esa-sceva/satcom-chunk-collection huggingface dataset:
# Improvements of the xAAL Home Automation System
## 5 The xAAL Communication Protocol
### Security Considerations
#### 5.6.2 Cloud Communication
Such connected microphone records sounds at home all the time; when a sound matches a given keyword, the sound stream (or chunks) is pushed to computers of the company of the connected microphone; the sentence is analyzed by a speech-to-text process; if keywords associated with home automation are recognized then the text is pushed to computers of the home automation company; if some home automation commands are recognized then these computers send commands to devices at home to which they are permanently connected. As a result, the end user may tell requests to its house and see it in action, which is very impressive. This kind of universal interface is somehow a kind of interoperability in the case where there are several home automation technologies at home. In fact, there is no real inter-operation between them, just a single user interface. For now, this is performed in the cloud, this is why such solutions are called 'interoperability in the cloud'. In fact, this could also be performed locally at home. Speech-to-text is a complex process, and is a precious added value for companies that manage it. On-line (cloud) solutions seem to be of better quality than off-line (local) solutions. However, depending on the user's real needs and feeling, local solutions are good enough for simple interaction (recognize keywords), as for home automation services. Moreover, there is no convincing evidence that speech-to-text algorithms executed nowadays by cloud computers could not be performed by local devices. Companies promise continuous improvements of their secret algorithms by collecting the voice and sentences of their consumers in the cloud, possibly with the help of \"digital labor\" services [38]. This process of interoperability in the clouds also reaches its limits while trying to get interoperability between competing solutions. For instance, there is nowadays no way to make an Alexa box and a Google Home system interoperate. There is no evidence of any effort in this direction, even if both do almost the same thing. There is actually no way to set up a scenario mixing devices belonging to the two realms. The consumer has to buy devices in double, or to stay bound to one world. The interoperability is pushed away, in the clouds, but not really addressed. To sum up, performing home automation 'in the clouds' raise many safety, security, performance and ethical issues, without really providing interoperability. There is no real technical obstacles to perform this completely locally, at home, without any leak of information in the clouds or to third parties. xAAL is designed with this idea in mind.
# ATWin: An Improved and Detailed Startup Model of TTP/C
## 2 Background Work and Literature Review
### The Startup Task of the TTP/C Cluster  
The startup task of the TTP/C cluster centers on the establishment of consistent communication parameters for asynchronous nodes within a limited time, such as GTB, group membership vector (GMV), initial time slots (ITS) and other parameters. There are two core problems in the startup phase, including determining the upper bound for startup time and addressing contention issues.  
**A. Contention during startup**  
This paper focuses on the contentions that occur in bus-based deployments. Due to the propagation delay of the physical layer channel, nodes initiating startup that are unable to detect each other's presence in time may simultaneously perform cold-startup, resulting in concurrent startup. The phenomenon of contention is fundamentally a consequence of concurrent startup and can be categorized into two types: physical contention and logical contention, as noted by [PERSON] [15]. Physical contention arises from overlapping frames, whereby multiple nodes access the common communication medium to transmit message frames nearly simultaneously. In bus-based deployments, logical contention occurs when the message frames sending time is shorter than the propagation delay time within the system, resulting in the message failing to completely occupy the entire channel. In such  
Figure 1: The time slot structure of TTP/C.  
an instance, if remote nodes attempt to send frames nearly simultaneously, there may be contention due to frame overlapping.  
**B. The upper bound of startup time**  
It is requisite for a TTP/C cluster to complete startup in a limited time. The upper bound of the startup time is a critical requirement for the system. The accurate upper bound of startup time is essential to validate the startup model's effectiveness in practice. It is influenced by factors such as system size, the contention-removing strategy, and delay parameters. Prior studies, including [PERSON] et al. [18] and [PERSON] et al. [27], have employed model checking methods to estimate a coarse-grained upper bound of startup time for TTP/C clusters with a fixed number of nodes. In these methods, the initial upper bound is subjectively estimated, such as one slot, and subsequently adjusted until the model checking method fails to provide a counter-example. However, the credibility of these methods is related to the startup model. In addition, the current research lacks a fine-grained formal representation of the upper bound of startup time for a TTP/C cluster with any number of nodes.
