esa-sceva/satcom-chunk-collection huggingface dataset:
# Remote Sensing of Snow Cover Using Spaceborne SAR: A Review
## 5 Discussion
### The Advances of SCE-Detection by SAR
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Detection Approach** & **Backscattering-Based** & **InSAR-Based** & **PolSAR-Based** \\ \hline Background theory & \begin{tabular}{c} The backscattering \\ coefficient reduces when \\ snow becomes wet \\ \end{tabular} & \begin{tabular}{c} Coherence loss over \\ snow covered surfaces \\ \end{tabular} & \begin{tabular}{c} Scattering mechanisms \\ of dry and wet snow and \\ the surface behave \\ differently \\ \end{tabular} \\ \hline Minimum numbers of & 2 & 2 & 1 \\ required SAR images & \begin{tabular}{c} Pair sensed at the same \\ geometry \\ \end{tabular} & \begin{tabular}{c} Pair has a short temporal \\ baseline \\ \end{tabular} & \begin{tabular}{c} Image has dual or quad \\ polarizations \\ \end{tabular} \\ \hline \begin{tabular}{c} The complexity of the \\ algorithms \\ \end{tabular} & Low & Medium & High \\ \hline \begin{tabular}{c} Primarily analyzed \\ component \\ \end{tabular} & Backscattering coefficient & Coherence & Polarimetric parameters \\ \hline LIA dependency & High & Medium & Low \\ \hline \
\begin{tabular}{c} The richness of derived \\ information \\ \end{tabular} & Medium & Low & High \\ \hline
\begin{tabular}{c} The noisiness of derived \\ information \\ \end{tabular} & High & Low & Medium \\ \hline \hline \multicolumn{3}{c}{Snow Type Sensing Capability} \\ \hline Wet snow & Yes & No & Yes \\ \hline Dry snow & No & No & Yes \\ \hline Total snow & No & Yes & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Overall comparison of the three mainstream SAR-based snow cover detection approaches.
# Translational Echocardiography: The Dog as a Clinical Research Model of Cardiac Dysfunction
## 3 Cardiac Anatomical Similarities between the Human and the Dog
### Ventricular Chambers in Both Species  
With characteristics common to the human heart, the ventricles are the main ejection chambers of the heart. Therefore, their walls are much more muscular than the atria, making this normal function more efficient. Since the wall thickness of the right ventricle corresponds to approximately one-third of that of the left ventricle, the structure results in lower systolic pressure. The left ventricle exhibits a greater degree of muscularity compared to the right ventricle. This is because the left ventricle has to generate high pressure to propel blood through the systemic circulation, which comprises a larger network of blood vessels compared to the pulmonary circulation. In fact, systemic circulation has a resistance that is usually more than four times higher than pulmonary circulation, necessitating a greater contractile force in the left ventricular wall. To achieve efficient contraction of the heart, trabeculae are interlaced between the walls of both ventricles, primarily near the apex, thereby reinforcing the walls and increasing the force of systolic contraction. This intricate network of trabeculae, known as the trabecular network, plays a crucial role in maintaining the structural integrity and functionality of the heart [11; 13]. Larger mammalian hearts have less ventricular trabeculation than normal adult human hearts, and the trabeculations are usually thicker than human hearts as a compensatory contractile phenomenon [7; 9; 11]. In dogs, the walls of the ventricles are attached to papillary muscles that support the atrioventricular valves.  
The heart of a large mammalian animal, such as a dog, has three papillary muscles in the right ventricle and two in the left ventricle, similar to human anatomy. However, variations occur in individuals and species [7; 8; 11]. There are at least two chordate tendinaee per papillary muscle to ensure redundancy. Both ventricles are traversed by fibrous or muscular bands containing Purkinje fibers. The dog's right ventricle usually has a prominent moderator band [7]. Species differ significantly in the origin and insertion of the band, as well as in its structure. In the dog, the septal wall near the base of the anterior papillary muscle extends into the lumen through one or more branched muscle filaments. Dogs' left and right ventricles are also structurally symmetrical to human chambers. At the opening of the septal leaflet of the mitral valve, the dog's left ventricle physiologically divides into an inflow and outflow region, exhibiting a conical structure [11; 19].
