esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### SPME-GC-MS Analysis  
WOF sample composition analysis was performed using headspace solid-phase microextraction/gas chromatography-mass spectrometry (HS-SPME/GC-MS), as described by [PERSON] et al. [41]. Approximately 0.5 g of fresh WOF, after removing the seeds, was placed in a 15 mL glass vial, followed by the addition of 10 \(\upmu\)L of 4-methyl-2-pentanol (0.05%) as an internal standard. Then, the closed vial was inserted in a water bath adjusted to 50 \({}^{\circ}\)C for 15 min, and the volatile compounds were extracted on a divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) fiber (1 cm, 30/50 \(\upmu\)m; Supelco Ltd., Bellefonte, PA, USA) during 30 min. Then, SPME fiber was desorbed at 240 \({}^{\circ}\)C in splitless mode for 10 min in the injector port of a GC interfaced with a mass detector using a Shimadzu GC-MS system (GCMS-QP2020, Shimadzu Group Company, Kyoto, Japan). Separation of the volatile compounds was carried out on a DB-5 MS capillary column (30 m \(\times\) 0.32 mm i.d., film thickness 0.25 \(\upmu\)m; Agilent Technologies, Santa Clara, CA, USA). The initial oven temperature was set at 40 \({}^{\circ}\)C for 5 min, then increased to 180 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C/min, to 240 \({}^{\circ}\)C at a rate of 30 \({}^{\circ}\)C/min, and held there for 5 min. The linear velocity of helium as carrier gas was 36 cm/s. The mass spectrometer was operated in the electron impact mode (EI) at 70 eV, scanning the range of 40-350 \(m/z\) at a scan rate of 2 scans/s. The temperature of the ion source and interface was set at 230 \({}^{\circ}\)C and 240 \({}^{\circ}\)C, respectively. Identification of the peaks was performed by comparing their mass spectra data with those from mass spectra databases (NIST 98 and Willey 7), using the accompanied Shimadzu Lab Solutions GC-MS software (version 4.52; Shimadzu, Kyoto, Japan). For the determination of the retention indices (RI), a C7-C22 n-alkanes series was injected under the same chromatographic conditions, and the values were compared, when available, with those reported in the literature for similar chromatographic columns. The relative content of each compound was calculated as a percent of the total chromatographic area, and the results were expressed as the means of three replicates.
## 4 Experimental Results
### Evaluation of the Scaling
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{4}{c}{**Average Chamfer Distance**} \\ \cline{2-5}
**Experiment** & \multirow{2}{*}{**Benchmark**} & \multicolumn{3}{c}{**Result**} \\ \cline{3-5}  & & **UniDepth** & **Depth Pro** & **MiDaS** \\ \hline Image-wise calibration & 2.01 m & **0.41 m** & 0.46 m & 0.65 m \\ First image calibration & 2.01 m & **0.42 m** & 0.55 m & 0.65 m \\ Average calibration & 1.94 m & **0.71 m** & 1.15 m & 0.89 m \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the experimental results on scaling. Figure 7: Average Chamfer distances obtained through calibration across all scenes that satisfy the preliminary conditions. (**a**) Average Chamfer distances obtained through image-wise calibration across all scenes that satisfy the preliminary conditions. (**b**) Average Chamfer distances obtained by calibrating on the first recording of a scene and applying this calibration to the entire scene. The results, presented in Figure 9, indicate that when using UniDepth (Best) as the backbone, the average Chamfer distance improved significantly across all scenes except one (2 %). The scene 7_approach_underground_station_7.1 exhibited a slight deterioration of 0.13 m. Despite this, the reconstruction in that scene remained meaningful, and both Depth Pro and MiDaS backbones achieved improvements in this scene. Using Depth Pro, six scenes (13 %) showed no improvement, while MiDaS failed to improve five scenes (11 %). These results demonstrate that even in this challenging case, UniDepth exhibited only minimal degradation (2 %, 0.13 m), while achieving a substantial overall improvement in reconstruction accuracy, from an average Chamfer distance of 1.94 m without calibration to 0.71 m with calibration. Furthermore, as illustrated in Figure 7(a),b, all scenes that failed to improve in this experiment using Depth Pro or MiDaS backbones had previously shown  
Figure 8: Visual Results: The upper row shows unscaled 3D point clouds with benchmark Chamfer distances, while the lower row shows the respective scaled point clouds using the Depth Pro backbone, with significantly reduced Chamfer distances. As shown, scaling varies throughout the scenes, making average calibration challenging. However, image- or scene-wise calibration allows the system to adapt effectively to these variations in scaling. Notably, in all cases, scaling potentially ensures staying within the legally required 1 m clearance zone. Figure 9: Average Chamfer distances obtained by applying average calibration from scenes meeting the preliminaries to the entire dataset. improvements under both image-wise and initial calibration methods. This suggests that regular updates to the calibration during operation will consistently yield improvements, even if calibration is not performed for every single recording. Averaged results are presented in Table 1.
