esa-sceva/satcom-chunk-collection huggingface dataset:
# Melanoma Brain Metastases: Immunotherapy or Targeted Therapy? A Systematic Review and Meta-Analyses
###### Abstract  
Brain metastases are one of the leading causes of death in melanoma patients. This systematic review and meta-analysis aimed to look at the variables that affect melanoma patients' intracranial treatment responses to immunotherapy and targeted therapy. **Methods.** A systematic search of PubMed and Scopus up to December 2023 was conducted to identify trials investigating treatment response of melanoma brain metastasis. This meta-analysis presents summary estimates (SEs) of treatment response and odd ratios (ORs) for the comparison between symptomatic and asymptomatic metastases. Generalised linear mixed models were used for the SE of the proportion of clinical responses and 95% CIs are reported. We investigated between-study heterogeneity using meta-regression. **Results.** We included 19 independent clinical trials for a total of 1074 patients with brain metastases. The SE of the overall response was 36% 95%CI [27%; 47%], \(\Gamma^{2}\) = 84%, similar to the SE for symptomatic metastases: SE = 29% 95%CI [16%; 47%], \(\Gamma^{2}\) = 80%. A significantly higher response of symptomatic metastases was observed between patients who had previously received immunotherapy compared to those who had not (47% vs. 9%, \(p\)-value = 0.001). The SE was greater for asymptomatic metastases (38% 95%CI [29%; 49%], \(\Gamma^{2}\) = 80%), and among these, patients that received the combo-immunotherapy importantly responded more than those who had received monotherapy (45% vs. 26.1%, \(p\)-value = 0.002). The major limit of our analysis is the absence of data about the specific intracranial response separately in asymptomatic and symptomatic patients in seven studies. **Conclusions.** This study shows the importance of starting immunotherapy as early as possible in asymptomatic patients. Randomised trials with greater statistical power are needed to find the best strategies for symptomatic and asymptomatic brain metastases.  
melanoma; brain metastases; immunotherapy; targeted therapy 2024
# Query-Based Cascade Instance Segmentation Network for Remote Sensing Image Processing
## 2 Methods
### Query  
The RPN-based prediction model has become the most frequently used effective method for extracting anchor boxes in various detection algorithms. However, the large number of redundant boxes it generates imposes a huge burden on the subsequent post-processing tasks. To alleviate this burden, query, an information factor that can circulate in the network, is introduced to make the detection process dense to sparse.  
Figure 3: The SW-MSA shifting window process.  
In this paper, we categorized the queries into two parts based on their different functions, namely the object query and the feature query. The object query can locate the coordinate information, while the feature query represents the visual features, such as texture and shape. The design concept of the object query is derived from [8, 42], which are used to represent the positional information. Specifically, the object query is set to a set of learnable proposal boxes (\(G\times 4\)), where \(G\) is the number of preset objects and 4 represents the coordinate information of the object bounding box. Namely, the center point coordinates (\(x\) and \(y\) coordinates) of the box, and the height and width of the box. During the training period, the coordinate information of the bounding box will be updated using back propagation to finally obtain the prediction result. Considering the significant role of visual features such as texture and shape in representing the object, we also introduce the feature query to encode the visual feature information of the object. The shape of the feature query is \([G,v]\), where \(G\) is the predetermined number of objects, which is the same as \(G\) in the object query, and \(v\) represents a high-dimensional vector (usually 256-dimensional) used to encode the visual feature information of objects. Therefore, the QCIS-Net not only uses object queries to obtain rough location information of the object, but also combines feature queries to further represent the visual feature information of the object. This helps to accurately detect objects in images. In addition, this query-based approach generates less redundancy and does not require complex postprocessing work.
## 3 MFDFA Algorithm
### Basic MFDFA Algorithm  
Detrended fluctuation analysis (DFA) was first proposed by [PERSON] et al. on the DNA analysis in 1995 [33] and then MFDFA was proposed by [PERSON] et al. in 2002 [34]. For the calculation methodology, [PERSON] developed MATLAB code in [35] and [PERSON] modified the algorithm in [7].  