## 2 Related Work  
### Long-Term Time Series Forecasting  
Time series prediction tasks aim at forecasting future time series in the prediction window, given historical time series data in the conditioning window. Long-term time series forecasting is characterized by the large length of predicted series. Mainstream time series prediction models could be divided into traditional statistical methods and machine learning-based methods. Traditional statistical methods mainly include autoregressive methods, such as ARIMA [14; 15], and additive models, such as Holt-Winters [16] and Prophet [17]. In particular, Holt-Winters [16] and Prophet [17] capture versatile temporal patterns (e.g., trends, seasonality, and randomness) to better model nonlinearities. Thesemethods have strong explainability and are suitable for dealing with relatively stable and regular time series data. Meanwhile, these methods suffer from some limitations, such as sensitivity to outliers and missing values, difficulty in dealing with nonlinear and complex time series data, and difficulty in integrating other relevant information, such as timestamp information. In particular, when applied in the long-term forecasting tasks, the aforementioned statistical methods fail to capture the reliable dependencies.  
In recent years, transformers based on self-attention mechanisms [10] has shown powerful capabilities in sequential data, such as natural language processing [18], audio processing [19], and even computer vision [20]. However, in long-term forecasting tasks, due to the quadratic complexity of sequence length \(L\) in memory and time, applying self-attention mechanisms to long-term time series prediction is computationally expensive. [PERSON] [21] introduced local convolution into transformer and proposed the LogSparse attention mechanism to select exponentially growing intervals of time steps, reducing the complexity to \(O(LlogL)\). [PERSON] [11] used a local-sensitive hashing (LSH) attention mechanism to reduce the complexity to \(O(LlogL)\). [PERSON] [1] extended transformer with the ProbSparse 2 attention mechanism based on KL-divergence to achieve \(O(LlogL)\) complexity. Nevertheless, it is worth noting that these methods are based on ordinary transformers and attempt to change self-attention mechanisms into sparse versions. They still followed the pointwise dependence modeling principle. Autoformer [2] decomposed complex time series into seasonality and trendiness, and used the autocorrelation mechanism of the sequence to capture reliable temporal dependencies; FEDformer [3] also used a similar decomposition idea to complete the self-attention calculation for seasonal components in the frequency domain. In this paper, the frequency domain transform technique for disentangling seasonal and trend components is based on the inherent seasonality and trendiness of time series.
# The Potential Therapeutic Role of Bruton Tyrosine Kinase Inhibition in Neurodegenerative Diseases
## 3 Bruton Tyrosine Kinase's Inhibitors in Neurodegenerative Disease
### Huntington's Disease  
HD is an autosomal dominant neurodegenerative disorder caused by an expanded CAG repeat in the huntingtin (HTT) gene, resulting in mutant huntingtin (mHTT) protein with a toxic polyglutamine tract. Clinically, HD manifests with progressive motor dysfunction (chorea, dystonia), psychiatric symptoms (depression, irritability), and cognitive decline. Neuropathologically, HD is characterized by neuronal loss in the striatum and cortex, accompanied by widespread microgliosis and neuroinflammation [85]. Microglial activation is an early and sustained feature of HD, observed even before clinical onset [86]. PET imaging studies using TSPO ligands have demonstrated increased microglial activity in pre-symptomatic mutation carriers, correlating with disease progression [87]. In HD brains, microglia exhibit a pro-inflammatory phenotype with increased expression of IL-6, TNF-\(\alpha\), and C1q, which could contribute to synaptic pruning, glutamate excitotoxicity, and neuronal apoptosis [88]. BTK is expressed in CNS-resident microglia and peripheral monocytes and modulates key inflammatory pathways that are involved in HD pathogenesis: TLR signaling through BTK leads to NF-\(\kappa\)B activation and the production of pro-inflammatory mediators [89]. Given BTK's role downstream of TLRs and upstream of both NF-\(\kappa\)B and NLRP3, it likely functions as a central amplifier of inflammation in HD. Even though BTK inhibition has not yet been tested on this condition, several hallmarks shared with other discussed diseases support the concept of BTKi application in HD. BTKis could be able to prevent neuroinflammatory escalation by acting on microglial signaling. Also, BTK inhibition may modulate astrocyte reactivity and peripheral immune dysfunction in HD. Indeed, peripheral immune cells from HD patients exhibit a pro-inflammatory profile, with increased IL-19 and TNF-\(\alpha\) secretion [90]. Given the genetic clarity and consistent inflammatory features of HD, BTK inhibition offers a compelling therapeutic avenue.