# Modern Techniques for Flavonoid Extraction--To Optimize or Not to Optimize?
## 3 Sample Preparation and Hydrolysis
Of course, the choice of the right hydrolysis technique depends on the experimental design and the desire to detect the flavonoids we are interested in. After the sample has been prepared and stored appropriately, various liquid-liquid or solid-liquid extractions can be used to extract flavonoids. The most commonly used method is extraction by solvents (methanol, ethanol, acetone, water, etc.). Unfortunately, this method consumes large amounts of solvents, is time consuming, and can result in low extraction efficiency. For this reason, new technologies, new solvents, and new approaches are constantly being sought to eliminate some of the aforementioned drawbacks of the so-called traditional methods. Some of the new approaches are the application of so-called green technologies such as:  
* ultrasound-assisted extraction (UAE)
* enzyme-assisted extraction (EAE)
* microwave-assisted extraction (MAE)
* pulsed electric field-assisted extraction (PEFAE)
* supercritical fluid extraction (SFE)
* liquid extraction under pressure (PLE)
* ohmic heater-assisted extraction (OHAE)  
Each type of method has a specific scope depending on the target molecules to be extracted or on the plant material. The most commonly used methods for flavonoid extraction are UAE, MAE, SFE and PLE. Although these methods are described as safe, efficient, suitable for extraction of sensitive compounds, short, etc., they must be carefully selected based on the sample and target compounds, as mentioned earlier. The selection of a suitable method still does not guarantee maximum extraction efficiency, so the selected methods usually need to be optimized. In general, all extraction techniques require optimization of variables such as particle size, solvent for extraction, temperature, time and sample-to-solvent ratio (Figure 3), and these variables should be the starting point for optimization of the extraction protocol. The selection of the other variables that should be optimized depends strongly on the extraction technique. Some of them are discussed in this article depending on the chosen extraction technique.
# SARFNet: Selective Layer and Axial Receptive Field Network for Multimodal Brain Tumor Segmentation
###### Abstract  
Efficient magnetic resonance imaging (MRI) segmentation, which is helpful for treatment planning, is essential for identifying brain tumors from detailed images. In recent years, various convolutional neural network (CNN) structures have been introduced for brain tumor segmentation tasks and have performed well. However, the downsampling blocks of most existing methods are typically used only for processing the variation in image sizes and lack sufficient capacity for further extraction features. We, therefore, propose SARFNet, a method based on UNet architecture, which consists of the proposed SL\({}_{4}\)RF module and advanced AAM module. The SL\({}_{4}\)RF downsampling module can extract feature information and prevent the loss of important information while reducing the image size. The AAM block, incorporated into the bottleneck layer, captures more contextual information. The Channel Attention Module (CAM) is introduced into skip connections to enhance the connections between channel features to improve accuracy and produce better feature expression. Ultimately, deep supervision is utilized in the decoder layer to avoid vanishing gradients and generate better feature representations. Many experiments were performed to validate the effectiveness of our model on the BraTS2018 dataset. SARFNet achieved Dice coefficient scores of 90.40, 85.54, and 82.15 for the whole tumor (WT), tumor core (TC), and enhancing tumor (ET), respectively. The results show that the proposed model achieves state-of-the-art performance compared with twelve or more benchmarks.  
brain tumor segmentation; MRI; medical image; deep learning; UNet +
Footnote †: journal: Medical Image Computing
## 4 Experiments
### Second Experiment  
The second environment is more complicated because in the initial phase, the camera view is limited by the walls around it, as illustrated in Figure 7. Therefore, after the initial phase, more than 80% of the unknown space remains. The NBV algorithm should search these locations by gradually acquiring new information about the environment.  
For this more complex environment, we refine our selection of weight configurations based on the findings from the first experiment. The extremal configurations IG1_T0_R0 (pure information gain) and IG0_T1_R0 (pure distance minimization) are retained to serve as baselines. The IG0_T0_R1 (pure rotation) and IG1_T1_R1 (equal weights) configurations are omitted, as the first experiment demonstrated their suboptimal performance.  
Based on our initial findings, we hypothesized that a successful strategy must balance the aggressive exploration of information gain with the efficiency of distance minimization. Therefore, we introduce two new configurations to test this trade-off: IG1_T05_R01 and IG1_T025_R01. These configurations heavily prioritize information gain but add a small, non-zero weight for and rotation to act as penalties for overly long or complex movements. This allows us to investigate whether a small travel cost consideration can temper the otherwise greedy nature of an information-gain-only approach.  