The MFDFA method starts with a possibly non-stationary time series \(\{e_{i}\}\) for \(i=1,\ldots,N\), where \(N\) indicates its length.  
Define the \"profile\" \(E\) and transform original data into mean-reduced cumulative sums, \[E_{j}=\sum_{i=1}^{j}(e_{i}-\bar{e}),j=1,\ldots,N,\] (10) where \(\bar{e}\) is the mean of series, such that the aggregated time series are with zero mean.
2. Divide time series \(E_{j}\) into \(N_{\rm s}=\text{int}(N/s)\) non-overlapping segments of equal length \(s\), starting from the beginning. Since the length \(N\) of the series is often not a multiple of the considered time scale \(s\), in order to not miss any piece of data, another set of segments starting from the end of data is made from the end coming to the beginning. As a result, \(2N_{\rm s}\) segments are obtained covering the whole dataset.
3. Calculate the local trend \(p_{k}\) for each of the segments \(k=1,\ldots,2N_{\rm s}\) by a least-square fit of the series.
4. Calculate the mean square error \(F^{2}(k,s)\) for the estimate of each segment \(k\) of length \(s\). \[F^{2}(k,s)=\frac{1}{s}\sum_{i=1}^{s}(E[(k-1)s+i]-p_{k}[i])^{2},\] (11) for each segment \(k=1,\ldots,N_{\rm s}\) and \[F^{2}(k,s)=\frac{1}{s}\sum_{i=1}^{s}(E[N-(k-N_{\rm s})s+i]-p_{k}[i])^{2},\] (12) for each segment \(k=N_{\rm s}+1,\ldots,2N_{\rm s}\).
5. Average over all segments to obtain the \(q\)th order variance (or fluctuation) function \(F_{q}(s)\) for each size \(s\): \[F_{q}(s)=\left(\frac{1}{2N_{\rm s}}\sum_{k=1}^{2N_{\rm s}}[F^{2}(k,s)]^{q/2} \right)^{1/q}.\] (13) For \(q=0\) use \[F_{0}(s)=\exp\left\{\frac{1}{4N_{\rm s}}\sum_{k=1}^{2N_{\rm s}}\ln(F^{2}(k,s)) \right\},\] (14)
6. Repeat steps (2)-(5) for different \(s\) evaluating new sets of variances \(F_{q}(s)\).
7. Plot \(F_{q}(s)\) for each \(q\) in log-log scale and estimate the linear fit with least squares. If slope \(h(q)\) varies with \(q\), multifractality is suspected. Single slope shows monofractal scaling.
8. Calculate multifractal exponent \(t(q)\) as \[t(q)=qh(q)-1.\] (15)
9. Use Legendre transform to evaluate Holder exponent \(\alpha(q)\) and multifractal spectrum \(f(\alpha)\): \[\begin{cases}\alpha(q)=H(q)+qH^{\prime}(q)\\ f(\alpha)=q[\alpha(q)-H(q)]+1\end{cases}\] (16)  
The slope \(H_{q}\) of scaling function \(F_{q}\) with different \(q\) order demonstrates the LRD behavior of signals. The multifractal spectrum indicates how much dominant are the various fractal exponents present in the series. The width of the singularity spectrum denotes the range of the generalized Hurst (Holder) exponent, which is defined as  
\[\Delta(\alpha)=\max(h_{q})-\min(h_{q}), \tag{17}\]It is often used to quantitatively measure the degree of multifractality of the series. Thus, the wider the spectrum the more multifractal series is.
## 2 Methods
### Protocol  
The experimental procedure was explained to each subject, who then provided written consent. The subjects wore well-fitting sportswear with reflective markers and shoes in which the force plates were installed. During the experiment, the subject looked at the marker placed in front of the treadmill to ensure they maintained the central position of the treadmill. The experiment was conducted at two speeds: 3.5 and 4.0 km/h, which were determined through preliminary experiments. In the preliminary experiment, the subjects were startled by tripping at high speed such as 5.0 km/h. For ethical reasons, we could not perform an experiment under such a condition. Therefore, we selected 4.0 km/h as the fast speed to ensure that subjects were not frightened by tripping.  
