esa-sceva/satcom-chunk-collection huggingface dataset:
# The NEWA Ferry Lidar Experiment: Measuring Mesoscale Winds in the Southern Baltic Sea
## 6 Outlook  
First comparisons show a good correlation of the measurement data from the NEWA Ferry Lidar Experiment recorded using a ship-mounted lidar device with mesoscale model data simulated using the WRF model and mapped onto the ship's route in time and space. This opens up promising possibilities, overcoming the difficulty of verifying mesoscale model output data against in-situ data of, e.g., met masts that represent a very small and fixed volume only. Data of a ship lidar system  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Horizontal Wind Speed [m/s]**} & \multicolumn{2}{c}{**Wind Direction [\({}^{\circ}\)]**} \\ \cline{2-5}  & **4-Day Period (25-28 February 2017)** & **All Data** & **4-Day Period (25-28 February 2017)** & **All Data** \\ \hline Bias & 0.09 & 0.35 & \(-\)5.2 & \(-\)6.3 \\ \hline \(RMSE\) & 1.31 & 1.90 & 8.3 & 29.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Bias and RMSE values for the deviations in horizontal wind speed and wind direction between the Ferry Lidar and WRF data.  
Figure 9: Comparison of the measured and simulated (Ferry Lidar vs. WRF) time series of horizontal wind speed and direction, here for 100 m measurement height only.  
could instead be representative of a line covering a certain space that is similar to the resolution of a mesoscale model simulation for a respective time scale. Comparing the time series of an NWP model with the data trajectories of the measurement system moving in space, and finding good correlations, may increase the trust in the model data and further promote their use as primary data for a wind resource assessment where on-site measurements are otherwise challenging. Studying the correlations between the two data sources along well-defined tracks may help to assess and understand where NWP models show larger and possibly systematic discrepancies.  
The comparisons presented in this contribution represent only a first step. Within the NEWA project, the data of the Ferry Lidar Experiment are provided as a basis for more detailed benchmark studies that are planned and currently prepared for the near future. Furthermore, apart from that, the measurement data are part of the open accessibly NEWA Experimental Database, which will be available to the broader public for manifold investigations after the end of project, i.e., from May 2019.
## 2 Materials and Methods
### Statistics  
The primary endpoint for sample size calculation was based on an estimated correlation value between manual and photogrammetry-derived measurements of a conservative R = 0.5, resulting in an estimate of 75 subjects including 20% of drops. Reliability across methods was assessed with the two-way random effects, consistency, and multiple rater ICC [13], with thresholds defined as <0.90 = good, 0.90-0.95 = excellent, >0.95 = optimal. Agreement was assessed with several performance metrics: proportions of values with a deviation within a prespecified limit (difference considered clinically acceptable of \(\pm\)1 cm), standard error of measurement (SEM) that reflects the random error, CV to compare variability both between the methods and with respect to a reference of 0.1-20.9 [1], and Bland-Altman analysis (mean bias, limits of agreement \(\pm\)1.96 standard deviations, detection of outliers). Proportional bias was checked to test whether errors systematically increased with increasing measurement size. Analyses were performed using the R programming language (version 2024.04.2 + 764) by means of irr (version 0.84.1), dplyr (version 1.1.4), and ggplt2 (version 3.5.1) packages.
# The Potential Therapeutic Role of Bruton Tyrosine Kinase Inhibition in Neurodegenerative Diseases
## 3 Bruton Tyrosine Kinase's Inhibitors in Neurodegenerative Disease  
Neuroinflammation in neurodegenerative diseases is characterized by chronic microglial activation, elevated production of pro-inflammatory cytokines and ROS, and sustained activation of transcription factors like NF-kB. BTK acts as a proximal transducer of these signals, linking receptor engagement to the activation of pathways that promote cytokine transcription, inflammasome assembly (notably NLRP3), and phagocytic responses [11]. In particular, when BTK is inhibited, NLRP3 inflammasome assembly is disrupted, reducing caspase-1 activation and subsequent maturation of IL-1B and IL-18. In this context, BTK inhibitors exert their effects by blocking BTK's kinase activity, thus dampening the amplification of detrimental neuroinflammatory loops. Evidence of this, for instance, can be found in preclinical studies that have shown BTKi-dependent reductions in microglial pro-inflammatory markers in models of AD and MS, promoting a microglial shift toward neuroprotective phenotypes [12; 43]. Taken together, these data underscore the potential of BTK inhibitors to act as disease-modifying agents in neurodegenerative disorders, not merely through immunosuppression but by reprogramming maladaptive neuroimmune interactions. Their pleiotropic effects on microglial biology, cytokine signaling, and inflammasome regulation position BTKis as a targeted approach to mitigate neuroinflammation and its downstream neurodegenerative consequences.