The results shown in Figure 8 meet the expectations. Again, the more the information gain is preferred, the faster the space is searched, but there is an increased risk of getting trapped in the local maximum. On the other hand, the more distance minimization is preferred, the more iterations the algorithm requires, but the total distance traveled is shortened, as shown in Table 2. The best results are marked in bold.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Measurement** & **JD [Rad]** & **CPC** & **CD [m]** & **OC [Rad]** & **RDM [m]** \\ \hline IG1\_T0\_R0 & 253.24 & 72.34 & 77.88 & 201.79 & 81.45 \\ IG0\_T1\_R0 & 174.34 & **46.63** & 71.73 & **156.49** & 72.62 \\ IG0\_T0\_R1 & 206.29 & 64.67 & 66.12 & 204.54 & 70.52 \\ IG1\_T1\_R1 & **173.60** & 50.41 & **55.99** & 176.28 & **58.89** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sums of criteria values computed by [26] in the first experiment.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Measurement** & **JD [Rad]** & **CPC** & **CD [m]** & **OC [Rad]** & **RDM [m]** \\ \hline IG1\_T0\_R0 & 628.62 & 199.51 & 217.56 & 696.77 & 220.48 \\ IG0\_T1\_R0 & **405.04** & **109.43** & **140.43** & 447.56 & **141.21** \\ IG1\_T025\_R01 & 428.76 & 130.10 & 140.91 & **399.45** & 144.82 \\ IG1\_T05\_R01 & 470.46 & 134.67 & 165.89 & 484.18 & 167.93 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sums of criteria values computed by [26] in the second experiment.  
Figure 7: (**a**) The second experiment setup; (**b**) the occupied and unknown space after the initial phase.
## 4 Performance Evaluation  
As it is costly to have testbed or implementation in a large scale, we have decided to study the performance of the network using MATLAB, where various conditions and different sets of simulations have been conducted using real life parameter values and features that would help us understand the behavior in the design and planning phase of the network. 100 CR sensor nodes deployed non-uniformly in a 1000 m \(\times\) 1000 m area, which can emulate a 100 hectares' forest. We assume each node is equipped with a supercapacitor such as 3 FNESSCAP [45], as energy storage for harvested energy. At the initial stage, the supercapacitor is considered fully charged and it can hold up enough power for 300 events. Simulation runs for 1000 rounds and in each round 30 randomly chosen sources transfer a packet to 30 randomly chosen destinations using the shortest available path, where several performance metrics such as average packet delivery ratio (PDR), average hop-count, and consumed power are studied. To achieve reliable results, reducing the effect of randomness and obtaining sufficient confidence, we run the simulation with 50 different network setups. Results are averaged with a 90% confidence interval. Simulation parameters are summarized in Table 1.
# A Parametric Identification Method of Human Gait Differences and its Application in Rehabilitation
## 2 Methods
### The Process of Implementation  
For the sake of illustrating the whole procedure clearly, the application process of expressing the differences of gait curves by the differences of shape parameters to make a distinction between normal and abnormal gait is shown in Figure 3. For the normal group, the three average gait curves of the participants were initially fitted by the GE-Bezier curve. The appropriate number and the coordinates of commonality points of each average curve was selected and calculated. Then, the mathematical model of each joint was established under the determined commonality points, and the normal ranges of shape parameters (values from maximum to minimum) were summarized through the models.  
Figure 2: The relationship between local shapes and three shape parameters of the GE-Bézier curve with five invariant control points (\(P_{1}\),..., \(P_{5}\)): (**a**) The curve with three appropriate waves when the shape parameters were all median values; (**b**) The first wave changed with the alteration of first shape parameter; (**c**) The second wave fluctuated if only the second shape parameter was changed; (**d**) The third wave varied with the third shape parameter; (**e**) The grey area represents the range of curve shapes within the values of shape parameter ranges.  
For the patient group, the gait curves were directly fitted by the mathematical models derived from the normal group to calculate the shape parameters of each patient. Then, the shape parameters were analyzed to see whether they belong to the normal ranges. If most shape parameters of well-recovered patients belonged to this range, while most shape parameters of patients with poor recovery were beyond this range, this could indicate that this method can be used to assess rehabilitation status, so its validation is verified.
## 3 Results
### PSEFS Abolishes Tumor Growth in Xenograft Model Bearing Bladder Cancer T24 Cells  
To estimate the antitumor efficacy of PSEFS in vivo, various concentrations of PSEFS (0. 20, and 200 mg/kg) were administered by oral gavage to nude mice harboring T24 tumor xenografts, with the chemotherapy drug cisplatin (5 mg/kg) used to compare side effects and antitumor efficacy. Administration of PSEFS promoted a dose-dependent decrease in tumor growth in xenografted mice (Figure 5A). Additionally, both Ki-67 and hematoxylin and eosin (H&E) staining of tumor tissues revealed a diminished number of tumor cells in PSEFS-treated mice (Figure 5D), and tumor volume in PSEFS-treated mice was also reduced relative to controls (Figure 5B). Measurement of weight loss to assess the potential side effects of PSEFS treatment revealed that body weight remained unchanged in PSEFS-treated nude mice, whereas those administered cisplatin showed a \(\sim\)20% decline in body weight (Figure 5C). Moreover, plasma activities of the liver enzymes alanine transaminase (ALT) and aspartate transaminase (AST) were similar between PSEFS-treated mice and controls (Figure 5E), and no clear difference was observed in the activity of the heart-specific enzyme creatine kinase (CK) between PSEFS-treated and control mice (Figure 5E). These results supported the hypothesis that PSEFS does not produce negative side effects.  