The subjects performed two practice trials prior to recording. First, the subject walked on the treadmill for 5 min each at 3.5 and 4.0 km/h. Then, an additional 5-min practice trial was conducted with the subject wearing the half-covered goggles and headphones. Subsequently, several tripping trials were performed for practice and to test safety devices. The recorded trials began with walking at each speed without tripping to determine the subject's normal gait parameters. Following this, the subjects' recovery motions after tripping were recorded at 3.5 km/h and 4.0 km/h. The subject was tripped once at a random time while walking in the single tripping trial. Subjects were permitted to rest occasionally. Tripping was timed to occur in the early to middle swing phase to consistently assess the recovery strategy between trials. Tripping during this phase of gait was expected to result in the use of the elevating strategy [5]. We controlled the trip timing to focus on the effect of gait speed on tripping recovery. When tripped, the subject moved the tripped leg forward. The first and second steps after tripping were recorded and analyzed. Images of the tripping and recovery motions were reproduced and are shown in Figure 2. The subject was instructed to continue walking after being tripped. Therefore, the subject returned to their normal gait several steps after being tripped. The treadmill stopped after the recovery and a single trial was completed. Importantly, the treadmill speed and tripped leg were randomized between trials to prevent anticipation and learning. In total, 22 tripping trials were conducted for each subject.  
Figure 2: Tripping on the treadmill. The subject wore light-colored clothes and the left bar was not covered by black tape for better visibility: (**a**) Before tripping (**b**). Tripped timing (left foot). (**c**) First recovery step. (**d**) Second recovery step.
# Smart Waste Collection System with Low Consumption LoRaWAN Nodes and Route Optimization
## 3 System Proposed
### Developed Device
This amplifier receives load transducers' signals balanced in parallel and converts the small variations into digital measurements that are sent to the microcontroller. Due to these voltage changes, weight can be calculated by using the container weight when it is empty and calibrating the voltage difference with a known weight value. Regarding the temperature, it is measured by TMP36, an analog sensor. This sensor provides results with an accuracy of 1 \({}^{\circ}\)C. The temperature accuracy is not crucial for the developed sensor, but the protocol for communicating (analog in this case) is a key element. The essential element of the developed node is the microcontroller, the Atmel SAML21 model [75]. It is a 32 bit RISC processor belonging to the ARM Cortex-M-0+ ultra-low consumption processor family. This microcontroller has a consumption of 35 \(\upmu\)A/MHz in active mode and lower than 0.5 \(\upmu\)A in standby mode when working with 3.3 Volts. It can perform several tasks at the same time. In this case, it allows values to be obtained from sensors in parallel, as well as storing and processing them for sending. Choosing the microcontroller for the device requires checking efficiency rankings, such as ULPMark [76]. The one selected for this purpose is the only microcontroller with ARM Cortex-0+ architecture included in this ranking. A development board, Sammy-L21 [77], was firstly employed for a first prototyping in this microcontroller, as shown in Figure 4.  
Another key feature of this microcontroller is that it can perform customized logic operations due to its Configurable Custom Logic (CCL) unit [78]. This unit is really useful as it performs operations  
Figure 5: Load cell diagram and electric circuit  
that allow it to compare a temperature value against the previous one measured, i.e., compare the rise and evaluate if it is a fast rise or not. Depending on these data, it may send an alert. This utility is created by using programmable and logical doors. It does not depend on a synchronous code snippet--it asynchronously and continuously works, even if the microcontroller is in sleep mode, thereby increasing the energetic efficiency. Once the sensor has the relevant data and these data have been processed by the microcontroller, they are sent through a radio transceiver. The selected model for this purpose is a LoRa1276-C1, a sensor created by niceRF [79]. It has the relevant CE certification and provides great sound isolation as it isolates the radio sections from digital logic. In addition, it is worth noting its low consumption when in sleep mode, around 0.2 uA, which is an insignificant amount. The entire system receives power from a reduced dimensions battery located in the lower section of the system. The battery capacity is defined by the requirements of the case study. The analysis of the device's energetic consumption and the sizing of the required capacity are explained on the following pages.