# Battery Management System Hardware Concepts: An Overview
## 2 Battery Management System Requirements
### Electromagnetic Interference  
EMI can be a problem, if it is not given appropriate care. In general, all sensors are vulnerable to EMI. As a worst case, sensor acquisition could be distorted only slightly so that the BMS master is unable to identify false measurements. The other extreme condition would also result in complete uselessness of data points; however, at least it would be detectable. To reduce the influence, electric machines, power electronic components and other loads have to be designed well regarding EMI. This results in advisable application of appropriate EMI filtering devices, such as common mode chokes and blocking capacitors. These filter devices should be installed as close to the measurement paths of the sensors as possible to catch system disturbances that are e.g., defined in ISO7637-1, [13].
## 3 Proposed ATMALS-GSK Algorithm
### Atmals-Gsk
#### 3.2.2 Adaptive \(K_{r}\)  
The knowledge ratio (\(K_{r}\)) is a key parameter in the GSK algorithm that determines the proportion of individuals participating in the senior (exploitative) versus junior (explorative) phases. Its value typically lies within the range (0, 1), and it's often set around 0.9 in the original GSK. When \(K_{r}\) is low (e.g., 0.3-0.6), more individuals are assigned to the junior phase, leading to broader exploration. This supports preserving variety within the population and is particularly useful for complex or multimodal problems. However, it may also slow down convergence. As \(K_{r}\) increases (e.g., 0.7-0.9), the algorithm shifts focus toward the senior phase, improving exploitation and accelerating convergence. This is effective for problems where refining known good solutions is more valuable than broad search. With very high values of \(K_{r}\) (close to 1), nearly the entire population engages in the senior scheme. Although this enhances exploitation and can lead to quicker convergence in simpler or well-known problems, it significantly limits diversity and raises the chances of converging too early to suboptimal solutions.  
Finding the right balance for the knowledge ratio \(K_{r}\) is essential, as it directly influences how much of an individual's knowledge is retained versus how much is gained from others during the learning process. To address the limitations of setting a fixed value for the knowledge ratio, the ATMALS-GSK algorithm adaptively adjusts \(K_{r}\) during the optimization run, allowing it to dynamically respond to the algorithm's progress and the problem landscape. This adaptive adjustment is driven by historical improvement trends mirroring the mechanisms used for tuning \(K\) in Equations (3)-(7), which ensures that \(K_{r}\) evolves based on the success rate of previous generations. By allowing \(K_{r}\) to adaptively vary within [0.85, 1] with a discretized level of 0.01, the ATMALS-GSK algorithm intelligently balances knowledge retention and acquisition, improving both convergence speed and solution quality across diverse optimization problems.
## 2 Materials and Methods
### Well Logging and Production Data Preprocessing  
#### 2.1.1 Data Collection and Processing  
Two data types need to be collected: the well-logging data based on the depth range of perforated wells that have undergone hydraulic fracturing, and the gas production during well testing on the first day. Since the sample size provided by the perforated segment is too small to carry out reliable and stable production prediction, the gas production during well testing is divided into \"production segmentation\" for each logging sample. When analyzing LAS files containing well-log data, the initial step involves removing all \(-999\) marked as invalid values. Additionally, essential adjustments are made to the porosity, permeability, and water saturation data obtained in the field. Specifically, negative porosity and permeability values are adjusted to 0, and water saturation values exceeding 100% are corrected to 100%. Similarly, all records with gas production less than 0 are processed as 0.
