esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background and Related Work
### Edge Intelligence  
EI is an emerging paradigm that effectively integrates edge and cloud computing within a continuum [7], being particularly effective for smart environments where challenges related to privacy, scalability, and economic viability are of utmost importance. If edge computing primarily focuses on processing and storage at or near the data source, regardless of its specific nature (traditional computers, smartphones, edge servers, etc.), EI aims to establish a more comprehensive computing ecosystem, where IoT devices are in the spotlight and provided with ad hoc, optimized AI models consistent with their resources' availability [10]. Being cloud-independent but cloud-interoperable, EI enables a collaborative framework with flexible workload distribution based on real-time conditions, performance requirements, and resource availability.  
Such a new paradigm is key because a large amount of data are produced in smart environments such as smart homes, commercial buildings, smart hospitals, etc. The ability to process these data in real time is crucial for sustaining the responsiveness and operational efficiency of these systems. EI fulfills this requirement by facilitating data processing in proximity to the data source--at the network's edge--rather than depending exclusively on centralized cloud servers [11]. This computational approach significantly reduces latency, thereby facilitating intelligent systems to react more promptly to varying environmental conditions, which is especially vital in domains where immediate decision-making is paramount, such as in healthcare and safety systems. Furthermore, EI ensures privacy, as it is not necessary to send the locally processed data to the cloud, which not only abides by privacy regulations but also minimizes the risk of data breaches. Privacy concern is especially related to smart environments where personal and sensitive information is mostly at stake, such as in smart homes and healthcare systems [12]. Another beneficial aspect of EI is its scalability. Smart environments are able to manage a large number of sensor devices without over burdening the underlying network. The scalability makes smart environments more efficient in terms of robustness and economics because limited volumes of data are transmitted to the cloud.
## 3 SAM-Based Approach to Improve Panoramic Image Segmentation in Indoor Construction Environments
### Object Extract
#### 3.1.1 Adjustment of Input Parameters  
As previously explained, SAM enables fully automated mask generation through point grid sampling, threshold-based filtering, and post-processing. These parameters can be user-defined, and since they involve a trade-off between mask quality and quantity, their optimal configuration is crucial.  
Table 1 summarizes the initial input parameters used in this study. The points_per_side parameter determines the number of points per side for grid generation, where a higher value results in finer segmentation but increases computational load. stability_score_thresh and pred_iou_thresh are thresholds between 0 and 1, representing mask stability and intersection over union (IoU), respectively. Higher values generate higher-quality masks but reduce the total number of detected masks. min_mask_region_area defines the threshold for post-processing, removing disconnected components and filling small holes within masks below the threshold size. To ensure the detection of new objects in each iteration, the points_per_side value was increased by 4 per iteration. Additionally, the initial value of stability_score_thresh was set to 0.97 and decreased by 0.002 per iteration. If no new objects were detected and the background proportion remained unchanged, the pred_iou_thresh value was reduced by 0.01 from its initial value of 0.85.  
Figure 1: Framework of the proposed method.  
Figure 3 illustrates the difference in the number and quality of detected objects when the stability_score_thresh is set high (0.97) versus low (0.85). When a high threshold (0.97) was applied, only 31 objects were detected, but they maintained overall high quality. In contrast, when a low threshold (0.85) was applied, 135 objects were detected. However, many masks exhibited overlapping regions or lower quality. In this study, we first set a high threshold to prioritize the detection of high-quality segmentation masks. Then, we gradually lowered the threshold to detect additional objects that were not captured in the previous stages.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Parameter** & **Initial Value** & **Variation** &
\begin{tabular}{l} **Conditional** \\ **Variation** \\ \end{tabular} \\ \hline points\_per\_side & 32 & \(+4\) & \\ stability\_score\_thresh & 0.97 & \(-0.002\) & \\ pred\_iou\_thresh & 0.85 & & \(-0.01\) \\ min\_mask\_region\_area & 100 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Initial input parameters and parameter variation.  
