esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Study  
As shown in Figure 3, a case study is found in the Changsha Metro Line 5 from East Laodong Road Station to Huaya Station. The interval right tunnel passes through Huaya International Hotel at a close distance between mileage YDK27 + 680 m and YDK27 + 750 m. The building is a frame-shear wall structure of 3-22 stories, with a basement of 1 story. The foundations are made of artificial bored piles with a diameter of 1000 mm. The pile toe enters a 1.5 m thick bearing layer. The minimum horizontal distance between the pile foundation of adjacent building and the tunnel edge is approximately 6.01 m. The thickness of soil in this section is about 16.0 m, and the stratum is mainly weathered conglomerate.  
Before shield tunnelling to the Huaya International Hotel, the concrete piles with 800 mm diameter and 600 mm spacing are pre-installed in a single row to isolate and protect the building near the right tunnel side. Isolation piles made of C30 underwater concrete (strength is equivalent to ordinary C35 concrete) are installed from the ground surface to the bottom of the tunnel. At this time, the shortest distance between the edge of right tunnel and the isolation pile is merely 3.7 m.  
In order to study the restraint effect of isolation pile on surface settlement trough induced by shield tunnelling, the surface settlement observation points were monitored by precision leveling instrument and indium steel ruler. The monitoring holes were drilled by 140 mm crystal drill.  
Figure 3: Relationship among building, isolation pile, and tunnel: (**a**) top view and (**b**) sectional view.
# Detecting Suicidal Ideations in Online Forums with Textual and Psycholinguistic Features
## 3 Materials and Methods
### Data Preprocessing
#### 3.2.4. Lemmatization  
We conducted lemmatization to convert different inflections of words into a common root, lemma. Lemmatization allows semantically similar words to be grouped. For example, the words \"drives\", \"drove\", and \"driven\" are converted to \"drive\". This step helps reduce the number of textual features. We used the SpaCy (version 3.5.1) open-source Python library for this task [31]. It is worth noting that lemmatization was performed as a prerequisite step for generating TF-IDF and NRC features only but not for LIWC. LIWC contains features pertinent to past, present, and future focuses. If we were to convert all forms of a verb into the present tense, the LIWC analysis tool would not be able to capture these features.
## 3 Methodology
### Model Architecture  
CBF-IDS integrates CNN and BiLSTM, enabling the efficient processing of large-scale and high-dimensional network traffic. Furthermore, to tackle the significant issue of class imbalance within the dataset, the model incorporates the focal loss. The CNN component excels at extracting spatial features and capturing significant patterns in the input data. On the other hand, the BiLSTM component excels at capturing temporal dependencies, allowing the model to analyze the sequential nature of time series data.  
The initial three layers of this model consist of convolutional layers, with their respective numbers of convolutional kernels set at 64, 128, and 256. These convolutional layers utilize kernels to extract salient features and generate informative feature maps. The feature maps are subjected to dimension reduction by utilizing a max-pooling layer with a size of 2 \(\times\) 2, aiming to decrease the dimension of the data while preserving crucial features. Subsequently, the model adds a batch normalization (BN) layer, which greatly enhances its generalization capabilities. In order to fulfill the input requirements of the subsequent BiLSTM layer, a reshape layer is integrated after the BN step. This layer allows for adjusting the output dimensions from the preceding layer to align with the expected dimensions of the subsequent BiLSTM layer. The BiLSTM with 64 units is utilized to process reshaped time series features. In order to prevent overfitting, a dropout layer with a parameter of 0.5 is added after the BiLSTM layer. Next, a fully connected layer is added to connect all neurons from the previous layer, facilitating the integration of feature extraction. Finally, to derive the probability distribution of each data class, the softmax function is applied as the activation function. During the training process, we employed the Adam optimizer, with a learning rate set at 0.001. The loss function used was focal loss, which increased the weight of the minority class to address the issue of class imbalance.
# Quantum Key Distribution in Kubernetes Clusters
## 3 Quantum Software Stack 2.0  
As the first contribution of this paper, we introduce in this section a complete software refactoring of the original QSS with the addition of significant changes in both architecture and programming approaches. In Figure 2, we illustrate with dashed lines the components that have been either introduced or considerably modified from the earliest version. The rest of the components demanded minor changes.  
Starting from code refactoring, QSS 2.0 introduces a paradigm shift from a synchronous approach based on multithreading to an asynchronous one based on multiprocessing (Section 3.1). Moreover, this new QSS version allows long-distance QKD exchanges through trusted repeaters, becoming suitable for large QKD network scenarios (Section 3.2). QSS 2.0 also includes a routing module within the QKS, making possible the discovery of the QKD network topology and finding the best path over trusted repeaters when a long-distance exchange occurs (Section 3.3).
# An Intelligent Measurement Method and System for Vehicle Passing Angles
## 2 Design of the Measurement System
### Hardware System Framework  
For the convenience of system operation, the measurement system consists of measurement equipment and an operator interface. The operator interface is a handheld device used to initiate and terminate the system and to receive the point cloud data and calculated parameter data processed by the industrial PC mounted on the wheeled robot. As shown in Figure 2, the hardware block includes a wheeled robot and a handheld device. The wheeled robot's dimensions are 295 mm in width, 384 mm in length and 88 mm in height, while the LIDAR has a diameter of 103 mm and a height of 72 mm. The system is suitable for common household vehicles, off-road vehicles, delivery vehicles and any special vehicle with a chassis higher than 160 mm.  
In the robot, the STM32 is used as the controller for its actuator, which controls the motor system to drive the [PERSON] wheel to achieve movement via the CAN bus and electronic speed controller (ESC). The wheeled robot can move in all directions without rotating. In such a case, the motion model of the equipped LIDAR can be considered as a linear model [27], simplifying the distortion correction problem caused by the LIDAR data's width moving.  
