esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
### Basic ADL Recognition  
It has been shown that a fine-tuned traditional CNN effectively excels in comparison to SVM, Multilayer Perceptrons, LSTM, and Bidirectional LSTM (BiLSTM) networks according to [PERSON] et al. [21]. This method outperformed other machine learning techniques, such as DL, RF, and others. Unfortunately, there are certain drawbacks since these methods can only extract basic features. DL architecture by [PERSON] et al. [22] could extract features from inadequately labeled datasets. A distance-based reward rule mechanism was used to label the data in the architecture, which was implemented on top of an HAR structure. The freshly labeled data were combined with the previously labeled data and processed toward an LSTM component to extract the features. In this technique, labeling anonymous and inadequately labeled data was the primary goal, rather than the efficiency of categorization. Consequently, the labeling process needed a considerable amount of unlabeled data, which raised the computing overheads.  
[PERSON] et al. [23] developed an Attention-Based BiLSTM (ABiLSTM) structure that weights features according to their significance in the present recognition situation. When comparing the findings to other recent methodologies (including both shallow and DL systems), it was found that this method offered higher classification performance. To evaluate a signal-based system, we used publicly available pre-processed data for all of our experiments. No real-time data were collected for these assessments, which is crucial. The Deep LSTM (DLSTM) design for feature identification and filtering was presented by [PERSON] et al. [24]. Sensors on smartphones were used to train the model using labeled and unlabeled data. Between the input and output gates, DLSTM had numerous LSTM layers. The raw data were augmented to produce the information measure, and Gaussian noise was removed from the finalized input to remove abnormalities. With the DLSTM, only the high-level characteristics remained after the low-level ones were removed. Following a set of guidelines, the unlabeled data that had been lost were computed and labeled. In a user-controlled context, the assessments of the proposed DLSTM revealed better outcomes than cutting-edge semi-supervised learning frameworks.  
To construct the InnoHAR model, [PERSON] et al. [25] combined a regular RNN with an Inception Neural Network (INN) structure oriented for HAR using wearable sensors. The INN structure comprises many deep layers, each of which is formed of several convolution layers parallel to pooling layers, constituting the inception layer. The INN structure was evaluated on various publicly accessible datasets and outperformed Deep-Convolutional-LSTM models. The disadvantage of this architecture was the insufficient setup of INN, which demanded a considerable amount of processing, and slight adjustments might necessitate repeating the expensive retraining process.
## 5 Experiment Validation
### Tracking Results  
Three experiments with different rotational speeds were conducted. The spin rates are 3.8 r/s, 6.4 r/s, and 7.5 r/s respectively. The collected IF signals were processed by two different modules, including the receiver module and the roll angle detection module. The collected signals were processed by an SDR receiver. The carrier tracking loop of the receiver is a second-order FLL-assisted third-order PLL, and the code phase errors were detected by a non-coherent discriminator.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Group** & **Nominal Rotational Speed/(r/s)** & **Real Rotational Speed /(r/s)** & **Standard Deviation/(\({}^{\circ}\))** \\ \hline
1 & 5 & 3.8 & 2.5 \\
2 & 8 & 6.4 & 3.7 \\
3 & 10 & 7.4 & 4.2 \\ \hline \multicolumn{4}{c}{Average deviation} & 3.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Standard deviations of roll angle measurements.  
Figure 20: Experiments on a single-axis rotary table.  
The processing results of the carrier and code tracking loops, including carrier NCOs, code NCOs, one-millisecond accumulation values, and the carrier-to-noise ratios (C/N\({}_{0}\)) are shown in Figure 21. As we can see from Figure 21, the carrier-to-noise ratios of the satellite signals are about 40 dB\(\cdot\)Hz, which can be considered as strong signals. The carrier and code tracking loops can also work properly during antenna rotation and obtain the one-millisecond accumulation values constantly.  
The one-millisecond accumulation values were then used to construct the roll modulation signals with Equation (16), and they were processed by the roll angle detection module to get roll angle. Figure 22 consists of three groups of panels and shows the tracking results of the roll angle detection module. In each panel group, the top left panel depicts the tracking process of FLL-assisted PLL. The top right panel in each group shows the tracking results of the frequency of the input signal. The phase and frequency discriminators outputs are shown at the bottom of each group. In our experiments, the spin rate was started from 0 to a designated rate and then held. To ensure the tracking accuracy and stability of loops, the signals processed in the roll angle detection module was selected with a constant spin rate.  