Figure 3: Comparison of segmentation results based on threshold differences.  
Figure 2: Calculation of background proportion by cumulative summing of the foreground.
# A Survey of Deep Learning for Electronic Health Records
## 5 Deep Learning Applications in EHR
### Medical Prediction
#### 5.3.2 Temporal Medical Prediction  
In contrast to static medical prediction, this type of prediction task focuses on processing data based on time series and predicting the outcome or predicting the occurrence of a disease.  
[PERSON] et al. [74] proposed a framework of end-to-end DL + classic ML to perform Chronic Heart Failure (CHF) detection using heart sound data for the first time. The accuracy of the method reached 92.9%, which is already very close to the accuracy of the manual assessment. The STAN model using GCN for Spatio-temporal modeling proposed by [PERSON] et al. [73] was tested on COVID-19 data using real data for epidemiological prediction. According to the task, they performed graph construction, using location information as graph nodes to construct graph edges with geographic proximity and node population size, and fusing static features with dynamic node features in the graph nodes. The results are better compared with the epidemiological model SIR, SETP, and other deep learning algorithms [28; 96; 97].  
Moreover, mixing temporal information with textual data is becoming a popular choice to improve the performance of medical prediction tasks. [PERSON] et al. [75] proposed a multimodal architecture for mortality prediction. This architecture LSTM network performs representation extraction of time series, text embedding of text data from clinical records with CNN, and finally, fuses the feature vectors of both types for prediction. This multimodal model performed much better on the mortality prediction task than the model using a single data type, scoring eight percentage points higher on AUCPR than the LSTM and 0.3 percentage points higher than the previous multimodal model Multi-CN. [PERSON] et al. [76] proposed BiteNet with a self-attentive module called Masked Encoder (MasEnc). This special self-attentive mechanism would extract contextual and temporal relationships from the medical text. Compared with previous work [68; 98; 99; 100], BiteNet achieves better prediction performance on long sequences of medical texts.
## 3 New Protocol Concept  
In this section, we present a new routing protocol named the multi-path heterogeneous ad hoc routing protocol \"MHAR\" that ensures sustainable connectivity within a heterogeneous network while respecting the characteristics of each component.  
As shown in Figure 1, the main idea of the protocol is to exploit route discovery to choose the best path for a specific type of data exchanged between communicating nodes. Established paths are not only made up of a single category but can also be made up of two or more depending on the traffic density and the actual distribution of nodes making up the path.  
When multiple paths are computed, they are then classified based on the characteristics of the nodes that compose them, and packets will be delivered through the routes destined to a specific type of data (critical, informative, or preventive).  
These options give our protocol a specific structure made up of different stages linked to guarantee the transmission of data from the source to the destination:  
* The identification step allows each node to assign a class identifier to its neighbors according to their range and relative speed,
* The classification of the computed paths based on the class identifiers assigned to the nodes which compose them,
* The choice of the transmission path linked to the nature of the transmitted data,
* Data analysis determines the nature of the transmitted data: 1. Critical data (311) is very important when it comes to sending a fast and reliable path. The pH of the earth in precision agriculture is a very important measure. If it ever reaches a critical threshold, then the action must be taken immediately. 2. Preventive data (312) is shared information to warn of potential danger. It must be transmitted reliably, but not necessarily via the fastest route. In the forestry sector, a sensor sends the reached temperature in precise time to a specific location to notify the command post. 3. Informative data (313) is data communicated periodically on a less important path (the longest among those calculated) that is sufficient for its transmission. For example, a taxi regularly sends its position to its transport company.  
Before bringing the MHAR protocol into the real world and defining its packet and message formats, it is important to experiment with its operational concept in a basic study environment. For this purpose, we carried out a comparative study between different types of ad hoc networks (Table 1).  
