esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Setup
The RF signal obtained in the receiving antenna was transformed to an optical signal in order to be transmitted on an optical fiber. The signal traveled through the optical fiber along the tunnel back towards the VNA, which is then converted back to RF before connecting to the VNA. Since the measurements were performed along the tunnel, the separation between the antennas increased with every measurement. With a regular coax cable, every movement causes a change in the phase, and by using an optical fiber this phenomenon is avoided. The additional delay caused by the RF optic device, and the time it took the signal to travel back along the optical fiber, were taken into account and did not affect the measurement results. The RF Optic device as shown in Figure 5 supports up to 6 GHz with a nominal link gain of 7 dB. The optical signal is transmitted with a 1310 nm wavelength [27]. The device has the ability to increase the signal transmitted via the optical fiber with an LNA. In the experiments, the LNA was not used, since this brought the signal to saturation. \begin{table}
\begin{tabular}{l c} \hline Frequency range & 1–6 GHz \\ Number of samples & 11130 \\ Transmission power & 20 dBm \\ Receiver IF bandwidth & 1 kHz \\ Frequency resolution & 0.45 MHz \\ Time resolution & 0.2 ns \\ Maximum excess delay & 2.2 μsec \\ \hline \end{tabular}
\end{table} 
Table 6: Measurement parameters. Figure 4: Autonomous measuring setup. The experiments were performed in a pedestrian tunnel under a highway. The tunnel is long and straight. The walls are mostly smooth, but there was a certain roughness in between the smooth plates of the concrete. The dimensions of the underpass are: \(Height=2b=1\) m, \(Width=2a=1.85\) m and \(Length=75\) m, as shown in Figure 6a. The measurements were carried out along the tunnel, while the transmitting and receiving antennas were placed in the middle of the cross-sectional area of the tunnel. The tunnel was empty throughout the whole measurement process, and therefore there was no interference between the transmitter and receiver. The transmitter antenna was connected to an antenna stand. The receiving antenna was attached to a pole which was part of the wooden wagon. To give the laser a surface to reflect off of, cardboard was attached to the front of the cart. All components of the experiment were unable to wobble, besides the optical fiber which was loose, enabling it to extend along the tunnel. The experiment setup at the experiment site can be seen in Figure 6b. Figure 5: RF Optic [27]. Figure 6: Experiment setup at the experiment site. (**a**) Illustration of the tunnel dimensions. (**b**) A photograph taken at the experiment site.
# A Roadside and Cloud-Based Vehicular Communications Framework for the Provision of C-ITS Services
## 3 Roadside and Cloud Architecture
### Cloud Architecture
#### 3.2.2 Central C-ITS Platform  
All messages from the ITS-G5 environment, including periodic messages such as CAMs and event-driven messages such as DENMs and IVIMs, along with the current status of each RSU in the field, can be viewed on a dashboard provided by the road manager's main tool for overseeing and monitoring traffic, the central C-ITS platform (MOBICS). Figure 5 presents snapshots of the MOBICS dashboard. Through its graphical user interface, MOBICS also enables the user-friendly production of traffic events. This makes it easy to monitor all traffic activity and disseminate C-ITS warnings as necessary.  
The central C-ITS platform functions as a traditional traffic management system, but with V2X features for to communicate with road devices (RSUs and OBUs) and advanced road sensors, such as traffic radars and cameras. A traffic management operator can use it to filter received messages and monitor the condition of RSUs, while also setting rules for events to be generated automatically or manually.  
MOBICS adopts an open architecture that makes possible cooperation with other central systems (with the aim of eliminating human intervention) or with an access point (e.g., NAP) for the exchange of information, e.g., regarding traffic events. Currently, the platform is able to interpret CAM, DENM, and IVIM messages received or sent to the cloud MQTT broker, but it is planned to extend it include more ETSI ITS message types, such as VRU Awareness Message (VAM), Maneuver Coordination Message (MCM), Map (lane topology) Extended Message (MAPEM), and SPATEM. In the future, ASN.1 encoded ITS messages in binary format will also be supported, not only XML as at present. In terms of hardware, both the central platform and the cloud broker can be run on the same machine or on their own separate dedicated servers.