Figure 21: Carrier and code tracking results of different spin rates of 3.8 r/s (top left group), 6.4 r/s (top right group), and 7.5 r/s (bottom group).  
As shown in Figure 22, the tracking loops are fully capable of tracking the roll modulation signals, despite the roll modulation signals appearing to be a little different from the ideal sine waves. As we analyzed before, the roll modulation signals are directly related to the gain of the antenna. The gain pattern of the antenna we used in the experiments is not exactly the same as the sine wave, so the roll modulation signals seem to be different. To improve the measurement accuracy and tracking stability, the gain pattern of the antenna used to detect roll angle should be designed to match the sine function as much as possible.
# Cellular Self-Digestion and Persistence in Bacteria
## 4 Intracellular Degradation Mechanisms
### Lipid Degradation  
During the transition to stationary phase, the fatty acid degradation regulon is over-expressed and provides a carbon source to starved cells via the digestion of membrane components [220]. FadR is a global regulator of lipid and fatty acid metabolism and acts as a switch between fatty acid \(\beta\)-oxidation and fatty acid biosynthesis [221]. FadRrepresses several genes and inhibits transcription of the _fad_ genes [221; 222], and its activity is modulated by the long-chain acyl-CoA thioester small effector molecule, which binds directly to FadR [221]. This protein complex (FadR-acyl-CoA thioester) cannot bind to the operator sequence in the promoter of the fatty acid degradative genes, leading to _fad_ gene activation [221; 223].  
In response to carbon starvation, derepression of the FadR regulon results in digestion of membrane lipids, yielding fatty acids that are utilized by acyl-CoA synthetase to generate acyl-CoA [224]. The \(\beta\)-oxidation enzymes encoded by _fadBA_, _fadE_, _fadFG_, and _fadH_ then convert acyl-CoA into acetyl-CoA, which is an important source of energy during starvation [224]. Consistent with these observations, it has been shown that a strain lacking acetyl-CoA dehydrogenase (encoded by _fadF_) barely survives carbon starvation [224]. Of note, in _E. coli_, lipid degradation occurs on the outer and inner membrane, as there are no intracellular forms of lipid storage in this bacterium [67].  
Notably, cells can also activate emergency derepression pathways independently of FadR in stationary phase to survive carbon starvation [225]. Thus, in stationary phase, the _fad_ genes become active via activity of ppGpp-programmed RNA polymerase together with cyclic adenosine monophosphate (cAMP)-cAMP repressor protein (Crp) complex [225]. However, it has been proposed that under these conditions, medium- or short-chain acyl-CoA is the substrate for \(\beta\)-oxidation, not long-chain acyl-CoA [67; 225]. These medium and short chains of acyl-CoA also bind FadR and prevent binding to the operator sequence of the _fad_ genes, leaving them active during the self-digestion process [225]. Critically, although lipid metabolism and fatty acid oxidation are known to be a critical survival mechanism for cancer persisters by providing energy molecules [28; 226; 227], this remains largely unexplored for prokaryotic persisters.
# Advanced Techniques for Geospatial Referencing in Online Media Repositories
## 3 Methodology
### Analyzing Audio and Subtitles  
The audio component of videos is a rich source of data for analysis. These data are converted into text using OpenAI's Whisper, a proven, reliable tool in our transcription process [55]. Whisper accurately captures spoken content, and we additionally embed timestamps to produce transcripts in WebVTT format, which allows for a direct comparison with existing subtitles, ensuring the accuracy and integrity of the transcription and the reuse of our NER method.  
We employ the Levenshtein ratio metric, a key component in our evaluation process, to assess the transcription quality by comparing the Whisper-generated entries against the original subtitles [71]. This method enables a differentiated assessment of the accuracy of the transcription against the spoken word. The subtitles are a valuable benchmark in this process due to their alignment with the audio track. After transcription, the text and available subtitles are analyzed using NER and geocoding as previously described. The complete process is shown in Figure 4.  
Figure 4: Workflow diagram illustrating the analysis of the audio source and subtitles to retrieve coordinates.  
