esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Research on Diagnosability of Control Systems Based on Structural Analysis
## 3 Diagnosability Evaluation Based on Structural Analysis
### Quantitative Evaluation
#### 3.2.1 Residual Similarity Metrics
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Residual Measurement Methods** & **References** & **Application Scenario** & **Specificities** \\ \hline K-L scatter & [36] & Low-dimensional discrete control system & There is no diagnosability for some failure modes; quantitative evaluation of diagnosability is impossible when the system output dimension is one-dimensional; diagnosability metrics are asymmetric. \\ \hline JS scatter & [37] & Low-dimensional nonlinear control system & It can compensate for the shortcomings of K-L scattering, but the computational effort and complexity grow exponentially with the increased control system dimensions. \\ \hline Hausdorff distance & [38] & Low-dimensional discrete control systems & Vulnerability to the selection of equivalent space vectors and system modeling uncertainty. \\ \hline Bhattacharyya distance & [39] & Stochastic system & The effect of random noise is fully considered and does not depend on any particular fault diagnosis algorithm; however, the results of the diagnosability evaluation of open-loop and closed-loop control systems are inconsistent. \\ \hline Modified distance & [40] & Continuous system & Real-time fault diagnosis is possible; uncertainty faults can be handled effectively; the influence of noise on the results of diagnosability evaluation can be effectively overcome. \\ \hline Fisher information distance & [4] & Modeling accurate discrete systems & The designed diagnosability metrics have a precise geometric meaning, do not suffer from metric asymmetry, and are adapted to multi-failure scenarios. \\ \hline Energy distance & [41] & Low-dimensional discrete control system & Ability to overcome the effects of model uncertainty and process noise on diagnosability evaluation. \\ \hline Mahalanobis distance & [42] & The dynamic system without distribution knowledge & It does not require knowledge of the probability distribution of the noise. \\ \hline Bayesian error rate & [43] & Modeling accurate discrete linear control systems & More sensitive to external disturbances and noise. \\ \hline Statistical cumulative sum & [44] & Structured dynamic systems & Evaluation metrics are asymmetric and susceptible to system noise disturbances. \\ \hline Based on \(H_{\infty}\)-parameter & [45] & Model accurate dynamic systems & Only the ease of fault detection can be judged, and the evaluation metrics are susceptible to uncertain noise and disturbances. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Methods for quantifying diagnosability and characterization.
###### Abstract  
As an effective solution to the first- and last-mile logistics of door-to-door intermodal container transportation, inland container transportation involves transporting containers by truck between terminals, depots, and customers within a local area. This paper is the first to focus specifically on the inland container transportation problem with limited depot capacity, where the storage of empty containers is constrained by physical space limitations. To reflect a more realistic scenario, we also consider the initial stock levels of empty containers at the depot. The objective of this problem is to schedule trucks to fulfill inland container transportation orders such that the overall cost is minimum and the depot is neither out of stock or over stocked at any time. A novel graphical representation is introduced to model the constraints of empty containers and depot capacity in a linear form. This problem is then mathematically modeled as a mixed-integer linear programming formulation. To avoid discretizing the time horizon and effectively achieve the optimal solution, we design a tailored branch-and-price-and-cut algorithm where violated empty container constraints for critical times are dynamically integrated into the restricted master problem. The efficiency of the proposed algorithm is enhanced through the implementation of several techniques, such as a heuristic label-setting method, decremental state-space relaxation, and the utilization of high-quality upper bounds. Extensive computational studies are performed to assess the performance of the proposed algorithm and justify the introduction of enhancement strategies. Sensitivity analysis is additionally conducted to investigate the implications of significant influential factors, offering meaningful managerial guidance for decision-makers.  