## 3 New Generation Sensor Web Enablement
### Evolvement of SWE Information Model
#### 3.2.3 Description of Sensor Metadata  
For the description of sensor metadata, the SWE framework defines the Sensor Model Language (SensorML). SensorML 1.0 [55] specifies a model and XML encoding for the description of all kinds of sensor related _processes_. A process can be for example a measurement procedure conducted by a sensor or the post processing of previously gathered data. In SensorML a sensor is defined as a process which is capable of observing a phenomenon and returning an observed value. It allows a detailed description of a process including a listing of its inputs, outputs, parameters, and process methods. Further metadata of a process can be defined including its identification and classification, as well as characteristics such as the temporal availability or its spatial description. SWE services use SensorML as a format for describing their associated sensors.  
Thereby, the design of SensorML 1.0 focused primarily on the following functionalities:  
* Supporting the discovery of sensors by providing a means for encoding sensor metadata.
* Providing information that can be used for understanding and analyzing data produced by the sensor (e.g., the parameters of the sensor calibration).
* Allowing the description of post processing steps that were performed on sensor data so that it can be reconstructed how a data set has been created.  
The work on SensorML 2.0 is currently still in progress and addition of further functionalities is planned. First, a property inheritance mechanism for SensorML shall be included. This mechanism aims at reducing the size and redundancy of sensor metadata descriptions by constructing inheritance hierarchies. Second, SensorML shall be extended to enable the precise and well-defined description of a sensor's protocol and interface. The vision behind such a detailed description of the sensor protocol is to enable an on-the-fly integration of the sensor with the Sensor Web, by using the protocol definition to transform sensor messages to Sensor Web protocols. The description of the sensor protocol, once designed for a particular sensor type, can then be reused in different scenarios and can be shared among user communities, which facilitates the usage of SWE in general. An extension of SensorML, which allows such a declarative description of the sensor protocol, has been proposed by the Sensor Interface Descriptor (SID) concept [62,63] which may influence the development of SensorML 2.0.  
Since SensorML is very generic, potential use cases cover a broad range. However, this fact makes it also necessary to define profiles for SensorML in order to ensure that every SensorML based sensor description contains all metadata for the particular use case. An example for such a profile is the SensorML profile for discovery of sensors (Section 3.3.5).
## 4 Performance Evaluation
### Simulation Background  
To ensure the reliability of the experiment, the selection of experimental data is based on the principle of authority and diversity. The KDD-CUP99 dataset was selected for both the test set and training set. There are about 4.9 million records in this dataset, and all attack types are divided into 39 attack types in four major categories. Considering that the algorithm in this paper carries out abnormal detection of node behavior, the test data in the experiment contain attack types that only appear in the test set, but these attack types do not appear in the training set. The training set contains one normal identification type and 22 attack types. The dataset also provides a 10% size subset for training and testing. The data library of simulation experiment in this paper comes from these two subsets. Their sample distribution is shown in Table 2.
