esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model and Problem Formulation
### Communication Model  
The transmission rate of the ground user is related to the channel state and the spectrum state. In this paper, we consider that the user's channel remains unchanged during the task offloading. The achievable rate of uplink transmission is,  
\[r^{up}=B\log_{2}\biggl{(}1+\frac{p_{g}h}{N_{0}}\biggr{)}, \tag{1}\]  
where \(B\) denotes the bandwidth allocated for the satellite, \(p_{g}\) denotes the transmission power of user, \(h\) denotes the channel gain, and \(N_{0}\) is the channel noise. Then, we can use (1) to denote the transmission rate of the ground user based on the Shannon Theory, where the transmission rate is mainly controlled by the channel bandwidth \(B\) allocated for the satellite, and also influenced by the signal-to-noise ratio given by \(\frac{p_{g}h}{N_{0}}\). Similar to the rate of uplink transmission, the achievable rate of downlink transmission is given by \(r^{down}\), considering the transmission power of the satellite as \(p_{u}\). Thus, the delay of uplink transmission and downlink transmission can be given by \(t^{up}=d/r^{up}\) and \(t^{down}=\hat{d}/r^{down}\), respectively, where \(d\) is the size of the tasks and \(\hat{d}\) is the size of the computation results.
###### Abstract  
Intraoperative neurophysiological monitoring (IONM) is being applied to a wide range of surgical fields as a diagnostic tool to protect patients from neural injuries that may occur during surgery. However, several contributing factors complicate the interpretation of IONM, and it is labor- and training-intensive. Meanwhile, machine learning (ML)-based medical research has been growing rapidly, and many studies on the clinical application of ML algorithms have been published in recent years. Despite this, the application of ML to IONM remains limited. Major challenges in applying ML to IONM include the presence of non-surgical contributing factors, ambiguity in the definition of false-positive cases, and their inter-rater variability. Nevertheless, we believe that the application of ML enables objective and reliable IONM, while overcoming the aforementioned problems that experts may encounter. Large-scale, standardized studies and technical considerations are required to overcome certain obstacles to the use of ML in IONM in the future.  
intraoperative neurophysiological monitoring; artificial intelligence; machine learning; deep learning +
Footnote †: journal: Medical  
+++ ==WARNING: Truncated because of repetitions==
The use of machine learning (ML) in medical research and clinical practice is rapidly expanding [9]. In particular, ML is increasingly being used for diagnosis and prognosis [10; 11], as well as for disease classification, replacing existing methods [10]. Moreover, ML can execute proxy decision-making at the level of medical experts [12] and can readily and efficiently handle a large number of samples and variables [13]. Artificial intelligence (AI) models have the additional benefit of continuously improving themselves by learning from additional data and by applying more advanced techniques [14]. Although their performance depends on data quality and learning algorithms, in general, ML models have yielded promising results in clinical medicine [10; 15].  
In this narrative review, we focus on ML and its application in the field of IONM. We first summarize studies in which ML algorithms have been applied to IONM and then present a comprehensive review of ML algorithms. Furthermore, we discuss the limitations that should be considered in the application of ML to IONM. Finally, we conclude by pointing out the scope for future research to enable ML-based technologies to support human experts and cover the shortcomings of IONM.
## 5 Conclusions  
This paper has proposed the hybrid 1D and 2D beam models, which considered the equivalent interference factor \(\alpha\) from adjacent beams and the equivalent interference factor \(\beta\) from the TCNs intra-beam for the return link of the IWMSNNs. The closed form approximations of the optimal capacity and MMSE capacity were derived under Rician fading. The numerical simulations showed that \(\beta\) lowered the capacity, and the capacity of \(\hat{\mathbf{T}}_{\text{1D}}\) approximately decreased to zero as \(\alpha\) increased to one and as \(\beta\) in the lower region. The effects of intra-beam interference factor \(\beta\) of the TCNs were associated with inter-beam interference factor \(\alpha\) on the performance of the IWMSNNs and was even, to some degree, approaching the higher region of \(\alpha\). Due to the LOS and channel correlation characteristic of GEO satellite channels, which were unlike the independent channels of the users from base stations in the TCNs, thus, the capacities of the IWMSNNs were converging as the Rician fading factor \(\kappa\) increased, because the Rician fading could be converted to the additive Gaussian noise channel as \(\kappa\rightarrow\infty\) to get the nonfading channel. The optimal capacity and MMSE capacity exhibited different trends toward the number of sinks \(K\), which were mainly determined by using different decoding strategies. Meanwhile, from the numerical simulations, Rayleigh fading caused losses on the performance of the IWMSNNs, while the strong LOS benefited the performance.  
