; protecting against leakage [110; 114], poisoning [109; 115], Byzantine [116], backdoor attacks [117; 118; 119]; regulatory compliance (GDPR, HIPAA). & PETs (DP [48], HE [53], SMPC [55], secure aggregation [56]); Robust Aggregation (e.g., Krum [116], PEAR [120]); attack detection [121; 122]; TEEs [123]; ZKP-based methods (e.g., ByzSFL [124]). & Edge infrastructure optimization (caching, serving) [3]; scalable FL orchestration (hierarchical, decentralized, asynchronous) [42; 101; 125]; resource-aware management [3; 43]. \\ \hline \end{tabular}
\end{table}
Table 6: Major challenges in integrating IoT, LLMs, and FL, with mitigation strategies.
## 2 State-of-the-Art
### Forward Problem
Increments in the precision of the EEG forward problem results have also been demonstrated by the incorporation of tissue inhomogeneity, even within the gray and white matter [31]. Apart from FEM, computationally efficient and accurate solutions have also been achieved with the introduction of anisotropic conductivity equations and utilization of the reciprocity theorem [32] on FDM head models (AFDRM-NZ) [33]. In this regard, anisotropic conductivity values are integrated by employing a set of surface integral equations aligning the produced solutions with the analytical results [34]. Of note is that the skull conductivity and, therefore, the correct modeling is a pivotal point, which, due to head abnormalities or insufficient imaging tools, can be challenging to approximate [35]. To address this issue, Bayesian Approximation Error (BAE) approaches displayed high efficiency in reducing the source localization error by several millimeters, enhancing spatial accuracy, which is crucial in clinical applications [36].
# Methane Emission Estimates by the Global High-Resolution Inverse Model Using National Inventories
## 3 Results and Discussion
### Spatial Patterns of the Flux Corrections  
The anthropogenic posterior fluxes for 2010-2012 and the flux correction scales for cases with prior emissions of EDGAR (S0) and EDGAR scaled to UNFCCC reports (S1) are presented in Figure 5. The optimized anthropogenic emissions are represented by the ratios of anthropogenic corrections (\(CORR_{an}\)) to multi-annual average EDGAR emissions defined in Equation (3):  
\[scale_{an}=CORR_{an}/EDGAR_{2008-2012}. \tag{3}\]  
Figure 4: Regional anthropogenic methane emissions (unit Tg CH\({}_{4}\) yr\({}^{-1}\)) in 2010–2012 for cases S0 and S1. The error bars are the estimation uncertainties.  
The two optimized flux maps (2010-2012 average) for cases S0 and S1 are not distinguishable in Figure 5a,c showing large emissions in Southeast Asia, while the scaling factor maps illustrate evident differences. Figure 5b depicts obvious anthropogenic emission increases in northern Europe, Russia, and boreal America, and a larger area of decrease in east China for case S0. The scaling factor of case S1 shown in Figure 5d illustrates a relatively smaller decrease in China compared with that shown for S0, and a slight decrease in Russia. Case S1 shows a larger increase in the scaling factor over India compared that shown for S0, which is likely to be caused by lower prior emissions in case S1.  
The average natural posterior fluxes for the years 2010-2012 and the scaling factors are presented in Figure 6. The optimized fluxes are represented by the ratios of wetland flux corrections (\(CORR_{w}\)) to the multi-annual mean VISIT prior emissions defined in Equation (4):  
\[scale_{w}=CORR_{w}/VISIT_{2008-2013} \tag{4}\]  
where \(scale_{w}\) is the wetland emission scaling factor. The prior natural emissions from the VISIT emission inventory are shown to be the same for both cases (S0 and S1). The scaling factors are used to illustrate the optimization of fluxes. Figure 6d shows a greater increase in the Amazon area and greater decreases in Siberia and boreal America for case S1 compared with those shown in Figure 6b for case S0. It is shown that lower wetland emissions are required in Siberia with higher anthropogenic prior emissions for Russia in the S1 case.  
Figure 5: Posterior anthropogenic methane fluxes (S0 (**a**) and S1 (**c**)) and scaling factors (S0 (**b**) and S1 (**d**)). (2010–2012 average, unit mg CH\({}_{4}\) m\({}^{-2}\) day\({}^{-1}\)).