# Condition Monitoring of Industrial Equipment Based on Multi-Variables State Estimate Technique
## 5 Validation and Discussion  
### Validation of Blower Fan MSET Modeling  
The sample solution of data is 20 seconds, and the variables we discussed in Section 4 is available, which are vibration of horizontal \(x_{1}\), vibration of vertical \(x_{2}\), the temperature of end-side bearings \(x_{3}\), current air volume of unit \(x_{4}\), operation frequency of driving motor \(x_{5}\), current of driving motor \(x_{6}\), respectively. The typical distribution for normal data in 10/25/2015 is shown in Figure 4, the maximum  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Situation**} & **RMSE (mm/s)** & **MAPE (\%)** \\ \hline \multirow{2}{*}{Horizontal vibration \(x_{1}\)} & Proposed method & 0.0045 & 0.45 \\ \cline{2-4}  & Traditional method & 0.0265 & 2.12 \\ \hline \multirow{2}{*}{Vertical vibration \(x_{2}\)} & Proposed method & 0.0017 & 0.11 \\ \cline{2-4}  & Traditional method & 0.0177 & 2.79 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of RMSE and MAPE for condition I.  
of vibration in horizontal and vertical direction are 2.725 mm/s and 5.195 mm/s, respectively. In totally, 7 days normal samples were used for contrasting memory matrix \(D\).  
After using the method mentioned in Section 4, memory matrix \(D\) is constructed, and 211 vectors are concluded, therein, 11 vectors are selected by extreme condition of 6 variables and remove duplication, and the rest 200 vectors are obtained by method mentioned in Figure 3. Consecutive normal samples, including steady and dynamic working condition data, are used for validating the accuracy of MEST model.
# Occlusion-Robust Multi-Target Tracking and Segmentation Framework with Mask Enhancement
## 3 Methodology
### Mask-Conditioned Feature Fusion Network
#### 3.1.2 Adaptive Feature Fusion Sub-Module  
Studies [37; 40] also show that directly removing the background with a mask not only degrades person re-identification's performance but also tends to degrade MOT tasks' performances entirely, since directing the network's attention with incorrect segmentation instances will impact tracking results negatively.  
The Adaptive Feature Fusion Sub-Module is an intelligent component that combines foreground and background features and dynamically adapts to masks' quality changing conditions. Based on the dual-stream features \(F_{fg}^{t}\), \(F_{bg}^{t}\) generated from the aforementioned encoder, this module implemented a fusion mechanism through the following steps.  
The process began with a quality evaluation of the mask. For foreground feature \(F_{fg}^{t}\) in each frame, the module provided a quality scoring mechanism through a lightweight evaluation network. The output was a quality-scoring \(\alpha_{t}\in[0,1]\) according to the following:  
\[\alpha_{t}=\sigma(W_{2}\text{ReLU}(W_{1}[\text{GAP}(F_{fg}^{t})\oplus\mathcal{ Q}(M_{t})])) \tag{2}\]  
where \(\mathcal{Q}(M_{t})\) represents a mask quality index that includes boundary sharpness and area consistency between frames, while \(W_{1}\) and \(W_{2}\) are the learnable weights. The sigmoidal activation \(\sigma(\cdot)\) guaranteed a normalized quality score. The quality scores led to an adaptive fusion of foreground and background features through a gating mechanism as follows:  
\[F_{fused}^{t}=\alpha_{t}\cdot F_{fg}^{t}+(1-\alpha_{t})\cdot\text{SEBlock}(F_{bg }^{t}) \tag{3}\]  
The Squeeze and Excitation Block (SEBlock) plays a crucial role in the fusion process. It performs per-channel feature recalibration. This design allows the module to emphasize reliable features and suppresses noise components. When the quality of the mask is high (\(\alpha_{t}\to 1\)), foreground features are dominant. When the mask is unreliable (\(\alpha_{t}\to 0\)), enhanced background features are dominant. The output \(F_{fused}^{t}\) of this module maintains the same dimension as the input features. It achieves temporal consistency through frame-by-frame adaptive fusion.
