esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix B The Gradient Vector and Fisher Information Matrix  
The gradient vector of \(\ell(\mathbf{\beta},\alpha)\) with respect to \(\mathbf{\beta}\) and \(\alpha\) are given by  
\[\frac{\partial\ell(\mathbf{\theta})}{\partial\mathbf{\beta}} = \sum_{i=1}^{n}\biggl{[}1+\frac{\mu_{i}(y_{i}-1)}{\mu_{i}+(\alpha- 1)y_{i}}-\frac{\mu_{i}}{\alpha}\biggr{]}\mathbf{w}_{i},\] \[\frac{\partial\ell(\mathbf{\theta})}{\partial\alpha} = \sum_{i=1}^{n}\biggl{[}\frac{(y_{i}-1)y_{i}}{\mu_{i}+(\alpha-1) y_{i}}-\frac{y_{i}}{\alpha}+\frac{\mu_{i}-y_{i}}{\alpha^{2}}\biggr{]}.\]  
The Hessian matrix is  
\[H(\mathbf{\theta})=\left(\begin{array}{cc}\frac{\partial^{2}\ell(\mathbf{\theta})}{ \partial\mathbf{\beta}\partial\mathbf{\beta}^{\dagger}}&\frac{\partial^{2}\ell(\mathbf{ \theta})}{\partial\mathbf{\beta}\partial\alpha}\\ *&\frac{\partial^{2}\ell(\mathbf{\theta})}{\partial\alpha^{2}}\end{array}\right),\]  
where  
\[\frac{\partial^{2}\ell(\mathbf{\theta})}{\partial\mathbf{\beta}\partial \mathbf{\beta}^{\dagger}} = \sum_{i=1}^{n}\biggl{\{}\frac{(\alpha-1)\mu_{i}(y_{i}-1)y_{i}}{[ \mu_{i}+(\alpha-1)y_{i}]^{2}}-\frac{\mu_{i}}{\alpha}\biggr{\}}\mathbf{w}_{i}\mathbf{w }_{i}^{\top},\] \[\frac{\partial^{2}\ell(\mathbf{\theta})}{\partial\alpha^{2}} = \sum_{i=1}^{n}\biggl{\{}-\frac{(y_{i}-1)y_{i}^{2}}{[\mu_{i}+( \alpha-1)y_{i}]^{2}}+\frac{y_{i}}{\alpha^{2}}-\frac{2(\mu_{i}-y_{i})}{\alpha^{3 }}\biggr{\}},\] \[\frac{\partial^{2}\ell(\mathbf{\theta})}{\partial\mathbf{\beta}\partial\alpha} = \sum_{i=1}^{n}\biggl{\{}-\frac{(y_{i}-1)y_{i}\mu_{i}}{[\mu_{i}+( \alpha-1)y_{i}]^{2}}+\frac{\mu_{i}}{\alpha^{2}}\biggr{\}}\mathbf{w}_{i}.\]  
The Fisher information matrix is given by  
\[\mathbf{I}(\mathbf{\theta})=-E\bigl{[}\mathbf{H}(\mathbf{\theta})\bigr{]},\]  
where  
\[E\biggl{[}\frac{\partial^{2}\ell(\mathbf{\theta})}{\partial\mathbf{\beta }\partial\mathbf{\beta}^{\dagger}}\biggr{]} = -\sum_{i=1}^{n}\biggl{\{}\frac{\mu_{i}^{2}+2\alpha(\alpha-1)\mu_{ i}}{[\mu_{i}+2(\alpha-1)]\alpha^{2}}\biggr{\}}\mathbf{w}_{i}\mathbf{w}_{i}^{\top},\] \[E\biggl{[}\frac{\partial^{2}\ell(\mathbf{\theta})}{\partial\mathbf{\alpha }^{2}}\biggr{]} = -\sum_{i=1}^{n}\biggl{\{}\frac{2\mu_{i}}{\alpha^{2}[\mu_{i}+2( \alpha-1)]}\biggr{\}},\] \[E\biggl{[}\frac{\partial^{2}\ell(\mathbf{\theta})}{\partial\mathbf{ \beta}\partial\alpha}\biggr{]} = \sum_{i=1}^{n}\biggl{\{}\frac{2(\alpha-1)\mu_{i}}{\alpha^{2}[\mu_ {i}+2(\alpha-1)]}\biggr{\}}\mathbf{w}_{i}.\]
# Exploring the Effect of Virtual Environments on Passive
## 2 Related Works
### Haptics  
In the field of robotics, many studies have been introduced on sensory design for grasp control and manipulation of real objects with human-hand-like motions [15]. In a few more recent results, researchers have introduced haptic devices and rendering. Haptic technologies have a long history of use in many pieces of research such as a dental training simulator [16]. Haptics is a field of study mainly about the sense of touch, and enables the feeling of simulated objects via an interactive method depending on the physics through a haptic interface [17]. Immersive VR systems typically focus on visual and auditory feedback; to provide more interactive content, haptics provide additional feedback to participants. There are two types of haptics methodologies: passive and active haptic. Passive haptic for natural interaction is a method where real objects that resemble virtual objects provide feedback naturally through the sense of touch of their tactile properties such as contact location, pressure, vibration, and temperature [4]. In contrast, active haptic creates forces through haptic equipment with motor-controlled actuators to provide kinesthetic perception [18]. More recently, many researchers have been concerned about haptic rendering to compute the correct interaction forces for virtual objects [17]. For example, there have been a few attempts to make collision detection algorithms to find penetrations and the contact area, as well as force response algorithms to create return values with respect to applicable forces in the collision state [17]. However, in haptic research results, it remains to be determined how passive haptics should be configured to have a high level of presence in the virtual environment to enhance the effect.