## 4 EAT Segmentation Methods via Pericardium Delineation
### Traditional Image Processing Methods  
Methods in this category rely on various heuristics and thresholding to identify a volume of interest (VoI), i.e., the parts of the CT scan that are likely to contain epicardial fat. Discarding known irrelevant parts of the image not only speeds up the segmentation process but also lowers segmentation errors since other parts of the image can't interfere with the segmentation process. Once the VoI is determined, active contours are used to delineate the pericardium line. EAT is then segmented and quantified by thresholding out adipose tissue inside the contour.  
Figure 8: A diagram of the U-Net CNN architecture [35]. The left side of the diagram shows the contracting path, while the right side of the diagram shows the expanding path. The blue arrows represent crop and concatenation operations, while the gray arrows represent convolutional and pooling operations. In the final prediction, the red area is EAT while the green area is paracardial adipose tissue.  
An example of such an approach is [PERSON] [41], one of the first papers to describe a semi-automatic method for EAT quantification. Their method first discards the lungs, and then an expert observer manually selects control points on the pericardium on each relevant slice, which is then used to define the Vol. The next step of their method is to threshold the Vol to the Hounsfield scale interval corresponding to adipose tissue. The exact values of the threshold can be influenced manually by the observer. The thresholded image is sparse and has oscillations in its contours. To further refine the shape, the authors use geodesic active contours, an active contours variant to find a smooth contour of the sparse thresholded shape on each slice of the CT image.  
This approach can also be fully automatic as exemplified by [PERSON] et al. [42]. A summary of their method is shown in Figure 9. While [PERSON] [41] initializes the pericardium contour manually using an expert observer, [PERSON] et al. [42] initializes the contour automatically using an atlas-based method for pericardium detection. This initialized contour is then refined using geodesic active contours to more accurately detect the pericardium. The authors report a Dice coefficient between 0.88 and 0.95 on 15 patients and a time of 60 s to perform a segmentation of a scan. This method is further validated in [PERSON] et al. [24] on 50 patients, with a reported Dice coefficient of 0.92.
# Emotion AI-Driven Sentiment Analysis: A Survey, Future Research Directions, and Open Issues
## 4 Results and Discussions
### Using Machine-Learning-Based Sentiment Analysis  
In the machine learning approaches, the multinomial NB algorithm, RF algorithm, SVM, and XG Boosting algorithm were tested. The SVM resulted in high accuracy of 96%, recall of 66%, and F-measure of 60%. Moreover, the machine-learning-based SVM approach achieved 96% accuracy for the considered data. These results may vary when the dataset varies. The results are shown in Figure 11.  
The above discussion provided quantitative results of various approaches in Sentiment Analysis. Recently, Twitter data analysis has been used the most to predict sentiment. The overall merits and demerits identified from this work is presented in Table 8.  
Figure 11: Evaluation metrics of machine-learning-based sentiment analysis.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Types** & **Approaches** & **Merits** & **Demerits** \\ \hline \multirow{4}{*}{Lexicon based} & Dictionary-based and rule-based approach & There is more extensive-term analysis and, hence, accuracy reaches a high. & This is strictly limited to a smaller number of words in the lexicon trained; hence, it is utilized in the fixed score of opinion. \\ \cline{2-4}  & Bayesian networks & & This is strictly limited to a smaller number of words in the lexicon trained; hence, it is utilized in the fixed score of opinion. \\ \cline{2-4}  & Support vector machines & It is efficient in training the model for a particular purpose. & It is only applicable when the data are labeled. This is more costly. \\ \cline{2-4}  & Decision tree Neural networks & & \\ \hline \multirow{2}{*}{Hybrid} & Lexicon + machine learning & It is done at the sentence level, so it is easy at the document level. & It is complex and too noisy. \\ \cline{2-4}  & & & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Merits and demerits for lexicon, machine learning and hybrid approaches.  
Figure 10: Evaluation metrics on lexicon-based sentiment analysis.