Figure 3: Workflow diagram illustrating the extraction of text from the visible image and performing NER and geocoding to retrieve coordinates.
# Frequency Comb-Based WDM Transmission Systems Enabling Joint Signal Processing
## 1 Introduction  
The advent of the erbium-doped fiber amplifier (EDFA) in the late 1980s [1; 2] transformed fiber optic communications in two profound ways. The first was that it enabled radically increased link distances without resorting to electrical regeneration. The second and equally profound impact was that it facilitated wavelength-division multiplexing (WDM), i.e., the transmission of data on many parallel laser carriers of different wavelengths. The first demonstrations of amplified WDM data transmission came in 1990 [3; 4] and were followed by massive commercial installations of WDM systems during the second half of the 1990s [5]. These developments paved the way for affordable worldwide telecommunications, and more specifically, the Internet, as we know it today. Current optical WDM systems and state-of-the-art experiments demonstrating transmission records may use hundreds of wavelengths in a single fiber core [6], requiring large stacks of lasers in the transmitters. In addition, the increased popularity of coherent intradyne links [7; 8] with receivers needing local oscillator lasers and digital signal processing (DSP) have highlighted the need for coherent sources also in the receiver.  
The question that arises is if a frequency comb, comprising a large number of phase-locked spectral lines, could be a viable alternative as WDM sources and replace discrete laser stacks. The answer is yes, although some care needs to be taken to ensure source figure of merits such as the line width and signal-to-noise ratio (SNR) are met. In this paper we will review the work on using optical frequency combs for WDM transmission and put particular emphasis on discussing the unique benefits that are enabled by the use of frequency combs in contrast to discrete lasers.  
This introduction will continue with a history of optical frequency combs and their use in WDM systems. Then, we will briefly outline the unique possibilities that combs enable in optical communications, as well as provide an overview for the remaining sections of this article.
## 4 Results and Discussion  
In this section, we provide the extraction of each LiDAR point feature and its usage in land/water discrimination with the evaluation of the results. As aforementioned, the point features were extracted from the neighboring points within a 2D searching radius of 1 m, and threshold values were applied to each feature to discriminate water from land. The results of the land/water discrimination using elevation-, intensity-, and geometry-based features are presented. Also, the results of using the seeded region growing algorithm and the delineation of land/water boundaries are presented at the end of this section.  
The LAS data files were converted into ASCII files using lastools (_lastools_; rapidlasso GmbH; Gilching, Germany) so that they could be processed. The LiDAR point features extraction, the seeded region algorithm, and point labeling were implemented using Matrix Laboratory (MATLAB) (_MATLAB2016a_; MathWorks; Natick, MA, USA). The threshold values, which were identified using Jenks break optimization method, were selected using the embedded function in ArcGIS (_ArcGIS_; Esri; Redlands, CA, USA). The accuracy assessment and the LiDAR data visualization were both conducted using ArcGIS as well.
## 3 Methodology and Overall Framework
### Predictive Maintenance on a Conveyor Belt
#### 3.3.3 Anomaly Detection with Kernel PCA  
Kernel PCA has previously been proposed as a novelty detection algorithm [35]. The novelty detection is carried out by transforming the data from the original feature space to the infinite-dimensional space and vice versa, after which the reconstruction error generated is considered as a novelty measure. Using the same approach, we train a model, transform the input data to infinite-dimensional space and inverse transform the data back to the original space. The reconstruction error is then calculated. Additionally, the influences of the hyperparameter \(\gamma\) in RBF kernel and the hyperparameter \(\alpha\) of the ridge regression that learns the inverse transform on the performance of the algorithm are also tested and compared. The parameter \(\alpha\) stands for regularization strength in ridge regression; the larger \(\alpha\) is, the less variance the estimates have. In other words, decreasing the value of \(\alpha\) will lead to higher model complexity whereas \(\gamma\) serves the same purpose as explained in OCSVM in Section 3.3.1.  
Figure 4: The detection models with final decision boundaries.  
Using the same set shown in Figure 3, we run a grid search and calculate the overall error rates to select the best parameter values as \(\alpha=0.005\) and \(\gamma=15\). The corresponding trained model is demonstrated in Figure 4c. The grid search algorithm favors bigger \(\gamma\) and smaller \(\alpha\) to obtain a better performance around the decision boundary. A side effect of this parameter combination is that some jagged edges already appear on the decision boundary.  