# Simple HPLC-PDA Analysis to Determine Illegal Synthetic Dyes in Herbal Medicines
## 3 Results and Discussion
### Accuracy and Precision  
The accuracy of the HPLC-PDA method was evaluated as recoveries of the analytes after spiking the mixed color standard solution covering three different concentrations of herbal medicine blank samples. Furthermore, precision was demonstrated based on the intra- and inter-day relative standard deviations values. The recoveries of ten synthetic colorants obtained from each herbal medicine are summarized in Table 3. The intra-day recoveries of ten synthetic colorants ranged from 94.3-132.1% for Hawthorn fruits, 78.3-111.2% for Cornus fruits and 74.6-115.0% for Schisandra fruits (Table 3). On the other hand, inter-day recoveries were found in the range of 86.0-105.2% for Safflower, 94.0-116.0% for Hawthorn fruits, 78.4-112.2% for Cornus fruits, and 74.9-115.0% for Schisandra fruits (Table S1 in Supplementary Materials). The acceptable limits of AOAC were 80-115% for 10 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/g of the analysis concentration in dietary supplements and botanicals [23]. When recoveries obtained from samples spiked with low, medium, and high concentrations were calculated as average values, most samples had satisfactory recoveries by indicating the acceptable range of AOAC guidelines, even if a few samples showed either a slightly lower or higher recoveries than the acceptable criteria.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Colorants**} & \multirow{2}{*}{\begin{tabular}{c} **Wavelength** \\ **(nm)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Retention** \\ **Time (min)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Equation of** \\ **Calibration Plot** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Linearity** \\ **(\(\mathbf{z^{2}}\))** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Hawthorn Fruit (\(\mathrm{\SIUnitSymbolMicro\text{g}}\)/g)** \\ **(\(\mathbf{z^{2}}\))** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Cornus Fruit(\(\mathrm{\SIUnitSymbolMicro\text{g}}\)/g)** \\ **(\(\mathbf{z^{2}}\))** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Cornus Fruit(\mathrm{\SIUnitSymbolMicro\text{g}}\)/g** \\ **(\(\mathbf{z^{2}}\))** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c}  
The values of intra-day relative standard deviation (RSD, repeatability) and inter-day RSD (reproducibility, RSDr) are represented in Table 3 and Table S1, respectively. The ranges of RSD and RSDr indicated 0.2\(\sim\)6.9% for Hawthorn fruit, 0.1\(\sim\)3.4% for Cornus fruit, and 0.2\(\sim\)2.5% for Schisandra fruit. The AOAC guideline requires an acceptable repeatability value of 6% and reproducibility of 11% at 10 \(\mathrm{\SIUnitSymbolMicro g}\) of the analysis concentration in validated dietary supplements and botanicals. The proposed analysis method was found to accomplish satisfactory precision. Therefore, the proposed HPLC-PDA analysis appears to be a useful procedure that can quantify ten synthetic dyes regardless of the type of herbal medicine.
# An Integrated Dictionary-Learning Entropy-Based Medical Image Fusion Framework
## 3 Proposed Framework
### Low-Frequency Components Fusion
#### 3.3.2 Solution Discussion  
High-frequency components correspond to edges in an image. High-frequency components change rapidly, so that an edge is characterized by a sharp change in intensity values. Low-frequency components in an image correspond to smooth regions that represent global information about the shape, such as general orientation and proportions. Low-frequency components change gradually and slowly. The slow change feature would be reflected in the presence of large number of smooth regions. Sharp images have a great proportion of high-frequency components. Oppositely, blurry images have a low proportion of high-frequency components. High-frequency components have been strongly affected, but low-frequency components are unaffected by the blur. According to source images, most of image information is sharp and only a small portion is blurred. The low-frequency components in the small portion, as non-sparse information, are important for image fusion. The appropriate smoothing operation can reduce shape details to delineate the object accurately. It is important to set the threshold for the smoothing operation. The low threshold causes low contrast edges. A variety of new edge points of dubious significance are introduced. The high threshold looses low contrast edges as broken edges.  
L1-norm and L2-norm are usually used to transform image signals. L2-norm is more stable than L1-norm. A small perturbation of data points can change the regression line of L1-norm a lot, even its direction. However, L2-norm still maintains the shape of original regression line and only makes a small change in parabolic curve. This means that all future predictions of L1-norm are affected much more seriously than L2-norm predictions.  
L1-norm has the built-in feature selection property and tends to produces sparse coefficients. L2-norm does not have this property. For L2-norm, it does not matter whether the source image information can be represented in sparse coefficients.  
L2-norm based regularizer produces non-sparse coefficients. The quality of features is one of the most important properties that directly influences the performance of classification. Good-quality features generally enable samples in the same class to stay relatively closer to each other and thus make it easier to generate a discriminative collaborative representation with non-sparse coefficients [67]. Generally speaking, non-sparse representation is more promising than sparse representation in classification. It is robust against noisy and redundant feature sets [67].  