According to this comparison table, we notice that the MANET, VANET, and FANET networks can constitute an example of a hybrid network regrouping mobile, vehicular, and flying components using various versions of the same protocol OLSR standard. The latter already has a multi-path version MPOLSR [27] that improves the quality of communication by affecting various aspects: security, bandwidth, load balancing, and transmission delay.  
Based on this analysis, we decided to apply the concept of our MHAR protocol on OLSR, producing a new multi-path version, called MHAR-OLSR, that ensures the communication inside the MANET VANET FANET heterogeneous ad hoc network (HAdN_MVF).
## 4 Results and Discussion
### Selection of Landslide Conditioning Factors  
The importance scores for all 12 landslide conditioning factors obtained by Random Forest are shown in Table 2. The importance of all landslide conditioning factors was greater than 0, indicating that they all contributed to the prediction. Elevation achieved the highest importance score of 0.219, followed by NDVI (VI = 0.198), TWI (VI = 0.103), curvature (VI = 0.099), distance to rivers (VI = 0.087), distance to roads (VI = 0.073), SPI (VI = 0.059), slope aspect (VI = 0.046), terrain roughness index (VI = 0.042), slope (VI = 0.023), surface cutting depth (VI = 0.023) and terrain relief (VI = 0.021). As all factors contributed to the prediction, all 12 factors were used for evaluating landslide susceptibility in this study.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Landslide Conditioning Factors** & **VI** \\ \hline Elevation & 0.219 \\ NDVI & 0.198 \\ TWI & 0.103 \\ Curvature & 0.099 \\ Distance to rivers & 0.087 \\ Distance to roads & 0.073 \\ SPI & 0.059 \\ Slope aspect & 0.046 \\ Terrain roughness index & 0.042 \\ slope & 0.023 \\ Surface cutting depth & 0.023 \\ Terrain relief & 0.021 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Variable importance of the landslide conditioning using the Random Forest method.
## 4 Experiments and Analysis  
### Datasets and Evaluation Metrics  
This section delineates the datasets utilized in the experiments and the evaluation criteria employed.  
We evaluate the performance of our proposed maneuver prediction method using the publicly available Brain4 Cars dataset [33]. This dataset comprises 594 video segments, TheBrain4 Cars [3] dataset includes driver observation videos (1088 px \(\times\) 1920 px, 25 fps) and videos of the outside scenes (480 px \(\times\) 720 px, 30 fps) recorded simultaneously. The dataset consists of five driving maneuver categories: go straight, left lane change, left turn, right lane change, and right turn. Moreover, samples with no simultaneous recordings of the inside and outside view are considered invalid and not further used in our study.  
We use a 5-fold cross-validation for all the experiments in this work, aligning with previous works using the Brain4 Cars dataset [3, 4, 6, 7, 8]. The final evaluation metrics include average accuracy and F1 score, along with their standard deviations.  
In this research, we utilize accuracy, F1 score, and confusion matrix to evaluate the driver intent recognition performance of both the proposed model and other models. Accuracy (Acc) and F1 score are computed using Equations (4) and (7) [34, 35], outlined as follows:  
\[\text{Acc}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FP}+\text{TN}+\text{FN}} \tag{4}\]  
\[\text{Pr}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{5}\]  
\[\text{Re}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{6}\]  
\[\text{F1}=\frac{2\text{Pr}\cdot\text{Re}}{\text{Pr}+\text{Re}} \tag{7}\]  
In the equations, TP stands for true positive, indicating cases where both the true label and the predicted label are positive; TN stands for true negative, signifying instances where both the true label and the predicted label are negative; FP corresponds to false positive, representing situations where the true label is negative, but the predicted label is positive; and FN denotes false negative, indicating scenarios where the true label is positive, but the predicted label is negative. Pr and Re refer to precision and recall, computed using Equations (5) and (6), respectively, while the F1 score is the harmonic mean.