### Proposed Solution
The present study focuses on the use of well-established robust technologies operating at high frequencies (HFs) of 868 MHz and 2.4 GHz. As part of an analysis of whether these transceiver technologies can deliver a stable data link over a short wired PLC environ  
Figure 3: Illustration of the potential advantage of exploiting a short wireless range by inserting an RF transceiver inside a cylindrical metal shell with communication modules connected through physical wires instead of antennas. The general internal smart cell structure (**A**) is exemplified in a potential battery structure where the wireless obstructed short range is compensated by wired links (**B**), while a faulty power connection may still be bridged via RF coupling (**C**). ment, such as the one encountered in battery packs, a simulation followed by empirical experiments is conducted. The investigation metric is the received signal strength (RSS) for the simulation or the received signal strength indicator (RSSI) for the empirical test, in combination with the potential bit error rate (BER) or packet error rate (PER) indicators. The communication system is capacitively connected to the DC bus composed of four 18650 Li-ion cells presenting a state of charge (SOC) of 100%. Experimentally, the pros and cons of the two proposed HF frequencies of 868 MHz and 2.4 GHz are determined by varying payload size and coupling capacitance. The receiver (Rx) sensitivity of approximatively \(-100\) dBm, such as the ones delivered by the TI transceivers used in the experiment, \(-109\) dBm for CC1200 [44], and 99 dBm for CC2520 [45], are used as a reference for the data link available budget, while the transmitter (Tx) power is set at the generic value of 0 dBm.
# Brillouin Frequency Shift Sensing Technology Used in Railway Strain and Temperature Measurement
###### Abstract  
This study and verification are based on the Brillouin frequency shift (BFS), which is related to the strain and temperature changes of a single-mode fiber, because such a shifted frequency can be quantitatively measured and converted to strain and temperature differences. We explain the installation of a Brillouin distributed fiber sensing system (DFOS) on an actual operating railway to measure the temperature and strain of the rail. In addition, the measured data were calculated and analyzed, revealing the geometric irregularity of the tested rail and the location of the abnormality. We obtained a temperature difference of 12.1 \({}^{\circ}\)C between the temperature distribution of the measured rail and the atmospheric temperature, and there was a 1.5 h delay between the two. We also obtained rail irregularities ranging from \(-0.3\) to +0.4 mm by calculating the slight strain difference of the rail in this test.  