# Fuzzy Logic-Based Controller for Bipedal Robot
## 6 Simulation Results
### Simulation of Fuzzy Controller
In a similar way, results are shown for the simulation of the controllers for the bipedal robot at a steady walk, Figure 16, and walk to a stop state, Figure 17.  
Figure 16 shows the simulation results of controllers for a bipedal robot at a steady walk, during which the horizontal velocity of the robot body was almost constant. The calculation results obtained the average error between the desired joint coordinate value and the actual coordinates at the step in the steady period, which is also in the range of \(\sim\)10\({}^{-3}\) radians. The steady step simulations were similar to those of the starting step, including three graphs. The graph can be observed to see the accuracy and reliability of the controllers. The fuzzy controller can be as precise as the controller that is based on the exact dynamic model IDPD. The graph observation shows that the IDPID controller based on the dynamic model including noise was slightly less accurate. Figure 15: Simulation results of controllers for bipedal robots at the starting step: (**a**) IDPD controller; (**b**) IDPID controller; (**c**) fuzzy controller. Figure 16: _Cont._  
Figure 17 shows the controller simulation results for the bipedal robot at the last step when it comes to a stationary state. From the simulation results, it can be seen that:  
The IDPD controller was applied with the assumption that the robot dynamics model was accurate, so it gave accurate results. Even though the IDPID controller had an element to limit and eliminate the disturbance force, the control results still have less accuracy. The fuzzy controller with the proposed fuzzy law allowed accurate results to be obtained. Figure 16: Simulation results of controllers for bipedal robots at the steady step: (**a**) IDPD controller; (**b**) IDPID controller; (**c**) fuzzy controller. Figure 17: _Cont._  
The calculation results show that, during stable motion at all three stages of motion, the average error between the desired joint coordinate value and the actual coordinate was \(\leq\)10\({}^{-3}\) radians. As stated, it is practically impossible to determine the exact dynamics model. On the other hand, IDPD and IDPID controllers had to perform computations of cumbersome and complex mathematical expressions of the robot's dynamic model. When there are many abnormal and complex disturbances, it is more difficult for an IDPID controller to ensure control accuracy. The calculation expressions based on the fuzzy rule system and fuzzy inference are very simple and easy.
# AI-Supported EUD for Data Visualization: An Exploratory Case Study
## Discussion
### Design Guidelines for AI-Supported EUD for Data Visualization
#### 5.2.2 Practical Implications  
**Integrate multimodality in the AI interface.** Regarding the system's capability, potential, and limitations, natural language must be complemented with a suitable graphical user interface that guides users in the system usage and allows them to deepen data analysis without additional prompts that could be difficult to formulate. This integration should also include a more guided interface with clearer suggestions on how to formulate requests and more detailed explanations of available data viz types and their best use cases. This integration would also improve the usability of the system. More transparent system behaviors (also exploiting visual feedback) must be studied to avoid users having to check for output correctness through other tools.  
**Support advanced and iterative request refinements.** As emerged in the experiment regarding the trial-and-error behaviors of all three user profiles, and the necessity to address misunderstandings, an LLM-based EUD environment for data viz should provide advanced capabilities for iterative refinement of requests, supported by informative feedback about what the system \"knows\" and can do.  