## 3 Intelligent Contact Tracing Based on Transmission Characteristics
### Trajectory-Based Outdoor GPS Contact Tracing
#### 3.3.2 Step 2: Determine Exposed Case  
To reinforce the reliability of detecting close contacts outdoors, our system extends beyond the analysis of recent GPS data and incorporates historical trajectory information. The process is as follows.  
We consider a time window of \(C_{t}(u,v)\) with a size of \(t_{min ExposurePeriod}\) to determine whether a casual contact is a close contact or not. As the time window slides within \(t_{infectious}\), if the sum of \(C_{t}(u,v)\) during \(t_{min ExposurePeriod}\) exceeds the threshold (\(O_{threshold}\)), it  
Figure 3: Distance and similarity between the confirmed patient and potential contact in trajectory-based outdoor GPS contact tracing.  
is determined to be an exposed case (close contact). Figure 4 shows how exposed cases are determined during \(t_{infectious}\).
# Integrated Model Selection and Scalability in Functional Data Analysis Through Bayesian Learning
## 5 Results
### Results on Public Datasets
#### 5.2.2 Wind Speed  
The wind speed data, collected from 110 locations across Utah's Salt Lake Valley, range between 11 and 1440 measurements. The proposed method leverages 10 length scales selected from cross-validation and k-means clustering. Figure 7 illustrates the estimated mean function, curves of the observations, eigenfunctions, and covariance. The horizontal axis represents the seconds starting from 12:00 AM Greenwich Mean Time (GMT) on 15 June 2023, which corresponds to 6:00 PM in Salt Lake City. In Figure 7a, the estimated mean function depicts two pronounced peaks observed approximately at 8:00 PM and 6:00 AM, as well as two troughs around 12:00 AM and 12:00 PM. This pattern aligns with the diurnal cycle, particularly highlighting the thermal activity associated with sunset and sunrise. The peaks during sunset and sunrise are due to the interplay of topographical features, which result in specific breezes, such as the land breeze near the Great Salt Lake and the  
Figure 5: Cross-sectional visualization of eigenfunctions (eigenvalues) of the 4D simulation.  
distinct mountain and valley breezes. The troughs, on the other hand, reflect moments when the atmosphere is at its most stable, with minimal thermal activity disrupting wind patterns. The complexity of the data is distilled and represented using 12 descriptors with 17 basis functions. As Figure 7b shows, the primary eigenfunction is relatively level, indicating that the most significant variation is the location-specific average magnitude. Its profile echoes the influence of sunrise and sunset observed in the mean function, with elevations around 7:00 PM and 5:00 AM and subdued patterns during other times, indicative of similar atmospheric stability. The estimated covariance in Figure 7c highlights variance peaks around 8 PM and 5 AM, as well as a strong correlation between these periods. This underscores the effects of location-specific topographic factors on the wind speed.
# _Applied Sciences_
## 3 Experiments and Results
### Angiographic View Classification
The obfuscation method overcomes these limitations, since it is highly non-linear and no decryption key is involved. The C\({}_{4}\) experiment was run multiple times for different values of parameter \(N\). The results achieved on a testing subset are depicted in Figure 12. As expected, for \(N=1\), the accuracy drops to 50% (random guess) because all images become monochromatic. For the other values of \(N\), no monotonous tendency can be observed, suggesting that even for smaller values, enough details are preserved for the classification to be successfully performed.
## 2 Persisters of Foodborne Pathogens
### Definition of Persisters  
In 1944, [PERSON] termed persisters a subset of cocci of a _Staphylococci_ population that survived inactivation with penicillin as a non-growing dormant cellular phenotype without being genetically resistant [5]. Since then, persisters are mainly considered as physiologically dormant cells that can evade antibiotic treatments. However, recent research has questioned this concept. First of all, some types of persisters are correlated with active metabolism. For instance, increased metabolic activity leads to a higher level of persisters in a stationary phase population [47]. Intracellular _Salmonella_ persisters are able to secrete compounds to disturb the host immune system and avoid being killed [8]. Lastly, not all non-growing cells are persisters [48].  