Figure 4: PSEFS inhibits T24 cell migration and invasion by reducing MMP-9 activity and expression through transcriptional repression. T24 cells were incubated with the indicated concentrations of PSEFS for 24 h. (**A**) Migratory potential was investigated (40\(\times\) magnification) by a wound-healing migration assay. (**B**) T24 cell invasiveness was estimated using a Matrigel-coated chamber transwell assay. (**C**) Both MMP-2 and MMP-9 activity were assessed by gelatin zymography. Relative protein levels of MMP-9 were examined by immunoblot. (**D**) Investigation of DNA binding by SP1, NF-kB, and AP-1 to the cis-element of the MMP-9 promoter according to EMSA. Data represent the mean \(\pm\) SD of triplicate experiments. \({}^{*}\)\(p<\) 0.05, \({}^{**}\)\(p<\) 0.01 vs. control.
## 4 Introducing Samza
### Samza APIs  
Samza provides two different APIs that can be exploited to build applications: High-level or low-level APIs. In both cases it is possible to describe the application logic in its entirety, with the only difference that with high-level APIs the application processes message streams, while with low-level APIs single messages are the processing unit. Each Samza task receives data from an input stream (possibly split in more than one partition), process such data, and emits results to an output stream (in turn, possibly split in multiple partitions).  
When using high-level APIs, the stream processing application is represented as a Directional Acyclic Graph (DAG) operating on streams (each implementing the MessageStream <M> interface, where M is the type of message contained in the stream). To create an application using high-level APIs, the StreamApplication interface has to be implemented, by specifying the describe() method, where the (input and output) streams are described, together with the operations to be performed on each message of type M.  
On the other hand, the use of low-level APIs allows to specify operations on a single message in an object representing a real task (logical unit of parallelism). For this, it is sufficient to implement the TaskApplication interface, again by specifying the describe()method, to indicate that each message will be processed by another object, implementing either the StreamTask or the AsyncStreamTask interfaces, representing, respectively, synchronous and asynchronous tasks. To implement such interfaces, one needs to specify the process(M) method, describing how the message M has to be processed (the asynchronous interface also requires a callback argument, which is a function that will be invoked when processing of a message is terminated). The advantages of asynchronous communication are clear: Each task in the graph is allowed to process data at its convenience, while synchronous communication is necessary, for example, when a timely response is needed.
# A Novel Interpretable Deep Learning Model for Ozone Prediction
## 2 Materials and Methods
### Data  
#### 2.2.1 Study Regions and Datasets  
Ozone pollution is generally a regional issue, so we selected nine monitoring stations in several cities (Nanjing, Beijing, Chengdu, Guangzhou and Wuhan) that suffer from severe ozone pollution to represent the corresponding regions. The data used in our experiments comprised the air quality (including AQI, PM\({}_{2.5}\), PM\({}_{10}\), SO\({}_{2}\), NO\({}_{2}\), O\({}_{3}\) and CO) and meteorological (including radial wind, temperature, relative humidity and Residual Boundary Layer Height) data of those monitoring stations from January 2015 to December 2021. The longitude and latitude of each monitoring station in each city are shown in Table A1 and the geographical distribution of these monitoring stations is shown in Figure 2. These stations were distributed in different regions of their city, like the teaching area, downtown area, industrial area, etc.  
The air quality data included the hourly PM\({}_{2.5}\), PM\({}_{10}\), NO\({}_{2}\), CO and SO\({}_{2}\) concentrations and the air quality index (AQI). The meteorological information included ERA5 near-surface wind speed, wind direction, temperature, relative humidity and planetary boundary layer height. In each monitoring station, we took the data from January 2015 to December 2020 as the training data and January 2021 to December 2021 as the testing data, which was used to evaluate the performance of the final trained model. Linear interpolation [58] was used to fill in the missing data, because linear interpolation is simple and able to mostly reserve statistical features of original data [59]. Normalization was applied to the air quality, wind speed and temperature data to achieve a faster convergence. We transformed the raw dataset into a supervised dataset, consisting of input-output pairs based on a sliding window. For details of the data flow, we took the past 24 h air quality  
Figure 1: The model structure of the Spatiotemporal Attentive Gated Recurrent Unit (STAGRU).  
and meteorological data from all stations in a city as the input. Subsequently, 24 h O\({}_{3}\) forecasts of the target station were obtained via the STAGRU.