inland container transportation; limited depot capacity; empty container; branch-and-price-and-cut algorithm +
Footnote †: journal: Applied Sciences  
1 School of Business Administration, Shanghai Lixin University of Accounting and Finance,  
Shanghai 201620, China; [EMAIL_ADDRESS]
## 2 VSC System Modeling and Quantitative Evaluation Method of System Stability
### Quantitative Evaluation Method of VSC System Stability
Figure 5: Nyquist curves of the eigenvalue of \(\mathbf{L}\)(s). (**a**) Integrated graph; (**b**) Partial enlargement of (**a**). Figure 6: Nyquist curves of \(\lambda_{2}\)(s). (**a**) Nyquist curve \(\lambda\)2(s) for different parameters; (**b**) Partial enlargement of (**a**). Here, taking the parameters of the current control as an example to analyze the relationship between the control parameters and \(\phi\). The analysis is presented in Figure 8. Figure 8a indicates that the proportional coefficient of current control significantly affects the system stability, the system stability increases as the proportional coefficient increases. To intuitively present the relationship between current loop parameters and the \(\phi\) diagram, Figure 8b,c give the partial enlargement of Figure 8a and the contour map of \(\phi\). The above descriptions indicate that the stability of VSC can be evaluated by the proposed quantitative evaluation method, and the stable level of VSC can be judged by the defined angle of the stability evaluation point. In the following, the proposed quantitative evaluation method is employed to optimize the current control and PLL to improve the stability of VSC.
# Long-Distance Measurements Using a Chromatic Confocal Sensor
## 6 Results  
In this section, we will show the effect of applying the different proposed methods to increase the sensitivity of the sensor.  
Each displacement was executed in increments of 20 \(\upmu\)m. At each position, three thousand spectra were captured, and the averages of each one hundred spectra were computed to enhance the signal-to-noise ratio (SNR), resulting in thirty spectra per displacement. In Figure 12, the standard deviation (STD) for each sample position, based on 30 captured spectra, is compared. The upper section of the figure illustrates that the Gaussian fit generally exhibits a higher STD value across most of the sample positions, with the exception of two. The lower section displays the result of subtracting the frequency filter curve from the Gaussian fit curve. This difference shows that, apart from two sample positions, the values are predominantly positive, indicating that the uncertainty associated with the Gaussian fit is, on average, greater than that of the frequency filter within the examined range. The \"Gaussian fit with 10 interpolated points\" are scattered points, since some standard deviation values had a value of zero as no peak movement was detected, which is the same effect that is illustrated in Figure 8. The mean standard deviation with the largest oversampling outperforms the one with fewer interpolated points by a factor of 3.8.  
To justify the number of interpolated points, in Section 4.3, the actual resolution of 250 \(\upmu\)m/nm was obtained experimentally, supposing a linear shift between the detected peaks. Thus, in order to be able to detect movements of 20 \(\upmu\)m, at least 10 more values need  
Figure 12: A standard deviation comparison between the low-pass filtering (with no interpolation and interpolation) and the Gaussian fit for each measurement.  
to be interpolated to allow the peak detection for such sample displacements. The higher value of 1000 interpolated points was motivated by the highest spectrometer's resolution that can be found commercially, which is in the order of picometers.  
Additionally, an interpolation between real spectrum values was performed; for Figure 13, the resolution was enhanced by a factor of ten, while for Figure 14, the resolution was increased by a factor of 1000, yielding a significantly higher resolution than the original results. Therefore, as previously stated, this interpolation step is crucial when the spectrometer's resolution is insufficient to achieve the desired axial resolution.
## 2 Materials and Methods
### Methods
#### 2.2.2 Evaluation Criteria  
The validation accuracy was represented by four common statistic measures: the determination coefficient (R\({}^{2}\)), root mean square error (RMSE), Bias, and relative root mean square error (rRMSE), in which the rRMSE was used to eliminate the influence of different sample size. They were calculated by the following equations.  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{n}(y_{i}-\hat{ y}_{i})^{2}} \tag{18a}\] \[RMSE=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}}\] (18b) \[Bias=\frac{1}{n}{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})}\] (18c) \[rRMSE=\frac{RMSE}{mean(\sum_{i=1}^{n}y_{i})} \tag{18d}\]  
where \(y_{i}\) is the \(i\)-th ground observation, \(\hat{y}_{i}\) is the corresponding estimation, and \(n\) is the number of samples.