In 2019, [PERSON], together with other 120 persistence-involved researchers, provided a common definition of antibiotic persistence and persisters [49]. In general, antibiotic persistence (1) is a phenotypic feature of a subset of a bacterial population that can survive bactericidal antibiotic exposure, (2) is experimentally characterized by biphasic kill curves, which means that, while a large part of the population is killed by an antibiotic, a smaller fraction survives for a longer time, and (3) is a non-heritable and a population-level phenomenon. Meanwhile, persisters are defined as tolerant cells isolated from persistent populations.  
According to the distinct way of persister formation, persisters are generally divided into two types [9]: Triggered (type I) persisters induced by a myriad of stress conditions, such as exposure to antimicrobial agents, energy limitation, acid, immune cells, and compounds, which are spontaneous persisters (type II) that are continuously generated during growth. Yet, instead of exhibiting a unified antibiotic resistance profile in a single given condition, persisters show heterogenous abilities to withstand antimicrobials due to different ways of obtaining persister-cells [50; 51]. This may indicate that a quiescent target or dormancy is not the only strategy used for surviving. Different mechanisms of persister formation lead to divergent persisters. The definition of a persister needs to be extended to other fatal stress conditions. To sum up, the current definition of persisters is still evolving.  
In this review, we define persisters as a genetically susceptible and growth-arrested subpopulation that have a non-inheritable capability of surviving under possibly fatal conditions and can regrow into normal sensitive bacteria after stress removal. In this case, divergent phenotypes fitting this definition can be termed persisters (Figure 2), such as in planktonic bacteria stress-induced growth-arrested persisters as well as stochastically-formed persisters, biofilm-contained persisters, small colony variants (SCVs), unstable L-form cells, intracellular persisters, and bacterial spores.
# Rate of Penetration Prediction Method for Ultra-Deep Wells Based on LSTM-FNN
## 6 Conclusions  
Ultra-deep well drilling is faced with challenges such as high temperature and high pressure at the bottom of the well, strong formation abrasiveness and staggered soft and hard formations. Accurate ROP prediction is of great significance for bit tool optimization and engineering parameter optimization.  
The influence of temperature, pressure and other parameters on the ROP should be fully considered in the process of the ROP prediction of ultra-deep wells. In this paper, the drilling mechanism and data correlation analyses are combined to optimize the input parameters of the model.  
In the process of establishing the ROP prediction model, the LSTM-FNN model is established to fully mine the time series information and nonlinear information between multiple parameters and the ROP. Compared with the single FNN and LSTM ROP prediction models, the MRE of the model is reduced by 8.07% and the \(R^{2}\) is increased by 0.27 at most. In addition, on the new data set, the accuracy and \(R^{2}\) reduction of the LSTM-FNN model are the smallest, indicating that the model has strong migration ability. This paper verifies that the optimal combination of network structures can improve the fitting ability of the algorithm, and this plays an important role in improving the accuracy and mobility of the model.  
Figure 9: Comparison chart of ROP prediction results: **(a)**the MRE of three ROP prediction models; **(b)** the \(R^{2}\) of three ROP prediction models.  
**Author Contributions:** Conceptualization, H.L. and X.S.; methodology, H.L., X.S. and Y.J.; software, H.L. and Z.P.; validation, H.L.; formal analysis, X.S. and H.L.; investigation, X.S. and H.L.; resources, Z.P.; data curation, X.S.; writing--original draft preparation, X.S. and H.L.; writing--review and editing, X.S. and Y.J.; supervision, Y.J.; funding acquisition, X.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by National Key Research and Development Program of China (funded by National Natural Science Foundation of China, No. 2019 YFA0708300), the Strategic Cooperation Technology Projects of CNPC and CUPB (funded by China National Petroleum Corporation, No. ZLZX2020-03), the National Science Fund for Distinguished Young Scholars (funded by National Natural Science Foundation of China, No. 52125401) and Science Foundation of China University of Petroleum, Beijing (funded by China University of petroleum, Beijing, No. 2462022 SZBH002).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data are not publicly available due to involve the information of Chinese oil fields and need to be kept confidential.  
**Acknowledgments:** The authors want to thank the National Natural Science Foundation of China and the CNPC for the financial support.  
**Conflicts of Interest:** The authors declare no conflict of interest.