# MicroBERT: Distilling MoE-Based Knowledge from BERT into a Lighter Model
## 4 Experiments and Results
### Model Accuracy Comparison Experiment
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**System** & **Params** & **FLOPs** & **Speedup** & **SST-2** & **MNLI-m** & **MRPC** & **QQP** & **RTE** & **Avg** \\ \hline BERT(Google) & 109 M & 22.5 B & 1.0\(\times\) & 93.5 & 84.6 & 88.9 & 71.2 & 66.4 & 80.92 \\ BERT(Teacher) & 109 M & 22.5 B & 1.0\(\times\) & 91.1 & **84.4** & 86.0 & **88.8** & 64.2 & **82.9** \\ BERT-PKD\({}_{4}\) & 52.2 M & 7.6 B & 3.0\(\times\) & 89.4 & 79.9 & 82.6 & 70.2 & 62.3 & 76.88 \\ DistillBERT\({}_{4}\) & 52.2 M & 7.6 B & 3.0\(\times\) & **91.4** & 78.9 & 82.4 & 68.5 & 54.1 & 75.06 \\ MobileBERT\({}_{4}\) & 15.1 M & 3.1 B & - & 91.2 & 81.5 & 87.9 & 68.9 & **65.1** & 78.92 \\ TinyBERT\({}_{4}\) & 14.5 M & 1.2 B & 9.4\(\times\) & 87.6 & 80.5 & 82.3 & 87.7 & 61.7 & 79.96 \\ MiniLMV2\({}_{4}\) & 5.4 M & 4.0 B & 5.3\(\times\) & 88.4 & 74.3 & 81.8 & 82.0 & 60.6 & 78.0 \\ \hline MicroBERT & 14.5 M & 1.2 B & 9.3\(\times\) & 89.6 & 80.3 & **88.7** & 86.6 & 62.8 & 81.6 \\ \hline Avg & & & & 89.8 & 79.9 & 84.5 & 78.9 & 61.9 & 79.2 \\ \hline \hline \end{tabular}  
* On the GLUE benchmark test set, the models are assessed. For each model group, the top outcomes are bold. Using a single NVIDIA 3090 Ti GPU, inference speedup is evaluated.  
\end{table} 
Table 1: Model effect comparison table. In summary, based on the multi-task multi-model comparison experiments, we find that our ultra-lightweight model still maintains an extremely impressive accuracy rate in each task, and does not sacrifice too much in accuracy while improving the lightweight, and still remains competitive among the accuracy rates of the four lightweight models.
# Low Power Wide Area Network, Cognitive Radio and the Internet of Things: Potentials for Integration
## 7 Challenges/Future Directions of CR-LPWAN
### Upper Layer Challenges
#### 7.2.2 Networking Challenges  
Similar to most networks, CR-LPWAN networks are plagued by challenges at the networking level. Owing to the large internetwork within the IoT space, quite a number of networking protocols exist to ensure seamless interaction and transfer of data. Most IoT applications interact with sensors and actuators in order to monitor and conduct different tasks. Consequently, careful CR-LPWAN network designs are required to ensure efficiency and scalability of such networks. In this regard, a few challenges at the networking level are discussed as follows:  
1. **Small packet sizes:** The requirement for low power consumption rates along with long transmission ranges may limit the data frame size to be deployed in CR-LPWAN networks. For example, the data frame size for Sigfox technologies is a total of 26 bytes [151], whereas a total payload of 222 bytes (for data rates 4-7) is provided by LoRa for use in Europe and 242 bytes for use in the US [152]. This is quite small to satisfy the demands of all IoT use-cases. Furthermore, there are different standards for frame sizes across different proprietary technologies, which may render the networking process of CR-LPWAN devices a burdensome task. Improvement in the standardization process is highly encouraged.
2. **Routing**: Many CR-LPWAN networks consist of large numbers of end nodes; thus, effective routing schemes are essential. In this regard, light weight routing protocols are required in CR-LPWANs since CR functionalities often incur additional computational overheads. The IETF is strongly involved in addressing routing issues in IoT with several new protocols being deployed, for example, the CoAP. They have also developed the RPL (IPv6 Routing Protocol for Low-Power and Lossy Networks), which tackles problems associated with mesh networking in IoT [153]. With the burgeoning of CR-LPWAN, these standards may require further extensions in order to address the challenge of increased routing computational demands, which may arise from spectrum mobility across different white spaces.