Furthermore, L2-norm has analytical solutions, and there is always one solution for the certain data set. L2-norm solutions can be obtained by efficient calculation. Due to the sparsity properties, the L1-norm can use sparse algorithms to obtain the solutions. L1-norm is not efficient to solve non-sparse cases. L1-norm may have multiple solutions. So L1-norm is not efficient to obtain solutions as L2-norm.
## 3 Results  
### Flip-Flop Performance  
Performance and area metrics were simulated using the foundry supplied platform development kit. The results were recorded post-layout and are summarized in Table 1.  
As expected, all DICE designs consumed significantly more active power compared to the reference design. This can be attributed to the increased complexity and the larger number of transistors used in the DICE designs. Notably, the CnRx and Guard-Gate flip-flop designs consumed less power than the standard DICE design. The transistor stacking in the guard gate design inherently led to more resistance, which reduced the current and power consumed. This came at a cost, as shown by the clock-to-q delay for the guard gate design, it being the largest delay of all three DICE designs.  
The CnRx DICE design consumed less power compared to the standard DICE design. This can be attributed to the CnRx layout structure which is more power-efficient due to the increased hole mobility in PMOS transistors. However, the performance benefits that the CnRx structure can provide were not present in this design. It is likely that the logic density of a single flip-flop is not enough to show the performance benefits of CnRx, which resulted in no difference in the clock-to-q delay.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Flip-Flop** & **Area (n.u)** & **Power (n.u)** & **Clk-Q Delay (n.u)** \\ \hline Reference & 1 & 1 & 1 \\ DICE & 1.80 & 3.14 & 1.58 \\ CnRx DICE & 1.80 & 2.75 & 1.58 \\ Guard Gate DICE & 1.85 & 2.52 & 1.71 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Relative performance comparison of the proposed FF designs.  
Figure 7: Flip-flop shift register chain implemented in the test chip. Clock buffers were used between each flip-flop.
# An Efficient Person Search Method Using Spatio-Temporal Features for Surveillance Videos +
## 4 Experiments and Results
### Experimental Summary and Discussion  
To fully explore the property of spatial invariance and temporal continuity of videos, the proposed method generates spatio-temporal features to represent persons and organizes these features to achieve person search of surveillance videos. Compared with conventional  
Figure 8: The accuracy comparison between our method and the PPPS method with different numbers of video clips: (**a**) MAP comparison; (**b**) Top-1 accuracy comparison.  
Figure 9: The efficiency comparison between our method, the RCNN method, and the PPPS method with different numbers of video clips.  
methods [37; 38] utilizing frame differencing to detect persons in each frame, our method can detect persons more accurately with the Yolo network and better represent persons using spatial features. Simultaneously, unlike previous person search methods that only use spatial features, the spatio-temporal features not only contain the spatial features of persons in each frame, but also involve the temporal correlation of spatial features between adjacent frames. Thus, the spatio-temporal features are more representative than spatial features and are more suitable for surveillance videos. The spatio-temporal features increase the discrimination between different persons, thereby improving the search accuracy. As shown in the experimental results, the MAP value of our methods is 0.662 and the top-1 accuracy is 0.897, which are both better than the RCNN and PPPS methods.  
Although the proposed method achieves better search performance compared with some state-of-the-art methods, it still suffers from some limitations. Firstly, our method can achieve good performance in single person surveillance scenarios, but the search accuracy drops in crowded environments. Because the persons are occluded by each other, the Yolo network cannot detect a complete bounding box for each person, thereby reducing the description of spatio-temporal features. Furthermore, our method only uses one GRU to calculate the temporal correlation of spatial features between adjacent frames, which limits the representative ability of spatial-temporal features. Finally, due to the high dimensionality of the hidden state sequence, our method adopts an average pooling layer to reduce dimensionality and generate spatio-temporal features, but it may lose some information on hidden state sequence and reduce the discrimination between different persons.