# Hydrogen-Rock Interactions in Carbonate and Siliceous Reservoirs: A Petrophysical Perspective
## 3 Results and Discussion
### SEM-EDS Analysis
In Core BH, a slight increase in Fe is detected, with a more heterogeneous spatial pattern, suggesting secondary Fe-rich phase formation driven by hydrogen-induced redox reactions. The redistribution of Mg and minor elements is also observed, indicating localized elemental migration within the calcite matrix. In contrast, the EDS results for the quartz samples (C and CH, Figure 10) reveal consistent compositional stability. Both spectra show Si and O as dominant elements with uniform distributions of Al, K, Mg, and Fe. No notable differences are observed in elemental  
Figure 8: EDS spectrum and elemental distribution for core samples A and AH. Figure 9: EDS spectrum and elemental distribution for core samples B and BH. composition or spatial distribution after hydrogen treatment, further affirming the chemical inertness of quartz. Meanwhile, major elemental redistributions were observed in AH and BH samples, linked to ankerite formation (in dolomite) and secondary calcite precipitation (in calcite). These microstructural and compositional findings are consistent with textural parameter evolution. In calcite, hydrogen exposure reduced the BET surface area from 6.93 to 5.80 m\({}^{2}\)/g, total pore volume from 0.0175 to 0.0143 cm\({}^{3}\)/g, mean pore diameter from 3.023 to 2.853 nm, porosity from 19.89% to 16.33%, and permeability from 132.16 to 107.43 mD, highlighting partial pore collapse and occlusion due to carbonate reprecipitation. In contrast, the dolomitic system demonstrated a distinct structural response: after hydrogen exposure, the total pore volume increased from 0.0214 to 0.245 cm\({}^{3}\)/g, average pore diameter expanded from 3.3 to 5.09 nm, and permeability improved alongside surface area growth. The SEM images of Core AH confirmed severe grain disaggregation and development of an interconnected pore system, enriched with Fe-bearing phases attributed to ankerite formation. These contrasting behaviors clearly illustrate that calcite and dolomite respond differently to hydrogen treatment. While dolomite undergoes beneficial structural modifications enhancing porosity and permeability, calcite exhibits the degradation of the pore network through mineral reprecipitation and occlusion. This distinction is crucial for evaluating the petrophysical suitability of carbonate rocks in geological hydrogen storage, with dolomite emerging as a more promising candidate than calcite.
# Beach Deployment of a Low-Cost GNSS Buoy for Determining Sea-Level and Wave Characteristics
## 3 Results and Interpretation  
The raw data were downloaded via Wi-Fi using the set-up mentioned in Section 2.1; this was tested at around 250 m range to demonstrate that data could be downloaded with the buoy in the water. If deployed further out towards the low water mark it could be free-floating at times during the spring-neap tidal cycle (e.g., during neap tidal cycles). Data were recorded from 14:10 on 28 August until 15:25 on 29 August over two tidal cycles. The data were subdivided according to when the buoy was free-floating during the tidal cycle such that:  
* tidal cycle A refers to data between 18:00 on the 28 August 02:00 on the 29 August.
* tidal cycle B refers to data between 06:00-14:30 on the 29 August.
# Practical Work of Three-Phased Induction Motor Modeling in a Classical Spreadsheet Software
## 5 Practice
### Exercise 5  
A four-pole (\(p=2\)) three-phase induction motor, whose stator windings are wired in star (\(\gamma\)), is supplied by a three-phase line 230 V/400 V, 50 Hz. The following tests were performed:  
* At No-load test (\(s=0\)), the measures performed gave \(U_{0}=400\) V; \(I_{0}=15\) A, and \(P_{0}=300\) W.
* At a blocked rotor test (\(s=1\)), with a reduced phase-to-phase voltage \(U_{1}=90\) V, the measurements were \(I_{1}=100\) A and \(P_{1}=2.1\) kW.
1. From the no-load test, determine the values of \(R_{I}\) and \(X_{m}\).
2. From the blocked rotor test, determine the reactive \(Q_{1}\) power dissipated in \(R\) and \(l\omega\). Then, calculate \(R\) and \(l\omega\), given that \(i_{1}\approx i_{2}\) in a blocked rotor test.  
For a slip \(s=0.08\), calculate the following:  
\[\begin{array}{ll}3.&\text{The RMS value $I_{2}$ of $i_{2}$.}\\ 4.&\text{The power transmitted to rotor $P_{tr}$.}\\ 5.&\text{The rotor Joule losses $P_{IR}$.}\\ 6.&\text{The total mechanic power $P_{M}$.}\\ 7.&\text{The rotor rotational speed $\Omega_{R}$.}\\ 8.&\text{The electromagnetic torque $T_{em}$.}\end{array}\]