# Retinal Vessels Segmentation Techniques and Algorithms: A Survey
## 1 Introduction  
Retinal vasculature structure implicates important information helps the ophthalmologist in detecting and diagnosing a variety of retinal pathology such as Retinopathy of Prematurity (RoP), diabetic retinopathy, glaucoma, hypertension, and age-related macular degeneration or in diagnosis of diseases related to brain and heart stocks, which are associated with the abnormal variations in retinal vascular structure. Therefore, changes in retina's arterioles and venules morphology have a principal diagnostic value.  
In general, vessels (vessels structure-like) segmentation occupy a remarkable place in medical image segmentation field [1; 2; 3; 4]; retinal vessels segmentation belongs to this category where a broad variety of algorithms and methodologies have been developed and implemented for the sake of automatic identification, localization and extraction of retinal vasculature structures [5; 6; 7; 8; 9; 10]. In this paper, we have presented a review that covers and categorizes early and recent literature methodologies and techniques, with the major focus on the detection and segmentation of retinal vasculature structures in two-dimensional retinal fundus images. In addition, our review covers the theoretical basis behind each segmentation category as well as the associated advantages and limitations.  
The remainder of this paper is organized as follows. Section 2 describes the different types of retinal fundus imaging. The different challenges that faced by researcher during retinal images segmentation are elaborated in Section 3. Detailed description of different categories of vessel segmentation methodologies and algorithms along with theoretical framework is given in Section 4. Finally, Section 5 summarizes the discussion and conclusions of this work.
## 3 Results
### Characterization of Mesoporous Silica
#### 3.1.5 FT-IR Spectra  
Figure 7 shows the FT-IR patterns for the functionalized and non-functionalized materials covering a range from 4000 to 400 cm\({}^{-1}\). The functionalized materials show the characteristic bands of the organic ligands attached to the material. The peaks observed in the spectra at 1620 cm\({}^{-1}\) and 3400 cm\({}^{-1}\) have been attributed to OH vibration and stretching vibrations of physisorbed water. Also, a weak peak is observed at 3640 cm\({}^{-1}\) associated with the surface hydroxyl groups involved in forming hydrogen bonds with other neighboring silanol groups. The intensity of these signals is reduced in the functionalized materials because of the decrease in the number of free silanol groups. The presence of sulfonic groups in functionalized mesoporous silica is confirmed by the identification of bands characteristic of the ligands, like deformation vibration bands of -CH\({}_{2}\) detected at 1447 cm\({}^{-1}\) due to the alkyl chains of the ligand. The peaks located around 1410 cm\({}^{-1}\) correspond to S=O stretching vibrations present in undissociated acidic sulfonic groups, while the peak at 1344 cm\({}^{-1}\) is attributed to stretching vibrations of -SO\({}_{3}\)\({}^{-}\) species. The spectra also can be identified as characteristic bands for aliphatic stretching vibrations (C-H) of pendant alkyl chains located around 3000-2800 cm\({}^{-1}\). In addition, bands corresponding to the Si-O vibration at 790 cm\({}^{-1}\), the symmetric Si-OH vibration at 950 cm\({}^{-1}\), and a large band between 1000 and 1240 cm\({}^{-1}\) attributed to the asymmetric Si-O-Si vibrations are distinguished [38]. The greater intensity of these bands in the functionalized MCM-41 is a consequence of the higher degree of functionalization that this material presents compared to MSU-2.  
