esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion and Future Work  
Common practice does not yet incorporate large metadata models that include all of the data related to a building. Therefore, it is of high importance to examine the effort required to produce a metadata model. The initialization and set up of the Brick model used within this effort took approximately 20 h. The addition of the information related to the lighting system took 30 h, resulting in a workload of only 50 h for a model of the lighting subsystem. This data was acquired from technical documents, mainly stored in PDF format. Expanding the model with all subsystems is expected to take no longer than 150 h, including performing some basic plausibility checks. Despite the requirement of 150 h to produce the full metadata model, we believe that it is an improvement in comparison to the manual instantiation of PTs and the manual search for faults in a building. We expect that the general trend of buildings will expand to a situation where metadata models are handed over with new buildings. In this case, the production of the metadata model would fall into the hands of a qualified person within the contractor company and the metadata model would be under development in parallel with the building, reducing these initial effort hours to a minimum. In the case of the Brick schema, however, it is possible to derive a partially complete Brick model based on IFC building information models, which are far more commonly available [17]. The maintenance of the model is a matter of continuous attention.  
Furthermore, the current procedures are semi-automated and still require human help to reason out the true causes of failed PTs. We expect that these procedures would be a part of a more complex system which would intelligently discover when to apply which, and also how best to utilize their results.  
Future work heavily relies on expanding the test suite with additional PTs. The growth of the set of implemented PTs also implies the expansion of the metadata model. Creating procedures that use the results from PTs which are not only lighting-related will expand the FDD reasoning to the interactions with the rest of the subsystems.
## 2 Results
### Comparison of Identified Venom-Derived Polypeptides
#### 2.2.3 Yellow Royal Jelly Protein Domain  
This protein was identified from the SDS-PAGE gel band 5 (with peptide fragments covering 8% of the full sequence) and from the in-solution proteomic analysis protein 10 (with peptide fragments covering 56% and 70% of the full sequence). It was identified as the major royal jelly protein domain [MRJP domain], a 215-aa venom polypeptide corresponding to the transcript contig98_B2-2 (Figure 4 and Figure S3). This transcript (1614 bp) encodes a 430-aa long MRJP precursor, which has a signal peptide (16 aa) and a mature venom protein (414 aa) (Figure S3), with a theoretical molecular mass of 47616.06 Da and an apparent electrophoretic mobility of 45 kDa on SDS-PAGE (Figure 1, gel band 5). A list of proteins from this family in the Hymenoptera database can be found under the entry \"major royal jelly protein family\". The transcribed yellow protein-like protein of _D. quadriceps_ (Figure 4) shares 27% similarity with the _A. mellifera_ MRJP-1 protein and 40% identity with the _D. melanogaster_ yellow protein. The honey bee-like glycoprotein MRJP-1 is a 55 kDa monomer, but forms oligomers of 420 kDa [29]. The MRJP-1 from honey bee has three pairs of cysteine bonds, whereas the MRJP-like protein from _D. quadriceps_ venom contains only five residues of cysteine, possibly forming only two disulfide bonds.  
Figure 2: Alignment of the _D. quadriceps_ dipeptidyl peptidase-4 (vDPP-4) encoded in the transcript Contig164_B2-2 and sequences of homologous proteins from common wasp and honey bee. The _D. quadriceps_ vDPP-4 was aligned with its homologues from common wasp _V. vulgaris_ (VDDP4_VESVU), honey bee _A. mellifera_ (VDPP4_APIME) and a predicted sequence from the genome of _D. quadriceps_ (XP_014479089.1). Asparagine residues (N), marked in emerald green, indicate glycosylation sites. The light green tagged amino acids indicate residues located at the enzyme active site. The cysteine residues that are involved in the formation of disulfide bridges are indicated in purple and the connectivity pattern is indicated by solid black lines. The peptide signals are labeled in brown and were predicted using SignalP 5. The conservation is indicated by BLOSUM62 matrix using the Jaliview program.  