**Provide support for complex data viz requests.** On the one hand, there is a need to support intermediate and advanced users in the creation and refinement of complex data viz to support complex analytical tasks effectively. This complexity may be supported, e.g., by providing a suitable display of dataset attributes within the interface, giving AI-based suggestions about the queries that are feasible with certain data or the data viz types that could be obtained with them. On the other hand, the correct interpretation of complex queries formulated by the user should be guaranteed by the system.  
**Customize systems.** Customization features may include, for example, interactive tutorials for first-time users; enhanced text interpretation, potentially through autocompletion; refinement feedback, ensuring readability and comprehension of visualizations; and deeper numerical insights, with incremental details upon the user's demand, showing trend-based insights, numerical evidence, and clear indication of interactivity of charts, whenever available, through explicit indications.  
**Leave the control to the user.** Readability remains a priority, as it allows users to exercise full control of visual attributes, the order of displayed elements, and of 2D vs. 3D visualizations. Transparency in all the phases of the data lifecycle is a strong expectation. In particular, the more complex the generated SQL query, the higher the need to provide a design allowing full control of the further refinements, processing, and display of the data.
## 4 Integrated Approach for QoS Assurance in IP/Optical Networks
### Automation of Connectivity Adaptation and Service Virtualization Based on the Service Plane  
Another function of the service plane is to automate the connectivity adaptation and service virtualization necessary to handle complex services, such as a mesh of VPNs, prior to requesting the provisioning of connectivity that can be \"understood\" by the GMPLS control plane, e.g., lightpaths or Ethernet bandwidth. This adaptation and virtualization can be performed in single- and multi-layer optical networks. This will be achieved in the ADRENALINE Testbed(r) using the reconfigurable wavelength-routed network functionalities and applying or not the connection-oriented Ethernet technologies on top. Here, the key element of our approach is the SDE, which is based on the adaptive multi-service optical network architecture proposed by [PERSON] _et al._ in [21]. The service database integrated in the SDE includes translation strategies from composite services, e.g., VPN, into provisioning requests to the GMPLS control plane in charge of establishing and deleting connections, either lightpaths or Ethernet bandwidth. These translation strategies contribute to the common goal of achieving adaptive service provisioning through the accomplishment of service customization.  
Figure 5 illustrates the rationale behind adaptive decomposition of complex services. Figure 4(a) depicts the advanced reservation service, which is similar to what human operators can do using suitable interfaces or what IP routers do using the UNI interface in ASTN [8]. Axis N represents nodes in the service plane, in this case in a distributed architecture (_i.e._, SCE and SDE elements in different service-plane nodes). Axis n represents control plane nodes, while axis t represents the time. A user sends a request to service plane node N0 at t0 to reserve a lightpath between n1 and n2 from t1 to t2.  
Figure 5: Adaptive decomposition of complex services. Extracted from [21].  
N0 (SDE element) analyses the request and maps it into four base service functions: the network information service (SF0), the network monitoring predictions service (SF1), the request transfer service (SF2) and the advanced connectivity service (SF3), which are plotted in Figure 5b. These functions match those depicted in the SCE and SDE elements illustrated in Figure 4. Note that the process flow is shown by a red solid arrowed line in Figure 5a, from steps (1) to (4).  
Because of the diversity of service logics, the focus of the proposed approach is on decomposing complex services into lower-order services, _i.e._, basic services that will translate directly into provisioning requests to the GMPLS control plane and will focus on a set of complex services. This involves, besides the SDE, the components 'Service Logic' and 'Lightpath Establishment/Deletion Requester' of the SCE depicted in Figure 4. Note that the latter component incorporates IA-RWA into the establishment request process implicitly by means of impairment-constrained based routing integrated in the GMPLS control plane, which is an implementation of IA-RWA techniques [22].