Figure 6: Nitrogen adsorption–desorption isotherms of (**a**) bare MCM-41 and MSU-2 and (**b**) functionalized MCM-41-SO\({}_{3}\)H and MSU-2-SO\({}_{3}\)H. Pore size distribution curves of (**c**) bare MCM-41 and MSU-2, (**d**) functionalized MCM-41-SO\({}_{3}\)H and MSU-2-SO\({}_{3}\)H.
## 3 Tidal Characteristics of DS0 and DW2
### Latitude-Time Variations  
Tidal amplitudes of DS0 and DW2 from COSMIC at 30 km and from CMAM30 and ERA temperature data at 10 hPa are compared to understand the horizontal structure of their variability. The amplitudes of DS0 are shown in the top three rows of Figure 7 and those of DW2 are shown in the bottom three rows. In COSMIC data, a maximum amplitude of 0.4 to 0.6 K at \(\pm 10^{\circ}\) latitudes is observed during summers. Larger amplitudes reaching values of 2 K are observed during winters. In CMAM30 and ERA data, the tropical band-like structures with an amplitude of 0.5 to 0.6 K at \(\pm 10^{\circ}\) are also observed for DS0. The amplitudes of ERA are slightly larger in comparison, reaching 0.8 K. However, there are no significant amplitudes seen over winter at high latitudes, as observed in COSMIC. Additionally, in the Southern Hemisphere, a third band-like structure is seen in CMAM30 and ERA, around \(35^{\circ}\) S during summer, which gradually extends into the  
Figure 6: Altitude versus latitude cross-section of mean amplitude and phase of DW2 tide during August 2008 and February 2009 from COSMIC, CMAM30, and ERA-interim data.  
high-latitude region by the September/October months. No such feature is seen in the Northern Hemisphere. It is also not seen in COSMIC observations in both hemispheres.  
DW2 tidal amplitudes also show a similar structure with two tropical bands over the \(\pm 10^{\circ}\) latitudes. However, one of the bands is stronger in a given period. During boreal winter, the band in the Northern Hemisphere is stronger in COSMIC and CMAM30, while both bands are of almost similar amplitudes in ERA-Interim. During boreal summers, DW2 amplitudes are much smaller. Similar to DS0, the DW2 amplitudes are larger, reaching values of 2 K over winter high-latitude regions in COSMIC, but are negligible in CMAM30 and ERA.  
Figure 7: Latitude versus time variation of amplitudes of non-migrating tides, DS0 (in **top** three panels), and DW2 (in **bottom** three panels) at 30 km from COSMIC observations, and at 10 hPa from CMAM30 and ERA-interim data obtained using a 21-day window.
# Terahertz Inspection of Buildings and Architectural Art
## 2 Terahertz Uses in Architectural Art
### Clay Decorations  
In 2017, a study of the deterioration state of glazed terracota medallions from a XVI century house in Luneburg, Germany, as shown in Figure 3a,b using THz ToF was reported [36]. These architectural decorations were common at that time in Germany. The clay decorations, which were glazed with a colour and varnish layer have undergone weather related deterioration for over 400 years, showing the delamination of the glazing layer, which gives them their bright and colorful appearance. The study successfully reports cross-sectional images of the delaminated layers, and determination of the dimensions of the underlying air gaps, as shown in Figure 3c, this was valuable for the restoration process of such objects.  
Figure 2: Panels (**a–d**) are the reflected terahertz pulses from various locations on the medallion, the positions that correspond to each measurement are marked on panel (**e**). It is easy to see that the area where points (**a–c**) lay presents an internal air gap that generates an echo 35 ps to 45 ps from the surface, which corresponds to a depth of 2.3 mm to 3.4 mm from the surface. The measurement at point (**d**) shows that the stone is solid and has no internal structure at that position [34].