Brillouin frequency shift; distributed fiber optic sensing; rail strain; rail deformation; rail irregularity +
Footnote †: journal: Applied Sciences
## 3 System Model  
This study is focused on optimizing network performance by utilizing MM mechanisms within an ultra-dense HetNet environment consisting solely of small cells, specifically microcells. These microcells work together to provide a seamless UE connection. UEs connect to cells based on their signal strength metrics RSRP or Reference Signal Received Quality (RSRQ) level. While this ensures basic connectivity, it can lead to inefficiencies in highly dynamic user distribution scenarios, particularly in ultra-dense network environments. In such environments, UEs prioritizing strong signals might overload certain cells, causing uneven user distribution and underutilized resources in less congested cells. This leads to uneven user distribution, with congested areas experiencing limited resources for UEs while others remain underutilized. Consequently, the overall network efficiency suffers. On the other hand, the traditional cell selection for HO processes relies solely on maximizing signal strength metrics (RSRP or RSRQ) too. This approach leads to several issues:  
* Unnecessary HOs: UEs might unnecessarily switch to congested cells due to strong signal strength, even if their current connection is sufficient. This wastes resources and increases signaling overhead.
* Service Disruptions: Frequent and unnecessary HOs can trigger RLF or HOPP, further impacting user experience with dropped calls or delays.  
Current load-balancing approaches often prioritize static network configurations and struggle to adapt to real-time changes in user density. This can lead to persistent congestion in specific areas while other areas remain underutilized. Furthermore, a cell with an overloaded state is a critical challenge within this context. When a cell experiences a surge in user traffic or insufficient resources to handle existing traffic, it becomes overloaded. The duration of this overload significantly impacts network performance. Prolonged overload can lead to a domino effect, as UEs struggling to connect to congested cells might put additional strain on neighboring cells, further escalating the issue. These limitations in cell selection, handover procedures, and load-balancing techniques can lead to significant inefficiencies and a degraded user experience within the network. Users might experience the following:  
* Reduced Network Throughput: Congestion can limit the overall data transfer rate experienced by users.
* Increased Latency: Delays in data transmission due to congestion and unnecessary HOs.
* Higher Call Drop Rates: Increased likelihood of dropped calls due to RLF or HOPP triggered by inefficient HOs.  
The next section will introduce the DDLB algorithm, specifically designed to address the limitations identified in the current system model. The DDLB algorithm aims to improve overall network performance by dynamically adjusting target cell selection criteria based on real-time network conditions, particularly congestion levels.
## 2 Materials and Methods
### Dead-Band Case  
When the frequency is in its dead-band, FCR does not require any dP. This circumstance is less critical for frequency control regulation in general. Thus, it is exploited in several control services [10; 30] to allow SoC restoration. In this case, the DoF is represented as a share of regulating power that can be offset for charging and discharging while frequency is in its dead-band. We use as offset ratio (\(\rm R_{offset}\)) 25% with respect to regulating power: this is coherent with the offset allowed in the Fast Reserve pilot project in Italy. Therefore, \(\rm P_{rest}\) is computed as in Equation (6).  
\[\left\{\begin{array}{l}\rm P_{rest}=sgn(\Delta SoC)\times R_{offset}\times P _{nom}\quad if\,|dF|\leq dF_{DB}\wedge|\Delta SoC|>\varepsilon\\ \rm P_{rest}=0\end{array}\right., \tag{6}\]  
where sgn \(\Delta SoC\) is the signum function for \(\Delta SoC=SoC(t)-SoC_{target}\) (positive power for SoC greater than \(\rm SoC_{target}\)), \(\rm dF_{DB}\) is the dead-band of 20 MHz and \(\varepsilon\) is a margin of 2% to depict an acceptable margin of tolerance on \(\Delta SoC\). The resulting droop curve is shown in Figure 6, where the pink area represents the DoF in dead-band.  
Figure 5: The variable droop adopted to simulate over/under-regulation schemes. Dead-band is equal to the one adopted in the other cases. The pink area, instead, shows the possible dP that can be delivered at each dF. Full activation dF varies between \(\pm 30\) and \(\pm 45\) MHz.