This work is partially supported by National Natural Science Foundation of China (61231011, 91338201, 91438109 and 91338106). The author is deeply grateful for all help from the papers quoted. Finally, the authors would like to give gratitude to the reviewers for their constructive comments.  
Figure 11: The MMSE capacity of the hybrid 1D model and modified hybrid 1D beams with \(\gamma=10\) and \(N=100\), hybrid 2D model with \(\gamma=10\), \(L=50\) and \(M=50\) (\(K=1\), \(K=2\), \(K=8\) and \(\kappa=50\)) versus \(\alpha\) and \(\beta\).  
**Author Contributions:** Hongjun Li and Hao Yin proposed the the 1D and 2D hybrid beam models for the IWMSNNs. Hongjun Li and Xiangwu Gong derived the optimal and MMSE capacity approximations. The simulations were implemented by Hongjun Li and Feihong Dong. Hongjun Li and Baoquan Ren were responsible for the writing of the paper, while Jingchao Wang and Yuanzhi He were responsible for proof-reading the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
Understanding crop types and their annual cycles is key to managing natural resources, especially when the pressures on these resources are attributable to climate change and social, environmental, and economic policies. In recent years, the space sector's development, with programs such as Copernicus, has enabled a greater availability of satellite data. This study uses a multi-sensor approach to retrieve crop information by developing a Proof of Concept for the integration of high-resolution SAR imagery and optical data. The main goal is to develop a pre-automatized processing chain that explores the temporal dimension of different crop. Results are related to the advantage of using a multi-sensor approach to retrieve vegetation biomass and vertical structure for the identification of phenological stages and different crops. The novelty consists of investigating the multi-temporal pattern of radiometric indices and radar backscatter to detect the different phenological stages of each crop, identifying the Day of the Year (DoY) in which the classes showed greater separability. The current study could be considered a benchmark for the exploitation of future multi-sensor missions in downstream services for the agricultural sector, strengthening the evolution of Copernicus services.  
remote sensing; crop classification; SAR; optical; operational service; Common Agricultural Policy; Copernicus; NOCTUA; IRIDE constellation +
Footnote †: journal: _Land_ **2024**, 13, 91. [[https://doi.org/10.3390/land13010091](https://doi.org/10.3390/land13010091)]([https://doi.org/10.3390/land13010091](https://doi.org/10.3390/land13010091))
###### Abstract  
In B-cell malignancies, the overexpression of MYC is associated with poor prognosis, but its mechanism underlying resistance to immunocompetent therapy remains less clear. In further investigations of this issue, we show here that the pharmacological inhibition of MYC in various lymphoma and multiple myeloma cell lines, as well as patient-derived primary tumor cells, enhances their susceptibility to NK cell-mediated cytotoxicity induced by conventional antibodies targeting CD20 (rituximab) and CD38 (daratumumab), as well as T cell-mediated cytotoxicity induced by the CD19-targeting bispecific T-cell engager blinatumomab. This was associated with upregulation of the target antigen only for rituximab, suggesting additional escape mechanisms. To investigate these mechanisms, we targeted the _MYC_ gene in OCI-LY18 cells using CRISPR-Cas9 gene-editing technology. CRISPR-Cas9-mediated _MYC_ targeting not only upregulated CD20 but also triggered broader apoptotic pathways, upregulating pro-apoptotic FUMA and downregulating anti-apoptotic proteins BCL-2, XIAP, survivin and MCL-1, thereby rendering tumor cells more prone to apoptosis, a key tumor-lysis mechanism employed by T-cells and NK-cells. Moreover, MYC downregulation boosted T-cell activation and cytokine release in response to blinatumomab, revealing a MYC-mediated T-cell suppression mechanism. In conclusion, MYC overexpressing tumor cells mitigated the efficacy of therapeutic antibodies through several non-overlapping mechanisms. Given the challenges associated with direct MYC inhibition due to toxicity, successful modulation of MYC-mediated immune evasion mechanisms may improve the outcome of immunotherapeutic approaches in B-cell malignancies.  