# Representing and Inferring Massive Network Traffic Condition: A Case Study in Nashville, Tennessee
###### Abstract  
Intelligent transportation systems (ITSs) usually require monitoring of massive road networks and gathering traffic data at a high spatial and temporal resolution. This leads to the accumulation of substantial data volumes, necessitating the development of more concise data representations. Approaches like principal component analysis (PCA), which operate within subspaces, can construct precise low-dimensional models. However, interpreting these models can be challenging, primarily because the principal components often encompass a multitude of links within the traffic network. To overcome this issue, this study presents a novel approach for representing and indexing network traffic conditions through weighted CUR matrix decomposition integrated with clustering analysis. The proposed approach selects a subset group of detectors from the original network to represent and index traffic condition through a matrix decomposition method, allowing for more efficient management and analysis. The proposed method is evaluated using traffic detector data from the city of Nashville, TN. The results demonstrate that the approach is effective in representing and indexing network traffic conditions, with high accuracy and efficiency. Overall, this study contributes to the field of network traffic monitoring by proposing a novel approach for representing massive traffic networks and exploring the effects of incorporating clustering into CUR decomposition. The proposed approach can help traffic analysts and practitioners to more efficiently manage and analyze traffic conditions, ultimately leading to more effective transportation systems.  
network traffic monitoring; intelligent transportation system; CUR matrix decomposition; clustering analysis; big data +
Footnote †: journal: _Algorithms_  
**Algorithms**
## 1 Introduction  
Data hiding is a family of techniques aimed at embedding information into digital objects for various application contexts. Digital watermarking, for copyright protection or authentication, and steganography, for covert communication or information storage, are typical applications of data embedding and hiding (see [1, 2, 3, 4]).  
Many algorithms have been devised for watermarking in different kinds of objects like images [5], audio [6], video [7], neural networks [8] and text data [9]. These algorithms can be classified according to various properties and characteristics; one of the most important properties is reversibility, i.e., the possibility to obtain the original object after the extraction of the embedded data.  
On the other hand, printable string encoding of binary data is a well-known and widely used technique to cope with systems designed to manage only bytes representing printable characters: in other terms, printable string encoding is an encapsulation method to process, in a transparent manner, any possible bit string by systems able to run only printable strings (for example, some mail servers).  
Even though printable string encodings and watermarking are widely used techniques in several applications, to the best of our knowledge there are no attempts to combine these techniques for data hiding. The main questions we try to answer are:  
1. Can we hide information in string encodings?
2. How much information can we embed in such string encodings?
3. Can we make it reversible?  
In this paper, we present a framework defining data and procedures for embedding and retrieving a bit string, which will be called a watermark, from a printable character string built using a binary-to-higher-base representation, like Base45 [10] and/orBase85 [11; 12]. The embedding-retrieving process is reversible. Indeed, the original printable character string is restored after the watermark extraction.  
Notation  
In this subsection the notation and terminology used throughout the paper is presented. Small italic letters denote scalar values, e.g., \(a\), \(b\), \(p\).  
Boldface italic capital letters denote sets, e.g., \(\mathbf{T}\), \(\mathbf{C}\). The cardinality of a set \(\mathbf{T}\) is denoted as \(\text{card}(\mathbf{T})\).  
An alphabet is denoted with Greek capital letters (e.g., \(\mathbf{\Psi}\)).  
The terms _symbol_ and _character_ are applied interchangeably trying to use the most meaningful in the sentence context.  
A word composed from symbols of an alphabet \(\mathbf{\Psi}\) is called a _sequence_. A sequence of \(n\) symbols from the alphabet \(\mathbf{\Psi}=\{0,1\}\) is called binary string.  
Capital letters (e.g., B, S, W) denote sequences of symbols from an alphabet.  
Greek letters represent functions, e.g., \(\beta\), \(\varphi\).  
Specific characters or strings (sequences) of characters are enclosed inside single quotes, e.g., 'D' or'string\(<\)My Header\(>\)!'.  
The floor operation is denoted as \(r\), with \(r\in\mathbb{R}\), and returns the largest integer not greater than \(r\).