# Predicting Freeway Travel Time Using Multiple-Source Heterogeneous Data Integration
## Case Study
### Results and Comparative Analysis
Compared with BP neural network and SVM algorithm, it can be clearly seen that SVM has proved to be a highly accurate method in traffic data fusion. Therefore, the optimized SVM multi-source traffic data quality control model is feasible and effective. This means that the optimized SVM model described in this paper has higher travel time prediction accuracy in the road segment, and the mapping law of the input and output are better represented by the optimized SVM model. In terms of the relative prediction errors of the three prediction models, the MAPE of the optimized SVM model is lower than the prediction errors of the BP neural network and the conventional SVM model when using holiday and weekday data. This indicates that the optimized SVM model described in this paper has certain advantages in terms of the travel time prediction model of the road segment, and the data requirements are lower.
## 2 System Design and Principle
### Principle
#### 2.2.2 Frame Structure Design  
In the actual photon-counting UOWC system, how to determine the starting position of a segment of data is a difficult problem. Here we design a data frame format, which can quickly find the appropriate starting position and provide the premise for data recovery. The format of the data frame is shown in Figure 4. A complete data frame consists of a series of starting position estimation sequence (SPES), silent time 1, synchronization header, frame header, valid data and silent time 2. The SPES and silent time are used to find a suitable starting position. The synchronization header sequence is a square wave signal carrying synchronization information. The function of the frame header is to mark the starting position of the valid data; the valid data are placed behind the frame header. At the end of the data frame, there is a silent time to distinguish the two different data frames.
## 2 Materials and Methods
### Profiling, Identification, and Relative Quantification of Volatile Compounds Using HS-SPME-GC-MS  
The profiling, identification, and relative quantification of volatile compounds were carried out using the HS-SPME-GC-MS system (Agilent Technologies, USA). The separation was achieved with a DB-WAX column (30 m \(\times\) 0.25 mm, 0.50 \(\upmu\)m film thickness, Agilent Technologies, USA) using helium as a carrier gas at a constant flow rate of 1.2 mL-min\({}^{-1}\). All other gas chromatography conditions were the same as those in Section 2.5. The mass spectrometer was operated at EI mode with ionization energy of 70 eV and a source temperature of 230 \({}^{\circ}\)C. The full-scan mode (MS1) was used for the volatile profiling with a mass range from m/z 50 to 600. The scan time was 250 ms. The data were recorded and processed using MassHunter 4.5 software (Agilent Technologies, USA).  
The volatile compounds were identified by matching mass spectra with mass spectral libraries such as NIST, Wiley, and Fiehn GC-MS libraries, and comparing them with retention index (RI) values reported in the literature. The identification of some compounds was also confirmed using authentic standards such as 2-AP, indole, naphthalene, and 1-hexanol. The RI values were obtained from the analysis of a mixture of n-alkane (C7-C40) under similar conditions [11; 12]. The relative concentration of identified volatile compounds were expressed relating to the internal standard, 2,6-DMP [13].
## 4 Discussion
### Rotary Stability  
The parameters selected by the rotary stability test were NS-SR and S-SF for the stability factors and S-ThF for the mobility factors. The test assesses multiplane stability of the pelvis, core, and shoulder girdle during performance of a combined upper- and lower-extremity movement. It represents the coordination of stability and mobility. To obtain a score of 2, the individual must flex the shoulder while extending the opposite-side hip and knee and then bring the elbow to the knee while maintaining spinal alignment with the board on the ground. Our results indicate that in the rotary stability test, 1-point scorers demonstrate obvious instability of the supporting hand on the nondominant side and more shaking of the raised hand on the dominant side, both of which involve larger shoulder ranges of motion. In contrast, instability of the supporting hand would restrict the tested leg ROM. We observed that 1-point scorers had a larger average ROM than 2-point scorers for NS-SR (90.66\({}^{\circ}\) vs. 66.37\({}^{\circ}\)) and S-SF (99.06\({}^{\circ}\) vs. 81.20\({}^{\circ}\)) and smaller average ROM for S-ThF (27.74\({}^{\circ}\) vs. 36.16\({}^{\circ}\)).