# WLAN Aware Cognitive Medium Access Control Protocol for IoT Applications
## 4 System Model
### Interference Model  
The signal propagation considered in this paper is based on the free space path loss model. For correct reception of a packet by the WSN node, the received power should be higher than the given threshold, denoted as \(\lambda\), which is the Signal to Interference plus Noise Ratio (SINR) threshold. If a receiver IoT node comes within the transmission region of the WLAN transmitter, the packet of IoT node is lost. An interference radius \(R_{I}\) (area from which the interference exists from a WLAN transmission) for IoT node can be expressed as follows [39]:  
\[R_{I}=\sqrt[g]{\frac{(\lambda)(P_{WLAN})(P_{Lo})}{(P_{IoT})(P_{Lo})(r^{-\eta}) -(\lambda)(\sigma_{N}^{2})}}, \tag{6}\]  
where \(r\) is the distance between the transmitter and the receiver IoT sensor nodes, \(\eta\) is the path loss exponent, \(P_{IoT}\) is the IoT sensor node's transmit power, \(P_{WLAN}\) is the WLAN transmit power, \(P_{Lo}\) denotes the attenuation (at a distance of 1 m), and \(\sigma_{N}^{2}\) is the noise power. In this research, we used a simple noise model where noise power is given as \(N=kTB\). Here \(k\) is Boltzmann constant, \(T\) is temperature and \(B\) is bandwidth. We ignored antenna noise temperature and receiver noise figure to simplify the noise model.
# Spatial Distribution and Monitoring of Land Subsidence Using Sentinel-1 SAR Data in Java, Indonesia
## 3 Results
### Spatial Distribution and Land Subsidence Rate
#### 3.1.8 Greater Semarang  
LS also occurs in Kendal, Semarang, and Demak--collectively called Greater Semarang--located in Central Java Province (approximately 7.0052\({}^{\circ}\) S, 110.4381\({}^{\circ}\) E). These areas are located within an alluvial plain area. Semarang City, the capital of Central Java, is considered the fifth largest metropolitan area in Indonesia, after Jakarta, Surabaya, Medan, and Bandung. Positioned on the northern coast of Java Island, Semarang serves as a key economic and cultural hub in the region, with an estimated population of around 1.65 million in 2020 [16]. Its strategic location and vibrant economy make it prominent for trade and commerce in Central Java.  
The observation results of LS in the city of Semarang are consistent with previous studies conducted by [2; 36; 49; 50]. LS in Kendal, Semarang, and Demak is relatively significant, as indicated by the black boxes in Figure 10a (Figure 10(a.1-a.3)), with LS rates up to 80 mm/year, 160 mm/year, and 100 mm/year, respectively, as shown in Figure 10(c.1-c.3). Figure 10d shows the land use and field conditions in each area. In Kendal (Figure 10(d.1)), subsidence is detected in industrial areas near the port; in Semarang, due to high rates of LS, houses are lower than the roads (Figure 10(d.2)); and in Demak, LS occurs in residential and agriculture areas (Figure 10(d.3)).  
The impact of LS in Semarang has caused substantial damage in numerous aspects. One clear impact is the seawater inundation that has worsened over time. Figure 10b shows the coastline shift based on LANDSAT images from 1998 and 2024, showing a movement of more than 5 km inland. Other studies also indicate that inundations expanded by 650 m between 2006 and 2016 [51].  
Figure 9: LS in Pekalongan: (**a**) LS rate in mm/year in the Pekalongan area; (**b.1**) LS time series retrieved from InSAR from 2017 to 2023; and (**b.2**) land use of the region highlighted in the black box (**b**).
## 5 Data Acquisition via V2V Communications  
After content distribution, when a vehicular node passes a roadside unit carrying the contents which are requested, it receives these contents from the roadside unit. However, when several vehicular nodes communicate with the same roadside unit simultaneously (which frequently happens in medium-high density vehicular networks), some may not obtain its required data due to bandwidth competition. Since the control center only distributes no more than one copy for each request at roadside units, the failed content delivery may have no chance to continue in the whole storage cycle. Therefore, how to help these vehicular nodes to acquire data becomes a challenging task.  