In summary, the parameter effects and the approaches mentioned above are taken into account to redesign all three algorithms for our PM application. Our goal is then to pick the best performing algorithm with respect to the training speed, prediction time and the prediction accuracy. The experiment results, as discussed in Section 4.4, show that OCSVM outperforms the others in our case; hence, it is selected as our final PM algorithm.
## 4 Experiment and Results
### Training  
We detail the CCPTXL model structure and parameters in Table 1. The vocabulary size of our corpus is 10,091. The dimension of word embedding vectors was set to 410. We used 41-dimensional vectors Q, K, and V in the attention layer. We had 16 identical transformer-blocks and employed 10 heads of attention, as shown in Figure 2. The total number of trainable parameters was 46M. In order to better converge to the global optimum, we trained our model using Adam optimization and dynamic learning rate policy. The initial learning rate was 0.0001, which then decayed using the cosine schedule. To prevent the model from overfitting, the dropout mechanism was used in the Multi-head layer and Feed Forward layer, and the dropout rate was set to 0.1.
## 3 Results  
As is shown in Figures 1 and 2, the reference frame \(X\), \(Y\), \(Z\) is connected with the network, assuming the \(Z\)-axis passes through point 1 perpendicularly to the plane with points 1, 2, and 4. The axis \(Y\) is parallel to the section 11-21, and the third orthogonal axis \(X\) is parallel to 11-41. Hence, the three connecting reference sections with network points 1, 2 and 4 obtained the following coordinates: 1 (0, 0, _h_), 2 (0, 11-21, _h_), 4 (11-41, _h_). The sections 11-21  and 11-41  represent measured distances between points 1 and 2 as well as 1 and 4, respectively. The value \(h\) is the approximate height of the object. The approximate coordinates \(X\), \(Z\), \(Y\) of all remaining points 3, 5,..., 12 are computed by solving the network consisting of 40 measured distances between all points. To evaluate the proposed approach, we consider an example 3D network presented in Figure 3, which exemplifies a standard spatial network used in land surveying. The observational linear equation of the measured spatial distances between any two spatial \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Description** \\ \hline Type & Digital AF/AE SLR camera with built-in flash \\ Recording media & SD memory card, SDHC memory card \\ Image type & JPEG, RAW (14-bit, Canon original) \\ Image sensor type & CMOS sensor \\ Image sensor size & 22.3 \(\times\) 14.9 mm \\ Resolution & Effective pixels: approx. 15.10 MPx \\ Total pixels: approx. 15.50 MPx \\ Color filter system & RGB primary color filter \\ Low-pass filter & Fixed position in front of the image sensor \\ Focus modes & Servo mode; Auto-focus; Manual focus \\ Full Auto, Portrait, Landscape, & Close-up, Sports, Night Portrait, Flash Off, \\ Exposure control & Creative Auto, Program), shutter-priority AE, \\  & aperture-priority AE, auto depth-of-field AE, manual \\  & exposure, E-TTL II autoflash, movie shooting \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Key technical parameters of the camera Canon EOS 500D. Figure 2: The right stereo picture with 3D precise linear network on the object (the points 1,..., 12, and A, B, C, D represent characteristic vertices of the object; \(X\), \(Y\), \(Z\) define a local 3D coordinate system; \(x\), \(y\) represent pixel image coordinates). network points P (\(X_{P}\), \(Y_{P}\), \(Z_{P}\)) and Q (\(X_{Q}\), \(Y_{Q}\), \(Z_{Q}\)) is based on the well-known formula used in three-dimensional surveying [24]:  
\[ \begin{array}{ll}v=&-\frac{X_{Q}-X_{P}}{s_{0}}dX_{P}-\frac{Y_{Q}-Y_{P}}{s_{0} }dY_{P}-\frac{Z_{Q}-Z_{P}}{s_{0}}dZ_{P}\\ &+\frac{X_{Q}-X_{P}}{s_{0}}dX_{Q}+\frac{Y_{Q}-Y_{P}}{s_{0}}dY_{Q}+\frac{Z_{Q}- Z_{P}}{s_{0}}dZ_{Q}-(s-s_{0}),\sigma_{s}\end{array}
## 3 SAgic-IoT Platform Description
### Layered IoT Architecture
#### 3.2.5 Security Layer  
Security in the sensor nodes' communication is essential within the SAgric-IoT system. The nodes provide mechanisms for devices' access control to the network, encryption (using symmetric key cryptography), and integrity to verify that the received frames were not altered, following the security schemes defined in the 802.15.4 standard. The data are encrypted before being sent (128-bit AES encryption) and then decrypted at the receiver for use. The following parameters were configured in each Xbee node: first, E.E. = 1: AES encryption enable, and the parameter KY: AES encryption key with a string of 32 hexadecimal characters. Second, the camera nodes' security with WPA2. The service provider controls the security of the processing and storage layers. Finally, the end-user accesses the system using a specific username and password and the secure HTTP protocol for connection to the system.