Keywords:MYC; MYC inhibition; B-cell malignancies; lymphoma; CRISPR-Cas9; antibody therapy; T cell-redirecting antibodies; immunotherapy +
Footnote †: journal: Molecular Sciences
## 4 Numerical Simulation
However, while OMP only considers the sparsity of UWA channel, the CRER output of OMP is still inferior to that of BOMP, which is capable of taking advantage of the block sparsity of the UWA channel. Furthermore, the time-varying characteristic of the UWA channel would deteriorate the intra-block correlation of BOMP and cause the performance degradation. KC-CS and BS-RLS achieve roughly equivalent CRER results by exploiting the time-varying sparsity and block sparsity. However, the BS-RLS requires a relatively long training sequence and brings much computational burdens. The SDD-CS achieves the second-highest CRER results by exploiting the joint sparsity and time-varying characteristics simultaneously, while this type algorithm is more suitable for the hybrid channel. The proposed BS-CS outperforms the other six algorithms with the highest CRER output in the block sparse time-varying UWA channel. Specifically, the BS-CS not only utilizes the correlation among intra-block of the UWA channel, but also tracks possible addition new support caused by time-varying multipath while performing CS on the block sparse estimation error. Figure 4: The CRER output of different estimators. Figure 3: The simulated block sparse time-varying UWA channel and the corresponding sound speed profile. The red line and the light blue line represent the seabed topography and sea surface, respectively.
# Quantitative Assessment of Upper- Limb Volume: Implications for Lymphedema Rehabilitation?
## 4 Methods for Volumetric Assessment
### Bioimpedance Spectroscopy (BIS)  
Bioimpedance spectroscopy (BIS) is a non-invasive technique that has gained recognition for its application in the volumetric assessment of upper-limb lymphedema [48]. This method utilizes electrical impedance measurements to estimate limb fluid content and volume. By analyzing the impedance response to varying frequencies of electrical current, BIS provides valuable information about tissue composition and fluid distribution within the limb [49].  
The principle behind BIS lies in the fact that different tissues, such as muscle, fat, and fluid, have different electrical conductivities. When an electrical current is applied to the limb, the impedance to the current flow can be measured. By analyzing the impedance spectrum across multiple frequencies, BIS algorithms can separate out the contribution of different tissue types and estimate the volume of extracellular fluid, a key component of lymphedema [50].  
One of the key advantages of BIS is its ability to assess both the fluid content and the volume of the affected limb [51]. Traditional methods, such as circumferential measurements, provide limited information about tissue composition and may not capture subtle changes in fluid accumulation. On the other hand, BIS offers a more comprehensive evaluation of the limb's fluid status, enabling clinicians to monitor the overall volume, tissue hydration, and fluid distribution within the limb [52]. BIS has demonstrated good reliability and accuracy in assessing upper-limb lymphedema [43; 52; 53]. It allows for repeated measurements over time, facilitating the longitudinal monitoring of edema progression and response to treatment interventions. Additionally, BIS can provide valuable insights into the early detection of subclinical lymphedema, enabling early intervention and preventive measures [50].  
However, it is important to note certain considerations when using BIS for volumetric assessment. The technique relies on proper electrode placement and ensuring consistent contact with the skin to obtain accurate impedance measurements [43]. Factors such as limb positioning, hydration status, and electrode quality can affect the reliability of the results. The standardization of measurement protocols and training of operators are necessary to ensure consistent and reproducible outcomes [49; 50].  