## 4 Discussion
[59] emphasized the role of leg dominance in motor behavior during balance-challenging tasks, recommending its consideration in neuromuscular control assessments. The elevated MG involvement may reflect the high coordination and stabilization demands of this exercise. While the elevated MG involvement may suggest increased stabilization demands in unilateral FROM HREs, this interpretation should be considered speculative and context-dependent, given the lack of direct kinematic or coordination assessments. Several study design factors limit the results' generalizability. The participants were moderately active young adults, experienced in HREs and free from musculoskeletal impairments affecting MG or LG activation. Generalization to older adults or individuals with a gastrocnemius pathology requires further investigation. Activation strategies varied substantially across individuals, as evidenced by large standard deviations. As all HREs were performed unweighted, outcomes may differ under loaded conditions. Future research should examine the effects of external loads and surface inclination on muscle activation. The present findings have several important applications. LG and especially MG activation during unilateral weight-bearing HREs was comparable to the maximal EMG during sprinting, supporting its use in athletic training and late-stage lower-leg rehabilitation. In this context, the effectiveness of eccentric exercises in managing gastrocnemius tightness has been demonstrated by studies such as that by [PERSON] et al. [60], who observed that stretching and eccentric exercise (Stanish protocol) successfully normalized gait parameters, reducing compensatory knee flexion in individuals with isolated gastrocnemius tightness. HREs are also employed to enhance gastrocnemius hypertrophy and strength. The MG and LG showed functional differences depending on ankle ROM and exercise type. MG activation increased consistently with the ROM, whereas LG activation depended on dorsif flexion and execution. Additionally, LG activation during unilateral FROM HREs varied with limb dominance, which is relevant for assessing muscular asymmetries. These distinctions should guide individualized rehabilitation programming. The differential activation patterns of the medial and lateral gastrocnemius observed during FROM-based unilateral heel raise exercises have meaningful clinical implications. Targeted loading of the medial head may benefit post-operative rehabilitation following Achilles tendon rupture or medial calf strain, particularly given its high involvement and injury incidence in athletes [13]. Furthermore, unilateral FROM HREs may contribute to functional retraining in individuals recovering from anterior cruciate ligament (ACL) reconstruction and stroke-related gait asymmetries, where selective activation and ankle control are essential. Implementing region-specific gastrocnemius stimulation strategies may support hypertrophic adaptations, enhance postural stability, and assist in late-stage return-to-sport decision-making.
# Improvements in Rock Mass Description for Stope Design by Geophysical and Geochemical Methods
## 3 Industry Survey on Stope Design
### Survey Results and Discussion
Respondents ranked cutoff grade and dilution as the key parameters affecting stope design. Development costs, equipment size, deposit recovery, and stope stability were also identified as important factors, along with productivity, ventilation, mining cost, sequencing, drilling and blasting, flexibility in mine planning, rock mass, labor efficiency, the selective mining unit (SMU), and safety. Almost all respondents (97%) reported that they monitor stope stability, with cavity monitoring systems (CMS) being the most used method (77%). Other methods included planned and mucked ton comparison, visual inspections, seismic controls, borehole extensometers, and laser scanning with point cloud data. The most urgent improvements for stope design included additional geotechnical data (20%), improvements in software (18%), and integration into general mine planning (17%). Respondents also suggested improvements in speed, automation, backfill predictability, implementation of more reliable grade data, a variable cutoff, and reliable performance indication for dilution cable bolt utilization. Rock mass investigations were reported to be utilized by 45% of respondents, with most using geomechanical methods (73%) and some using geophysical (14%) and geochemical (13%) investigations. Commonly used geomechanical evaluation methods included RQD, RMR, MRMR, the Q-system, the LOM stress model, deformation monitoring, core logging, and ELOS charts. Geophysical methods included optical and acoustical televiewer and gravimetric testing. Geochemical methods mostly used atomic absorption spectroscopy and fire assay. More than half of the respondents expressed a willingness to implement novel methods in rock mass characterization and change the stope design process. However, the availability of additional data from the site within a reasonable timeframe was identified as a key issue for the use of new technology. More than 70% of respondents stated that additional data available within three days would satisfy their needs, but some were willing to wait longer if the data added substantial value to the stope design process. Machine and deep learning approaches were suggested as potential solutions to meet these requirements due to their speed, automation, and data utilization capabilities.
## 5 Conclusions  
In this study, an integrated fault recognizer based on SDAE is presented to denoise and extract features from the raw vibration signals. After the pre-training of each layer with the unsupervised learning method, the BP method is applied to train parameters of the whole model [45].  
The proposed method is validated by two datasets, namely, rolling bearing datasets and gearbox datasets. The results of the experiments demonstrate the superiority of the SDAE model to other fault diagnosis methods, such as DBNs, particularly in the mixed noise situations. The proposed model achieves a high degree of accuracy because of its denoising function and offers an automatic feature extraction procedure that is practical and convenient for the application in rotating machine fault diagnosis.  