Figure 3: Alignment of _D quadriceps_ venom glucose dehydrogenase [FAD, quinone] with homologous proteins from hypmenopterans. This venom component identified by proteomic analysis of band 3 and 4, resolved in the SDS-PAGE, is encoded in the _D. quadriceps_ venom gland transcript contig75_B2-2 was aligned to the N-terminal (the first 612 residues) of the predicted sequence generated by automatic annotation of _D. quadriceps_ genome (XP_014475855), the Indian jumping ant _H. [PERSON]_ (XP_011140071) and the Glucose dehydrogenase [FAD, quinone] from fruit fly _D. pseudoolscura_ (DHGL_DROPS). The _Drosophila_ protein (DHGL_DROPS) comprises a peptide signal of 42 amino acids (experimental). The nucleotide binding site is indicated in red. The amino acids that are indicated in green are divergent or conflictive residues. Selenocysteine is indicated by “U”.
# An Incremental Local Outlier Detection Method in the Data Stream
###### Abstract  
Outlier detection has attracted a wide range of attention for its broad applications, such as fault diagnosis and intrusion detection, among which the outlier analysis in data streams with high uncertainty and infinity is more challenging. Recent major work of outlier detection has focused on principle research of the local outlier factor, and there are few studies on incremental updating strategies, which are vital to outlier detection in data streams. In this paper, a novel incremental local outlier detection approach is introduced to dynamically evaluate the local outlier in the data stream. An extended local neighborhood consisting of k nearest neighbors, reverse nearest neighbors and shared nearest neighbors is estimated for each data. The theoretical evidence of algorithm complexity for the insertion of new data and deletion of old data in the composite neighborhood shows that the amount of affected data in the incremental calculation is finite. Finally, experiments performed on both synthetic and real datasets verify its scalability and outlier detection accuracy. All results show that the proposed approach has comparable performance with state-of-the-art k nearest neighbor-based methods.  
Keywords:outlier detection; local outlier factor; incremental algorithm; k nearest neighbor +
Footnote †: journal: Applied Sciences
## 2 Ultra-Low-Frequency Oscillation Suppression Based on Transient Energy Function
At the end of the first oscillation period, the transient energy change of the system is: \[ \begin{array}{l}\Delta V_{t_{k}}=V(t_{s+k})-V(t_{s})=\sum\limits_{i=1}^{n}[V_{ \text{PI}}(t_{s+k})+V_{\text{DI}}(t_{s+k})]-\\ \sum\limits_{i=1}^{n}[V_{\text{PI}}(t_{s+k})+V_{\text{DI}}(t_{s+k})]=\sum\limits _{i=1}^{n}\int_{\theta_{i}^{\prime}}^{\theta_{i}(t)}\left(P_{mi}-P_{ei}-\right. \\ \left.\frac{T_{ij}}{T_{II}}p_{COI}\right)-D_{j}(\widetilde{\omega}_{j}+\omega_{ COI})+\frac{T_{ij}}{T_{II}}\sum\limits_{j=1}^{n}D_{j}(\widetilde{\omega}_{j}+ \omega_{COI})\text{d}\theta_{i}\end{array} .\] (5)  
In order to realize the suppression effect of DC modulation on oscillation, when \(\sigma_{k}>0\), the first oscillation period is divided into four stages. Stage [\(t_{s}\),\(t_{1}\)), \(\widetilde{\omega}(t)\) decreases from 0 to \(\widetilde{\omega}_{\text{min}}\); stage [\(t_{1}\),\(t_{2}\)), \(\widetilde{\omega}(t)\) increases from \(\widetilde{\omega}_{\text{min}}\) to 0; stage [\(t_{2}\),\(t_{3}\)), \(\widetilde{\omega}(t)\) increases from 0 to \(\widetilde{\omega}_{\text{max}}\); stage [\(t_{3}\),\(t_{s+k}\)], decreases from \(\widetilde{\omega}_{\text{max}}\) to 0. It is assumed that \(l\) DC in the system can control the ultra-low-frequency oscillation mode, and when \(p\) (\(0<p\leq 1\)) DC participates in DC modulation, the system oscillation can be suppressed. The maximum percentage of each DC that can participate in power modulation is \(\varepsilon\), where the maximum controllable DC power of article \(\tau\) is \(\Delta P_{dct}=\varepsilon P_{dct^{*}}\), and where \(P_{dct^{*}}\) is the rated active power of article t. Within [\(t_{s}\),\(t_{2}\)), the DC of the system can reduce the transmission power so that the transient energy change of the system can be reduced. In [\(t_{2}\),\(t_{s+k}\)], the DC of the system can reduce the transient energy change of the system by increasing the transmission power. Therefore, the system transient energy reduction in the first oscillation period due to DC modulation is as follows:  