## 4 Obstacle Avoidance Planning  
### Artificial Potential Field Function  
The artificial potential field function is a mathematical function expression of the virtual artificial potential field. The artificial potential field function includes the attraction potential field function, the repulsion potential field function, and the resultant potential field function. The negative gradients of the three potential field functions can generate attraction, repulsive force, and resultant force respectively, and make the manipulator moves under the resultant force finally.
## 5 Simulation Study
### Simulation Parameters  
In our experimental environment, we define the event head of each event is composed of the following contents: \(\{A_{1}=x_{I}\), \(A_{2}=x_{2}\}\), where \(A_{1}\), \(A_{2}\) are attribute names and \(x_{1}\), \(x_{2}\) are double-type values randomly chosen from the range of (0, 10). Each subscriber has defined a subscription with the form of \"\(A_{1}<x_{I}\)\(\Lambda\)\(A_{2}<x_{2}\)\", where \(x1\), \(x2\) are also values randomly chosen from the range of (0, 10). In particular, we use the same data delivery ratio calculation method as in [20] to analyze the data delivery ratio in our simulation.  
Moreover, we assume the data generation of each sensor follows a _poisson_ process with an average arrival interval of 100 s. The network bandwidth is 10 kbps. The length of the whole test period is 2 hours, while over 200 seconds before the end of simulation, no event is generated. Other simulation parameters and their default values are summarized in Table 1. The performance metrics we use in our simulations are: data delivery ratio, data delivery delay, data delivery overhead and network lifetime. All the simulation results are averaged over 1,000 independent runs.
## 4 Simulation
### Coordinated Controller
#### 4.3.2 Simulation of Sinusoidal Steering Maneuver  
The sinusoidal steering wheel angle input is shown in Figure 10a. The other simulation parameters are the same as those in Section 4.3.1. The simulation results are shown in Figure 10.  
For the yaw's stability performance in Figure 10b, the yaw rate is much larger than the ideal value through the whole process when no control is taken. This is quite dangerous, given that the vehicle might be out of control. The tracking accuracy is good under an independent control, while the coordinated control has an even better tracking accuracy which is nearly the same as the ideal yaw rate. In Figure 10c, the sideslip angle is large when no control is taken. However, when controls are taken, the sideslip angle is much smaller. As before, the coordinated control has a slightly smaller sideslip angle.  
For the roll stability performance in Figure 10d, the roll angle under no control is apparently too large compared with taking control, which may lead to a dangerous rollover. Once the control is taken, the roll angle is effectively reduced. The roll angle of the coordinated control is slightly smaller.  
For the driving trajectory in Figure 10e, the real trajectory under no control is far from the ideal one, which is so dangerous that it needs to be avoided. Meanwhile, both an independent and coordinated control can track the expected trajectory. Figure 10f shows the required wheel torque of each wheel.  
To sum up, when the coordinated controller is introduced. The active suspension can play a role in the control of the yaw's stability. The active suspension and DYC can work in coordination. The yaw rate tracking is great, and the sideslip angle and vehicle roll angle are small. The yaw and roll stability are further improved compared with simply using two independent controllers.  
Figure 9: Stability performance under ramp steering. (**a**) steering wheel angle; (**b**) yaw rate; (**c**) sideslip angle; (**d**) row angle; (**e**) vehicle trajectory; and (**f**) wheel torque.