In general, the proposed method has the following advantages.  
* The proposed method can adaptively extract useful features from raw signals, which is substantially intelligent and has high recognition accuracy.
* The application of the deep fault recognizer to fault diagnosis has high generalization ability because of its unsupervised pre-training. This method also solves the problem where several labeled vibration signals for training are inaccessible.
* This method can be used independently because of its denoising layer. The proposed method is also more concise than the other traditional fault diagnosis methods, which combines several denoising and feature extraction methods. In addition, the parameters of the trained SDAE can be reused and can reduce quantity cost for training networks, which constantly consumes time and resources.  
This work was supported by the National Natural Science Foundation of China (Grants No. 51505311 and 51375322), the Natural Science Foundation of Jiangsu Province (No. BK20150339), and the China Postdoctoral Science Foundation funded project (2016T90490).The authors would like to thank [PERSON] of Case Western Reserve University for his kind permission to use their bearing data. The authors also want to appreciate two anonymous reviewers for their valuable suggestions and comments.  
[PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] analyzed the data; [PERSON] contributed gearbox measurement setup and data; [PERSON], [PERSON], [PERSON] wrote and revised the paper.  
The authors declare no conflict of interest at all.
## 3 Possible Use Cases  
Energy efficient operation of cellular networks has become imperative for the sustainability and proliferation of services on mobile devices. However to reduce cost (in terms of the network energy consumption) while maintaining and improving the user experience, considerations need to be taken at both the network access side and the radio domain. In a typical energy consumption profile of a European mobile network, more than 50% of the total energy consumption during operation is attributed to the BS equipment and another 30% is consumed by the mobile switching and core transmission equipment [15]. Therefore even when the embodied energy to produce the communication equipment is not factored into the cost, shrinking cell sites could not suffice as a viable approach unless efficient operation of the network components is achieved. In [16, 17] the authors propose turning off BSs during low utilization periods to save energy while adjacent cells increase their coverage to provide the necessary connectivity. If the BS is operating, then controlling the wireless resources, such as for example the transmit power, has an overall small contribution to the total energy savings. As a result, the most significant energy gains can be achieved by turning off certain BSs when the traffic load in their coverage area is low.  
On the other hand, communication over the wireless channel can easily drain the battery of mobile devices. As argued in [18] the energy consumption by the radio frequency (RF) module on a mobile device, is responsible for a large percentage of the total energy consumption budget. Also, transmitting over longer distances consumes considerably more energy as the output transmit power increases more than linearly with the transmission distance. Even worst, it has been shown in [19] that for CDMA (Code Division Multiple Access) cellular networks, the transmit power in the uplink channel increases more than linearly with the network load. Furthermore, in [20] the authors quantified the variability that exists in the communication energy consumption from accessing different RATs, pointing out the high energy efficiency gains of accessing wireless local area (WLAN) networks instead of macrocell sites. However in this case, the availability of WLANs depends heavily on the terminal's location.  
In this section we discuss how the proposed SCF relaying scheme could serve as an alternative (and also complimentary) method for reducing the network energy consumption in cellular networks making explicit reference to some of the sources of inefficiency as identified above.
# A Resilient LoRa-Based Solution to Support Pervasive Sensing
## 5 Results
### Mesh Tests
#### 5.3.7 Bike Lane Mesh Test  
This test was performed along the same conditions as the test carried out on the bike lane outlined at the start of this section: one node would be still and send messages while the other node would move further away, stopping at specific distances to receive data before moving again.  
The results in the first test are similar to those we obtained in this test, as seen in Figure 16. The nodes start with a spreading factor of 7, and when the message exchange starts when they are 500 m apart, the connection begins to show poor performance by dropping packets. For this reason, they move to a spreading factor of 10 to not lose any more messages. The positive results can be seen as only a slight dip in PDR at the 500 m mark, much higher than the one of the first outdoor Lora test. The nodes keep using a spreading factor of 10 up until they are 1000 m apart when the same connection issues return. At this distance, the nodes perform another spreading factor increase to use 12 and keep the PDR high. After the 2 km mark, the connection continues to show problems, but the two nodes already use the highest possible value for the spreading factor, so nothing can be done to avoid issues. The solution implemented was able to keep the connection stable and limit packet loss while, at the same time, the nodes were able to exchange messages with the highest possible throughput.