Considering that a vehicular node could receive content from others via V2V communications, we regard vehicular nodes as the supplementary storage devices so as to increase the access ratio [31]. The supplementary storage starts in V2U communications when some vehicles request to and receive extra contents from roadside units. We call these vehicles the assistant vehicles and the extra contents as the assistant contents. After that, when the assistant vehicles encounter the vehicular nodes requesting the assistant contents, they reply to the requests through V2V communications.
# Solar Irradiance Forecasting with Transformer Model
## 2 Dataset and Methods
### Positional Encoding
#### 2.7.2 Spatial Information  
Like the sequence of timesteps, for the identification of the location of the measured region, the Transformer model also needs additional information expressing the unique 1D code of the given location. These identification codes are also expressed by a vector that is added to the input features. The network trains these identifiers by itself; thus, it can mark sites with similar meanings with a similar vector and, conversely, mark sites with no direct correlation with significantly different vector values. From Figure 9, it is possible to see the related regions, which, in most cases, overlap on the map of India. From this, it can be concluded that the trained Transformer model \"understood\" the spatial similarities and grouped them together according to their importance in predicting future solar irradiance. For a better explanation, Figure 10 shows similarity between the 0 th patch and other patches on the world map. Here, it can be seen that the strongest similarity is in the neighboring regions, which have a reddish color corresponding to positive similarity.  
Figure 8: Cosine similarity of temporal position embedding. (**a**) The left side shows the position embedding for the encoder part. (**b**) The right side shows the position embedding for the decoder part.  
Figure 10: Cosine similarity between 0 th patch and other patches on the world map from Google Earth.  
Figure 9: Cosine similarity of spatial position embedding. (**a**) The left side shows the position embedding for the encoder block. (**b**) The right side shows position embedding for the decoder block.
# MEMS and IoT in HAR: Effective Monitoring for the Health of Older People
## 4 Materials and Methods
### Predictive Models
#### 4.3.1 Artificial Neural Network  
Artificial neural networks (ANNs) are deep learning models inspired by the workings of the human brain. These models, organized into layers (inputs, hidden, and outputs), can learn relationships between input and output variables to make predictions. Each node is connected to others via weights and thresholds, and the learning process takes place through training with large amounts of data. Common types of neural networks include feed-forward networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs).
## 4 Bayesian Learning-Based Clustered-Sparse Channel Estimation
### Complexity and Performance Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**LS** & **OMP** & **SOMP** & **TMSBL** & **PB-CSBL** \\ \hline \(\mathcal{O}(LK_{p}M)\) & \(\mathcal{O}(L^{2}K_{p}M)\) & \(\mathcal{O}(L^{2}K_{p}M)\) & \(\mathcal{O}(LK_{p}^{2}\overline{N})\) & \(\mathcal{O}(LK_{p}^{2}M^{3}\overline{N})\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The comparison of time complexity. As shown in the case of the high temporal correlation (i.e., \(\beta=1\)), the anti-noise performance of channel estimation can be improved. As for the low temporal correlation among CIRs (i.e., \(\beta=0\)), the anti-noise performance is not significantly improved. However, with more reference information, the better hyperparameter vector \(\gamma\) can be obtained. Therefore, when multiple OFDM blocks are utilized simultaneously, higher channel estimation accuracy can be achieved.
## 2 Materials and Methods
### Batch Test  
A batch test was performed in a 125 mL vial (working volume 100 mL) and 3 g/L biomass from the parent reactor was inoculated. The test was conducted in three phases to evaluate and understand the effects of cations on the salt stress of the anammox granules in a saline environment. First, the activity of the anammox granules were analyzed according to the salt concentrations (1, 3, 5, 10, 15, and 20 g NaCl/L) to determine the salinity stress level for the anammox granules (phase 1). At the salt concentrations corresponding to the IC\({}_{50}\), the activity of the anammox granules according to Ca\({}^{2+}\) (30, 70, 100, 200, and 300 mg/L) and Mg\({}^{2+}\) (20, 50, 70, and 100 mg/L) was evaluated (phase 2). Then, the effects of cations on the morphology, extracellular polymeric substances (EPS) content and composition, and microbial community structure of the anammox granules were investigated at the optimum cation concentration (C3 for 200 mg Ca\({}^{2+}\)/L and 5 g NaCl/L and C4 for 70 mg Mg\({}^{2+}\)/L and 5 g NaCl/L) (phase 3). C1 without salt and C2 with only salt (5 g NaCl/L) were used as controls.  