\[\Delta V_{\text{dct}_{k}}=\sum\limits_{\tau=1}^{p}(\int_{\theta_{i}(t_{k})}^{ \theta_{i}(t_{2})}\Delta P_{\text{dct^{*}}}\text{d}\theta_{i}+\int_{\theta_{i} (t_{2})}^{\theta_{i}(t_{s+k})}\Delta P_{\text{dct^{*}}}\text{d}\theta_{i}). \tag{6}\]  
When \(\sigma_{k}\leq 0\), the system can still maintain stability after disturbance, and DC modulation can make the system oscillation decay as soon as possible and shorten the time for the system to recover stability. When \(\sigma_{k}>0\), based on the principle of transient energy decline, in the first oscillation period, the energy change \(\Delta V_{\text{dct}_{k}}\) of the system \(p\) direct current used to suppress the oscillation must be at least greater than the change of the system transient energy--that is, \(\Delta V_{\text{dct}_{k}}>\Delta V_{t_{k}}\).
## 3 Results
### Comparison Between Homogenized and 3D-Printed Samples  
Figure 8 shows the thermal conductivity as a function of the apparent density of all the samples studied. Relative to the case of the plaster without PCM, the measurements show that the apparent density was clearly influenced by the printing process. A density of 900 kg.m\({}^{-3}\) was recorded for the reference printed plaster, and 860 kg.m\({}^{-3}\) for the P3D/PCM. The incorporation of the phase change material reduced the apparent density by 4.5% compared to the plaster without PCM. The values for the 3D-printed samples decreased by 29% compared to the reference homogenized plaster. The use of such powder bed techniques increases the porosity of the samples, as already observed by [24].  
It can be seen that the conventionally mixed reference plaster has a higher thermal conductivity than the printed plaster. A difference of 0.1 W\(\cdot\)m\({}^{-1}\cdot\)K\({}^{-1}\) is observed between the two plaster samples without PCM, a reduction of 19%, which is related to the air content of the samples. Concerning the P/PCM composites, a decrease of 0.18 W\(\cdot\)m\({}^{-1}\cdot\)K\({}^{-1}\) is noted for the case of the P3D/PCM compared to the P/PCM composite. Thermal insulation was improved by the use of 3D printing to form the samples and by the incorporation of the PCM, which induces a significant reduction in thermal conductivity. Thus, a 33% reduction in thermal conductivity for the case of the composite P3D/PCM, in comparison with P3D, was measured.  
Figure 6: Samples: (**a**) printed plaster (P3D), (**b**) printed plaster with a layer of PCM in the middle of the sample (P3D/PCM) and (**c**) homogenized plaster without PCM.  
Figure 7: Distribution of the PCM layer within the printed sample, seen at two different scales.  
This study suggests that the difference in thermal conductivity values depends on the method used to create the materials: additive manufacturing is the one that significantly reduces the thermal conductivity of the tested samples. On the other hand, the method of localizing the PCM to within the middle of the sample highlights the possibility of minimizing the damage to the microcapsules due to the absence of a mixing step.  
This study reveals that the additive manufacturing process not only lowers thermal conductivity but also achieves substantial reductions in density, especially for the P3D/PCM composite. These improvements can be attributed to the increased porosity and the localized incorporation of PCM, which synergistically enhance the material's thermal insulation and efficiency. This approach underscores the potential of 3D printing to simultaneously optimize the structural and thermal properties of construction materials.
# Error Detection for Arabic Text Using Neural Sequence Labeling
## 5 Results
### Testing on QALB  
In order to further validate our model, we used a portion of the Qatar Arabic Language Bank (QALB) corpus [51] for testing. We did not initially use it to train our models mainly because the type of annotation in QALB was targeted at error correction, which was not suitable for our task (error detection). Additionally, we wanted to support efforts produced by language experts, add to the existing number of Arabic corpora, and investigate sequence labeling for detecting errors in Arabic on a smaller scale and simpler model.  