Several studies have demonstrated the effectiveness of BIS in assessing limb volume in lymphedema, both in research and clinical settings. BIS has shown good agreement with other established methods, such as water displacement and geometry, and has been validated in various patient populations [39; 54].  
Altogether, this evidence underlines that BIS could be considered a promising tool in lymphedema volume assessment. Beyond its volume assessment capabilities, BIS offers the unique advantage of evaluating tissue composition and facilitating longitudinal monitoring, thus cementing its role as a cutting-edge and indispensable tool for the comprehensive evaluation of upper-limb lymphedema.
## 3 Results
### In Situ FTIR
#### 3.1.3 Polarized FTIR Measurements  
In Figure 7 we report polarized FTIR spectra measured on a d-sPS/dTol co-crystalline uniaxially deformed film with the polarized IR radiation parallel (blue line) and perpendicular (red line) to the drawing direction of the film. The FTIR analysis was done in this case independently to the SANS investigation and was meant to prove the performance of the IR polarizer using well-known samples [21]. SANS measurements on similar systems and guest exchange process were reported elsewhere [36]. The d-sPS/hTol-oriented film exhibits clear polarization in the 3100-2800 cm\({}^{-1}\) region, which suggests that toluene guest molecules are kept oriented in the crystalline region of the d-sPS, as they are located between the oriented helices of the d-sPS host system. The hTol provides IR bands due to benzene ring C-H stretch modes in 3100-3000 cm\({}^{-1}\), while its methyl group shows two bands due to antisymmetric stretch modes at about 2945 and 2920 cm\({}^{-1}\) and a band due to symmetric stretch mode at about 2860 cm\({}^{-1}\).
## 3 Current Measurement Techniques
### Other Techniques Applicable to Both Soil Moisture and Snow Property Determination
#### 3.3.2 Time Domain Reflectometry  
Time domain reflectometry (TDR) is used to determine the water content in a small volume of soil or snow, based on changes in the dielectric constants that depend on moisture content [52, 53]. The changes in dielectric constants are measured through the propagation of reflected electromagnetic waves. The method is relatively insensitive to soil density, temperature, and salinity, and only mildly disturbs the sample by the insertion of the probe. Accuracy is in the order of 1 to 2% [54]. Therefore, it has found widespread use in hydrological research and agricultural applications. Drawbacks are the limited distance between instrument and probe and the disturbance of the soil that still occurs when installing the probes. Equipment is commercially available and usually measures within small volumes in the order of 5 cm\({}^{3}\). Extensive reviews of TDR applied to soil moisture determination can be found in [55] and [54]. Frequency domain reflectometry (FDR) uses the same principle as TDR, but analyzes the reflected waveform in the frequency domain, enabling the determination of frequency dependent complex dielectric permittivity [56]. This facilitates the separation of probe response and soil response and enables better extraction of useful information [57]. Another option to obtain water content through changes in the dielectric constant of soil is the use impedance probes, or theta-probes. These measure differences in impedance on a coaxial transmission line formed by three shielding rods connected to ground and one central rod fed with a sinusoidal signal, where the soil in between acts as dielectric that changes the impedance [58].
## 2 FANET Architecture and Routing Protocol
### Communication and Application Architecture  
We show the communication architecture of the FANETs in Figure 2. We have divided the UAVs into an upper layer of large fixed-wing UAVs and a lower layer of small rotary-wing UAVs according to their wing type and operational altitude. Large fixed-wing UAVs typically have high mobility, higher altitude, and longer battery life, as well as  
Figure 1: Paper content structure.  
-wider signal coverage, allowing them to act as a communications relay between space-based and air-based networks. Smaller rotary-wing UAVs are lighter in weight, cheaper to produce, can remain stationary and take off and land vertically, and are suitable for covering ground users, vehicles, and IoT nodes for close monitoring and short-term emergency communications [20; 21].  