The batch test was performed for 1 h in phase 1 and for 6 h in phases 2 and 3. All experiments were performed in a constant temperature room maintained at 35 \(\pm\) 1 \({}^{\circ}\)C. The composition of the medium was as follows: 100 mg/L NH\({}_{4}\)+-N, 100 mg/L NO\({}_{2}\)\({}^{-}\)-N, 1.010 g/L NaHCO\({}_{3}\), 0.055 g/L KH\({}_{2}\)PO\({}_{4}\), 0.005 g/L CaCl\({}_{2}\)\(\cdot\)5H\({}_{2}\)O, 0.5 g/L MgSO\({}_{4}\)\(\cdot\)7H\({}_{2}\)O, 0.010 g/L FeSO\({}_{4}\)\(\cdot\)7H\({}_{2}\)O, and 0.005 g/L EDTA.
# A Filter-Based and Parallel Unknown Tag Identification Protocol in Open RFID Systems
## 4 Filter-Based and Parallel Unknown Tag Identification Protocol
### Collection Phase
#### 4.3.1 Building Real Frame  
The real frame is constructed based on the responses of all unknown tags. In particular, the reader broadcasts a request with the parameter \(\left\langle f,r_{3}\right\rangle\) to issue the frame, where \(f\) is the size of the real frame and \(r_{3}\) is a random seed. In this frame, each active unknown tag \(t_{u_{i}}\) randomly picks a slot by the hash function \(H(id_{u_{i}},r_{3})\)_mod_\(f\) and sends a 1-bit response to the reader in this slot. The reader listens to the channel and records the status of each slot, i.e., empty and collision are recorded as \"0\" and singleton is recorded as \"1\". Then, the reader obtains the real frame that reflects the actual responses from the unknown tags, and the frame is denoted by \(F_{R}\). \(F_{R}\) is also referred to as the indicator vector, and its length is \(f\).  
\[F_{R}[i]=\begin{cases}0,&\quad\text{the ith slot is an empty/collision slot},\\ 1,&\quad\text{the ith slot is a singleton slot}.\end{cases}\]  
For example, as shown in Figure 4, there are 10 unknown tags \(\{t_{u_{1}},t_{u_{2}},\dots,tu_{10}\}\) and \(f=10\). The tags \(\{t_{u_{1}},t_{u_{3}},t_{u_{4}},t_{u_{6}},t_{u_{8}}\}\) are mapped to the 1 st, 3 nd, 6 th, 5 th, and 10 th singleton slots, respectively, and other tags are mapped to collision slots. Therefore, the real frame (also called the indicator vector) is denoted as \"1010110001\".
# Improving Performance of Key-Value Stores for High-Performance Storage Devices
## 3 Background and Motivation  
### High-Performance Storage Devices  
With the advancement of flash memory, flash-based SSDs and NVMe SSDs are widely used and are rapidly replacing traditional tape-based storage and magnetic hard disk drives (HDDs). SSDs offer significantly faster throughput and lower latency compared to HDDs and have no moving parts, which enhances their durability and endurance.  
A key difference that must be considered in the software stack is that SSDs are typically equipped with multiple channels. In contrast to HDDs, where multiple requests from the host are serialized into a single queue and handled once at a time, SSDs can handle multiple requests simultaneously. This capability arises from the architecture of SSDs, which connect multiple chips (groups of flash cells) to multiple channels. Although each channel can accept only one request at a time, the presence of multiple channels allows for simultaneous access to different chips.  
This parallelism in SSDs enables the host to issue requests in a parallel and concurrent manner, rather than a serialized manner. However, existing applications and I/O stacks are optimized for HDDs, and thus, are designed to issue requests in a serialized manner. This approach only utilizes a single channel, despite the storage device's ability to handle requests across multiple channels. Therefore, a new approach is required to fully exploit the potential of multiple channels in storage devices by employing multiple threads to issue I/O requests in a parallel and concurrent manner.