# Research on Optimal Dispatching Strategy for Micro-Energy-Grid of Protected Agriculture
## 2 Model
### Energy Storage Element Model  
State of Charge (SOC) is the ratio of the current electricity content to the rated capacity of the battery, which ranges from 0 to 1. The relationship between the two moments of charging and discharging of batteries is as follows [8]:  
\[SOC(t+1)=(1-\eta_{sd})SOC(t)+\left[\eta_{c}P_{c}(t)-\frac{P_{d}(t)}{\eta_{d}} \right]\times\frac{1}{C_{b}} \tag{3}\]  
where \(SOC(t)\) is the SOC at moment \(t\), \(\eta_{sd}\) is self-discharging coefficient to characterize the capacity of a battery maintaining electricity in a certain period of time, and \(\eta_{c}\) and \(\eta_{d}\) are the charging and discharging efficiency, respectively. \(P_{c}(t)\) and \(P_{d}(t)\) are the charging and discharging power at moment \(t\), and positive or negative values indicate the direction of energy flow. When battery is charging, they are positive in most cases. \(C_{b}\) is the rated capacity of the battery.  
Phase change heat storage is a typical heat storage method utilizing the material change of state. The state of heat (SOH) is the ratio of the residual heat in the thermal storage to the rated capacity. The mathematical model of phase change heat storage device can be expressed as [8]  
\[SOH(t+1)=(1-\eta_{sh})SOH(t)+\left[\eta_{ch}H_{c}(t)-\frac{H_{d}(t)}{\eta_{dh}} \right]\times\frac{1}{C_{h}} \tag{4}\]  
where \(SOH(t)\) is the SOH storage at moment \(t\). \(\eta_{sh}\) is the self-release rate to characterize the capacity of phase change heat storage device maintaining heat in a certain period of time. \(\eta_{ch}\) and \(\eta_{dh}\) represent the charging and discharging heat efficiency of phase change heat storage device, respectively. \(H_{c}(t)\) and \(H_{d}(t)\) are the charging and discharging power of heat storage device at moment \(t\). Positive values indicate the phase change heat storage device is absorbing heat, vice versa. \(C_{h}\) is the rated capacity of the device.
# Feature Learning via Correlation Analysis for Effective Duplicate Detection
## 4 Our Approach
### BERT Algorithm  
The BERT model [7] (Bidirectional Encoder Representations from Transformers) is a cutting-edge natural language processing (NLP) model designed to understand the context of language in a more natural and nuanced way. This capability is achieved through its bidirectional training approach, which processes text by considering both preceding and succeeding words simultaneously. The model is built upon the transformer architecture, known for its efficiency and effectiveness in capturing contextual relationships within text. Figure 5 illustrates the two key stages of the BERT training process: pre-training and fine-tuning.  
The BERT model's training process is divided into the following stages:  
1. Pre-training: During this stage, the model learns a deep understanding of language structure and semantics by processing large volumes of unlabeled textual data. Two core tasks are often employed during pre-training: * Masked Language Modeling (MLM): Random words within a sentence are masked, and the model predicts these masked words based on the surrounding context. * Next Sentence Prediction (NSP): The model learns relationships between pairs of sentences by predicting whether one sentence logically follows another.
2. Fine-Tuning: In this stage, the pre-trained BERT model is adapted for specific downstream tasks through supervised training on labeled datasets. The process involves readjusting the model's hyperparameters to align with the requirements of the target task. For each unique task, a task-specific output layer is added to the model. The fine-tuning process allows the model to leverage its pre-trained linguistic knowledge while tailoring it to the nuances of the given application.  
In this study, the BERT model was fine-tuned to classify bug reports based on their status and identify duplicates effectively. By integrating the top-K feature selection algorithm, the model was optimized to extract and process the most relevant linguistic features. This approach allowed for accurate detection of duplicate bug reports, showing the model's ability to handle complex natural language patterns within technical domains.  
Figure 5: Schematic of the pre-training and fine-tuning.  
The use of BERT's bidirectional understanding and fine-tuning capabilities highlights its strength in addressing tasks like bug report classification, where both semantic and syntactic features are critical for achieving high performance. The integration of pre-training and fine-tuning ensures the model is both generalizable and specialized, making it a powerful tool for duplicate detection in software maintenance.