## 5 Specific Application of Topic Identification Models and Algorithms
### Application of LDA Theme Model and Its Derivative Models
#### 5.1.2 The Derivative Models of LDA Theme Model  
In light of the limitations of traditional LDA in determining the number of topics using a Bayesian model or perplexity calculation, [PERSON] proposed a topic model called hLDA based on hierarchical probability, and used time information to automatically mine potential topic information from scientific and technological documents [49]. Aiming at the problem of the fixed time window that arises in the traditional online topic model OLDA, [PERSON] introduced a topic similarity matrix into the model and proposed a variable online topic model VOLDA model [50]. [PERSON] introduced a dynamic weight calculation method to produce the IOLDA (improved OLDA) online topic model that can identify science and technology information topics [51] in view of problems such as the mixing of new and old topics caused by the fixed weight of the content evolution matrix in the traditional OLDA model. [PERSON] introduced word vector distribution into LDA and proposed the LDA2 vec topic model [52]. [PERSON] et al. proposed a CO-LDA model based on a combination of word co-occurrence analysis and the LDA model to mine online medical comment topics [27] in view of the issues of semantic sparsity and insufficient co-occurrence information in LDA models used for medical comment topic mining. [PERSON] and [PERSON] selected the PLDA model from Kmine experimental platform to identify themes in the graphene field [28]. [PERSON] et al. identified topics and keywords in the comment texts based on a parallel potential Dirichlet distribution model, PLDA [6].
# Recent Advances in Extraction and Stirring Integrated Techniques
## 2 Stir Bar Sorptive Extraction
### ICE Concentration Linked with Extractive Stirrer  
The improvement of the extraction yield can be also achieved by the proper modification of the experimental conditions. Recently, [PERSON] et al. have proposed the so-called ice concentration linked with an extractive stirrer (ICECLES) which combines the advantages of freeze concentration and SBSE ([PERSON] et al., 2016).  
Freeze concentration can be considered as an innovation in analytical sample preparation although it is frequently used in the food industry to concentrate liquid products. It is based on a simple and well known principle, freezing-point depression. This principle states that solutions have lower freezing points than pure water. As a consequence, when a sample is cooled down, ice starts to form, excluding the solutes which are concentrated in the remaining liquid.  
ICECLES operates in a simple and well established workflow (see Figure 3). A sorptive bar is introduced into a liquid sample and is stirred continuously. During the extraction, the sample is cooled down, inducing the freeze concentration. As a result, the solution is enriched with the analytes establishing a higher concentration gradient with the stir bar, thus increasing the extraction yield. In addition, the lower temperature increases the sorption of the analytes as this process is exothermic.  
ICECLES provides signal enhancement factors of 450 over conventional SBSE for the extraction of (semi)volatile compounds, which makes it a very promising technique in the coming years.
# Deep Learning-Based Next-Generation Waveform for Multiuser VLC Systems
## 5 Conclusions  
We investigated index modulation (IM) with Orbital Angular Momentum (OAM) multiplexing and Orthogonal Frequency Division Multiplexing (OFDM) for the multiuser communication system. To support multiple users, Power Domain Non-Orthogonal Multi  
Figure 10: User 3 and user 4 BER of OFDM-IM and OAM-IM for LoS/NLoS VLC channel.  
ple Access (NOMA) was considered. A Successive Interference Cancellation (SIC) mechanism with maximum likelihood (ML) and a CNN-based detection method were utilized at the receiver end to decode the users' data. The simulation results clearly demonstrated that OAM-IM generally outperforms the OFDM-IM-based VLC system. The use of IM in conjunction with OFDM and OAM showed to enhance system efficiency, at least in terms of the BER parameter. As evident through simulation results, OAM has substantially greater spectral efficiency than OFDM, which was further enhanced by IM. Moreover, the detection error was reduced for both users; however, it was observed to be more significant in users that had a low NOMA power factor. The detection error was further lowered in deep learning-based systems to around 20% and around 30% for low SNR and high SNR regions, respectively. The most significant finding in this study is that AI-based VLC systems can perform better; however, designing an optimal AI network requires effort. Based on the findings of this study, future research will attempt to develop multiuser communication systems using similar methodologies and text in a more realistic environment and explore other AI algorithms, in addition to considering channel estimation algorithms for better suitability of the system.  