QALB is, to the best of our knowledge, the only corpus for Arabic grammar checking. We took 50 MSA sentences from testing data of QALB 2014, and only used the first 10-17 words from each sentence to mimic the lengths of sentences in the training corpus and to speed up the annotation process. This resulted in having 516 words and 781 tokens for the QALB test corpus. Punctuation  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Hyperparameter/Model** & **SimpleRNN** & **LSTM** & **BiLSTM** \\ \hline Layers & 2 & 2 & 1 \\ Layer Size & 50 & 125 & 25 \\ Embedding Size & 50 & 32 & 100 \\ Batches & 1 & 1 & 32 \\ Dropout & 0.2 & 0.1 & none \\ Optimizer & Nadam & Nadam & Nadam \\ \hline \hline \end{tabular}
\end{table}
Table 6: Best Models’ Hyperparameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Figure 0.** & **Precision** & **Recall** & **F0.5** \\ \hline Baseline & 75.6 & 91.96 & 78.39 \\ SimpleRNN & 81.22 & 91.75 & 83.09 \\ LSTM & 81.42 & 94.57 & 83.71 \\ BiLSTM & 81.68 & 91.54 & 83.45 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of Best Configuration When Trained on Augmented Dataset.  
marks were eliminated, and punctuation edits were not taken into consideration. Furthermore, labeling was manually done, similar to our training corpus. In order to label this corpus, we depended on the original annotation in QALB to lead the labeling process. For example, an edited word in the QALB sentence was labeled as \"i\" in the token-level labeled test corpus.  
We ran QALB using the best configuration of each model (as in Table 6) in order to predict the labels of the words in our QALB test set. Table 8 shows the results of this experiment. While LSTM had the lowest performance in terms of precision and F\({}_{0.5}\) on our training set, it performed the best when tested on QALB. On the contrary, BiLSTM had the lowest performance on QALB over all measures. This might be a sign that BiLSTM was lacking regularization or further tuning.
## 2 Materials and Methods
### Case Study and Database
Savitzky-Golay filtering [11] was also applied in order to reduce extreme values in the distribution of data. This noise reduction technique was necessary in order to track nonlinear relationship between inputs and outputs without changing its tendency. In this paper, third degree polynomial and a window size of 15 were used when applying the Savitzky-Golay filtering technique. Finally, in order to deal with real values of measurement on different scales, all data were normalized into the [-1,1] range.
## 3 Results and Discussion
### Application Layer  
Table 3 indicates the general parameters used in the simulations, where for each grid size (number of NAN nodes), 21 different simulation runs were carried out. Each run was configured with different random seeds in order to obtain confidence intervals for the chosen performance measures: packet delivery ratio, throughput, network transit time, and routing table size. To ensure the network topology shown in Figure 6, we chose 80 meters as the grid distance between nodes. With this value and the propagation model parameter values, each node is only able to establish connections with the neighbors located on its sides.  
The applications that will be transmitted over the NAN network are detailed in Table 4, where they are grouped into four types of traffic according to the EDCA categories and the quality of service requirements. The table shows also the distributions and average values selected for the packet size and for the interarrival time, where two main distributions were considered. On the one hand, for periodical traffic types where applications generate packets of constant size at regular time intervals, the distributions were considered as deterministic. On the other hand, for traffic types based on events or by variable rate applications, an exponential distribution was selected, which can be better adjusted to the combination of this type of applications. To implement the latter distribution, the default application module of the ns-3 simulator was also modified. In addition, we considered it interesting to analyze the network performance in two different load conditions. Thus, two different sets of values were selected for the packet generation rate. Firstly, a relatively high Network Load (NL1) was considered and, secondly, a network load that caused an extreme congestion situation (NL2).
# Time Series Anomaly Detection Using Signal Processing and Deep Learning
## 3 Methodology
### Data Preparation  
Data preparation is crucial for adjusting the time series data to make them digestible by the model. The first step involves applying data scaling, such as standardization, to ensure that the data have a mean of zero and a variance of one, which helps improve model performance and convergence. Additionally, data transformation is performed so that the data dimensions align with the chosen neural network architecture. Time series data are then segmented using non-overlapping sliding windows to obtain a set of time series samples [50]. The sliding window size needs to be chosen carefully, depending on the specific problem and the temporal resolution required for analysis.  