## 5 Biomarkers of Response and/or Resistance to Immune Checkpoint Inhibitors
### Markers of Response and/or Resistance in Tumor Tissue
#### 5.2.3 Tumor-Infiltrating Lymphocytes and Other Immune Cells  
It is commonly believed that ICIs, in particular anti-PD1 and anti-PD-L1, act by reinvigorating the pre-existing tumor immune response [108; 109; 110]. Therefore, the density of TILs within a tumor may be another potential predictor of the clinical effectiveness of this treatment. Such an assumption is further supported considering that, in some tumors, TILs density is a strong positive prognostic indicator regardless of ICIs therapy [111]. Moreover, not only the simple evaluation of TILs density, but also TILs phenotype and lymphocytes localization within a tumor, may provide prognostic information. Indeed, in patients treated with anti-PD-1, TILs density measured by IHC at the invasive margin of the tumor, and not central infiltration, was strongly associated with the response [109]. Even in HCC patients, tumor T cell infiltration within the tumor is predictive of response [34; 66].  
Beyond lymphocytes, many other immune cells infiltrating the tumor may affect the efficacy of ICIs. As already mentioned, HCCs with increased T-cell infiltration and higher expression of cytotoxic markers, immune-cell homing genes (CXCL9 and CXCL10), and IFN\(\gamma\) signaling (immune-active tumors) show a better response to anti-PD-1 monotherapy [34; 61]. By contrast, some immune traits, such as the infiltration of CD4\({}^{+}\) regulatory T cells or immunosuppressive macrophages (M2 vs. M1 polarization) and increased stromal activation with increased cancer-associated fibroblasts [34] are associated with resistance to immunotherapy. A recent study evaluated the ability of baseline serum concentrations of 59 cytokines and pretreatment tissue 11 immune cell markers to predict the clinical benefit in 33 patients treated with sentilimab + bevacizumab [66]. Patients with a high density of proinflammatory (M1) macrophages in the tissue and high serum concentration of CD137 at the baseline obtained a clinical benefit from the combination, defined as disease control at 12 weeks. In particular, a high density of M1 macrophages in the baseline tumor microenvironment was associated with better OS and PFS [66]. Despite being interesting, these results should be interpreted with caution, since the definition of clinical benefit used in this study is not considered a strong endpoint, and the proposed markers require further validation in independent and larger patient cohorts.
## 3 Results and Discussion
### Single-Cell Oil (SCO)
#### 3.4.5 Morphology of SCO within the Yeast Cells  
The yeast cells were observed by live fluorescence microscopy after BODIPY\({}_{493/503}\) staining (Figures 10 and 11). It can be observed for both yeasts that when the cells were grown on acetic acid, the lipid droplets were bigger and more spherical compared to the lipid droplets formed when the cells were grown on glucose. They were also more uniformly dispersed in the cells, forming one or a maximum of two central big lipid droplets. When the C/N ratio increased from 50 to 100, the lipid droplets seemed to be slightly bigger, however, the change was not that significant, even though there was an increase in the lipid content and concentration. When the cells for both yeast species were grown in propionic acid, it can be noted that they form smaller and more numerous lipid droplets that are dispersed within the cells. _R. toruloides_ cells grown on propionic acid are bigger compared to the cells grown in glucose, while _C. curvatus_ cells seem to be more oval-shaped when grown on propionic acid, compared to the more round-shaped cells when the yeast is grown on glucose, but similar in size. When butyric acid was used as a substrate it can be observed that _R. toruloides_ formed a low number of lipid droplets, which are also very small in size. The size of the cells was also smaller than when the yeast grew on glucose, verifying that butyric acid is not a suitable substrate for _R. toruloides_. On the other hand, when _C. curvatus_ grew on butyric acid, a high number of lipid droplets can be observed, which have a similar shape to the lipid droplets formed on propionic acid. When VFAs effluent was used as a substrate, the formed lipid droplets were small in size but very abundant for both yeasts. In addition, the cell size was rather big and their morphology resembled a combination of the morphology obtained with individual VFAs. The above observations indicate that morphological changes that occur in the cells are species and substrate-dependent.  
Figure 10: Morphological analysis of the cells and lipid droplets when _R. toruloides_ was cultivated on synthetic and BSG-derived VFAs, as well as glucose. The cells were stained with the lipid dye BODIP\({}_{493/53}\). Scale bars correspond to 50 μm.