The high mobility of the lower layer of small rotary-wing UAVs leads to frequent changes in the topology of the FANETs, and the speed difference between the upper layer of large UAVs and the lower layer of small UAVs leads to the possibility of each node joining or leaving the network at any time. This requires the network to be able to react quickly to topology changes when nodes join, exit, or are abnormally destroyed. The use of traditional routing increases the routing overhead, which in turn reduces network performance and ultimately has a negative impact on user quality of service (QoS) and operator operating costs [22; 23]. At the same time, this poses the challenge of high time-variability and complexity for SAGINs.  
As shown in Figure 3, unlike a traditional FANET, the FANET network in SAGINs needs to consider not only the UAV nodes' own mobility, vulnerability to interference, and limited channel capacity, but also the interaction and integration with other networks, such as ground-based base stations and satellite networks, to achieve better network performance.  
Figure 2: Schematic diagram of the layered architecture of FANETs under SAGINs.
## 3 Materials and Methods
### InSAR (SBAS) Processing  
InSAR is a powerful technique that measures surface deformation relative to the satellite line of sight (LOS) by calculating an offset between the incident and return waves [37]. It provides a low-cost and short-time measurement of ground displacement [38]. A small baseline subset (SBAS) is an advanced interferometric technique that uses short spatial and temporal baseline to measure the surface deformation. Recent neotectonic studies successfully employed the SBAS technique to estimate the displacement rate along faults. Examples include a creep along the Chaman Fault System and Salt Range Thrust in west Pakistan [13; 39; 40], the Altyn-Tagh Fault in China [41], and the growth faulting in Houston, Texas [42].  
The launch of Sentinel-1A (S1) satellite by the ESA in 2014 provided day and night and all-weather C-band data with a shorter revisit cycle (12 days) than the European Remote Sensing (ERS) 1/2 and the Envisat mission [43; 44]. For this study, 90 ascending S1 images extending from 10 May 2018, to 20 October 2019, were acquired from the ESA sentinel data hub. Figure 4 summarizes the processing steps and the parameters employed to measure the displacement rate along the frontal Khirgi Thrust in the Manzai Ranges with the Sarscape module of ENVI software. Sentinel-1A data specification and the mean coherence of the 380 interferograms used for this study are shown in Supplementary Data (Figure S1, Table S1).  
Figure 3: Sentinel-2B band ratios (**a**) 12/7-12/3-12/11; (**b**) 11/12-12/7-8/11; (**c**) 8/11-11/12-12/7 displayed as a RGB composite to discriminate lithology and structural elements. The interpreted lithologic units correlate well with the previously mapped units. The band ratios in (**a**) differentiate younger wetter alluvial fans from older drier alluvial fans based on hydroxyl ion (OH\({}^{-}\)). The older drier alluvium appears in light blue, while the younger wetter one appears in plum color. The light pink color shows the vegetation on the fan. The band ratios in (**b**) and (**c**) discriminate the different types of sedimentary rocks based on the clay content and surface texture. In (**b**), the violet color shows intact and homogenous sedimentary rock type, and the light green color represents the loose surface clays within active streams entrenching the older drier alluvial fans. While (**c**) shows more heterogeneous lithology in the form of interbedded competent yellow-green sandstone and incompetent dark blue clays in the hanging wall of Khirgi Thrust, the yellow color depicts the Manzai anticline actively growing into the older alluvial fan; (**d**) Sentinel-2 true color composite (RGB: 432) showing streams deflection in an actively evolving fold–thrust belt.
## 3 Results
### Integration of Model LAI and Remote Sensing Product  
VV, VH, VV/VH, and NDVI were derived from Sentinel 1 and Sentinel 2 using GEE with filter dates corresponding to ground data visit dates. Among all remote sensing products, NDVI showed a good relationship with LAI (R\({}^{2}\) = 0.82; Figure 6); NDVI exhibited the best fit with LAI (correlation = 80%). Hence, it was integrated with the model LAI to estimate spatial yield as it is a commonly used RS index for assessing crop growth and estimating crop yield. Similar findings were previously reported [51, 52], revealing that NDVI and LAI show a good correlation of over 0.70 for different plant species.