The next key step is bandpass filtering. It is a signal processing technique that allows only a specific range of frequencies within a signal to pass through while removing components outside this range. As an additional preprocessing step in time series data analysis, it is particularly useful for improving signal quality by removing undesired noise or trends that may mask meaningful patterns. A bandpass filter is defined by two cutoff frequencies:  
* Low cutoff frequency--removes components with frequencies below it (e.g., trends or baseline drift).
* High cutoff frequency--removes components with frequencies above it (e.g., random noise).  
The signal components that fall within these two frequencies are preserved, allowing for the extraction of dominant periodicities or oscillations in the specified range. In practice, digital bandpass filters, such as Butterworth, Chebyshev, Elliptic, or finite impulse response (FIR) filters [51], are commonly employed to achieve smooth filtering with minimal distortion.  
In the bandpass filtering step of our data preparation process, we use a Butterworth filter [44]. Generally, prior spectral analysis (e.g., using Fourier Transform or Power Spectral Density) helps determine the dominant frequency components to preserve. However, in the scope of this paper, we decide the low and high cutoff frequencies based on the overall length of the signal (i.e., the sliding window length) to focus on the intermediate frequency band where the core signal resides. Given that the sliding window size is chosen appropriately according to the problem at hand, we can use proportional lower and higher cutoff frequencies, e.g., cutting 10% on each side of the frequency spectrum. Since bandpass filtering emphasizes repeating patterns in the data, it can help the Autoencoder models to concentrate on learning important patterns from the data and therefore improve  
Figure 1: Overview of the proposed methodology.  
the accuracy and reliability of downstream analyses, such as feature extraction, anomaly detection, or predictive modeling.
## 6 MIMO Antenna Performance Enhancement Techniques
### Mutual Coupling Reduction Using CDRA in Ground  
A CDRA-based antenna is presented in [81; 82]. In [82] the CDRA is used back to back on the opposite side of the substrate. For excitation of Port no.1 and Port no.2, a pair of coplanar waveguides is used with conformal microstrip lines. CDRA is placed on yop with a height of 6.5 mm and on the bottom with 6.0 mm. The excitation of Port no.3 and port no. 4 is conducted by microstrip line conformal fed to stripline. An improved interelement isolation is realized by generating two modes HEx and in each CDRA. Two different polarizations are produced due to the two different modes HEx and HEy. Excitation from opposite directions and two orthogonal modes in opposite directions make it feasible to excellently improve the isolation of closely colocated elements. DRA have shown better results in terms of bandwidth and gain [82]. In [30; 62], monopole antenna with inverted \"L\"-shaped geometry and its MIMO design with eight elements is demonstrated. Good  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Switching Types** & **Supremacy/** & **Weaknesses/** \\  & **Advantages** & **Disadvantages** \\ \hline \multirow{3}{*}{PIN Diode} & Extremely reliable & Power handling capability is high \\  & Very low in cost & The tuning speed is very high \\  & Most probable choice for reconfiguration & High DC biasing capability in ON state \\ \hline \multirow{3}{*}{MEMS} & Feasible for small flow of current continuous tuning & A nonlinear source \\  & Integration is easy & Lower range dynamically \\  & & Biasing circuitry is complex \\ \hline \multirow{3}{*}{Varacters} & Linear impedance bandwidth with high isolation & Voltage control level is high. \\  & Comparatively less noise figure and low power losses & The switching speed is slow \\ \cline{1-1}  & & Limited life cycle is limited compared \\ \cline{1-1}  & & with other components. \\ \hline \hline \end{tabular}
\end{table}
Table 4: A comparison of different switch components [83; 84; 85; 86; 87].  
efforts have been carried out in [29]. In [30] a MIMO antenna the size of 136 \(\times\) 68 mm\({}^{2}\) with eight elements covering 450 MHz bandwidth is presented.  
Isolation with value of 15 dB as well as high Gain of 4 dBi is achieved. In the structure of the antenna, a parasitic patch is extended from the ground plane. Excellent impedance matching in the desired frequency spectrum. MIMO antenna designing and mutual coupling reduction or decoupling techniques are shown in Figure 20.