Methodology, A.A.; Supervision, H.M.A.; Writing--original draft, A.A.; Writing--review & editing, N.T. and K.R. All authors have read and agreed to the published version of the manuscript.  
This research work received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results
### Chlorophyll Fluorescence  
Drought stress influenced the chlorophyll fluorescence parameters at all levels of fertilization. A significant increase in the values of the fluorescence parameters measured after dark adaptation (Fm, Fv/Fm) and a decrease in those after light adaptation (Yield and ETR) were observed as a result of drought (Table 3). As a result of the closing of the stomata during drought stress, the demand for photochemical energy decreased, which resulted in a decrease in the quantum efficiency of the photochemical reaction in PSII, as evidenced by the yield parameter, as well as the electron transport rate (ETR). However, drought stress was not severe and did not cause permanent damage to the photosynthetic apparatus. On the contrary, the increased Fm and Fv/Fm values indicated the mobilization of plants to adapt to the conditions of limited water availability. An increase in the value of this parameter means a disruption occurred in the energetic antennas related to the capture of light or its transport between photosynthetic pigments (mainly chlorophyll a). Similarly, the highest (most favorable) values of the parameters Fm and Fv/Fm were observed for the plants fertilized with HI frass. Significantly higher quantum yield of photosynthetic energy (yield) values were demonstrated in the plants fertilized with HI frass, but only under conditions of optimal irrigation. In turn, the electron transport rate turned out to be faster in plants fertilized with HI frass, both in drought and optimal irrigation conditions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Water Regime** & **Fertilizer** & **Dose (g/L)** & **Leaf Biomass (g/L)** \\ \hline \multirow{4}{*}{Watered} & Control & - & 29.6 ab \\  & Cattle manure & 10 & 25.7 ab \\  & HI frass & 10 & 36.4 a \\  & HI frass & 12.5 & 30.4 ab \\ \hline \multirow{4}{*}{Drought} & Control & - & 12.6 bc \\  & Cattle manure & 10 & 9.5 c \\  & HI frass & 10 & 9.9 bc \\  & HI frass & 12.5 & 10.5 bc \\ \hline \hline \end{tabular}
\end{table}
Table 1: Lettuce leaf biomass under different fertilizers and water conditions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Water Regime** & **Fertilizer** & **Dose (g/L)** & **A** & **E** & **Gs** & **Ci** \\  & & **(g/L)** & **(mmol/m \(\times\) s)** & **(mmol/m \(\times\) s)** & **(mmol/m \(\times\) s)** & **(tvm)** \\ \hline \multirow{4}{*}{Watered} & Control & - & 3.30 bc & 0.97 bc & 0.04 bc & 219 ab \\  & Cattle manure & 10 & 4.91 ab & 1.43 ab & 0.06 abc & 220 ab \\  & HI frass & 10 & 6.14 a & 1.71 ab & 0.08 ab & 216 ab \\  & HI frass & 12.5 & 6.62 a & 2.22 a & 0.11 a & 246 a \\ \hline \multirow{4}{*}{Drought} & Control & - & 0.51 d & 0.17 c & 0.00 c & 233 ab \\  & Cattle manure & 10 & 0.84 d & 0.23 c & 0.00 c & 204 ab \\  & HI frass & 10 & 1.51 d & 0.33 c & 0.01 c & 157 b \\  & HI frass & 12.5 & 1.06 d & 0.27 c & 0.01 c & 166 ab \\ \hline \hline \end{tabular}
\end{table}
Table 2: Lettuce photosynthetic activity under different fertilizer and water conditions.