The LIDAR used in our system is a VLP-16, which has a 16-channel laser sensor distributed over a vertical FOV of 30\({}^{\circ}\) (\(\pm\)15\({}^{\circ}\)) and an angular resolution of 2\({}^{\circ}\). The camera selected in our system is the RealSense D435.  
The digital transmission and receiver units access a handheld PC and the industrial PC via USB universal ports for communication to facilitate remote start-up and interrupt control.  
Regarding the placement position of the LIDAR, if the Z-direction is oriented towards the front of the wheeled robot, both the vehicle and the ground are too close to the sensor, causing interference in obtaining the point cloud data. Therefore, the LIDAR is horizontally positioned on the wheeled robot horizontally, as shown in Figure 1. This configuration  
Figure 2: Hardware block diagram.  
allows the laser lines above the horizontal line of the LIDAR to gradually scan the vehicle chassis and a portion of the tires as the wheeled robot moves.
## 3 Experiments and Results
### Metrics  
To study the performance of each proposed model in solving the IK problem, different metrics have been used. As was mentioned before, the mean squared error has been used as a predefined metric. Additionally, the position and orientation error have been implemented as callbacks and added to the training.  
For the position error, the three-dimensional Euclidean distance has been employed. However, for the orientation error, a range of options have been considered after studying the options brought forward by [31; 32; 33]. To select a metric, the orientation representation has to be taken into account, as it depends on whether quaternion or rotation matrices are being used. Euler angle representations have not been considered since they are not as accurate [34].  
In case the orientation is defined using unit quaternions, three possible errors are defined:  
\[dist(q_{1},q_{2})=min\{\|q_{1}-q_{2}\|,\|q_{1}+q_{2}\|\} \tag{5}\]  
\[dist(q_{1},q_{2})=arccos(|q_{1}\cdot q_{2}|) \tag{6}\]  
\[dist(q_{1},q_{2})=1-|q_{1}\cdot q_{2}| \tag{7}\]  
As [31] notes, Equation (5) defines a _pseudometric_ since \(dist(q,-q)=0\) but \(q\
eq-q\). However, the mapping from unit quaternions to SO(3) is 2-to-1, meaning that there are two possible unit quaternions for every rotation angle (\(q\) and \(-q\)). Therefore, the proposed _pseudometric_ becomes a metric on 3D rotations. It gives results in the range \([0,\sqrt{2}]\). The option given by Equation (6) measures the angle between the two unit quaternions, restricting the angles to the first quadrant. The use of the vector dot product \(\cdot\) with unit quaternions means that \(dist(q,q)=dist(q,-q)=arccos(1)=0\). This metric gives results in the range \([0,\frac{\pi}{2}]\). Finally, Equation (7) gives an estimate of the length of the geodesic path on the 4D unit sphere between the two quaternions. Its results are in the range \([0,1]\).  
If, instead, the orientation is defined using rotation matrices, four possible errors are considered:  
\[dist(R_{1},R_{2})=arccos(\frac{tr(R)-1}{2})\text{, where }R=R_{1}R_{2}^{T} \tag{8}\]  
\[dist(R_{1},R_{2})=\|R_{1}-R_{2}\|_{F}=\sqrt{tr((R_{1}-R_{2})^{T}(R_{1}-R_{2}) )} \tag{9}\]  
\[dist(R_{1},R_{2})=\frac{1}{\sqrt{2}}\|Log(R_{1}R_{2}^{T})\|_{F}\text{, where }Log(R)=\begin{cases}0&\theta=0\\ \frac{\theta}{2 SimB}(R-R^{T})&\theta\
eq 0\end{cases} \tag{10}\]  
\[dist(R_{1},R_{2})=\|I-R_{1}R_{2}^{T}\|_{F} \tag{11}\]  
The first metric, given by Equation (8), measures the angle of the difference rotation matrix between \(R_{1}\) and \(R_{2}\), which is given by \(R_{1}R_{2}^{T}\). The second option, stated in Equation (9), represents the Euclidean metric for rotation matrices, given by the Frobenius norm of the difference between two rotation matrices. Another possible choice is the one given by Equation (10), which represents the Riemannian distance between two rotations. It is the length of the shortest geodesic curve that connects \(R_{1}\) and \(R_{2}\). Finally, Equation (11) comes from a general \(SO(n)\) metric defined in [35], and is in the range \([0,2\sqrt{2}]\).
# ULYSSES: Automated FreqUentLY ASked QueStions for KnowlEdge GraphS
## 3 Methodology
### The Problem  
Next, we informally define the problem we address. The problem can be described as follows: Given a Knowledge Graph \(G\), efficiently construct a set of question-answer pairs that best captures the content of the information contained in \(G\).  
An idea based on similar literature for resolving the aforementioned problem would be first to try and identify the most interesting parts of the graph and use them to generate the corresponding text to be used as an answer. Then, construct the appropriate questions for those answers.  
For addressing the first subproblem, past approaches in the summarization domain have used adapted centrality measures to successfully identify the most important areas of a KG [1]. However, calculating centrality measures on top of large graphs, counting millions of nodes, is computationally expensive and in many cases unfeasible.  
We assume now that for the KG \(G\) we have available a query log \(\mathcal{Q}=\{q_{1},\cdots,q_{n}\}\). This assumption is reasonable, as all large KGs offer a SPARQL endpoint that logs user queries for various purposes, as multiple studies have already confirmed (e.g., [28]).  
Having such a query log available, our first idea is that we can use it instead of the KG to mine the most important areas of a KG, as indeed the most interesting parts of the graph are mostly queried. Such a query log (a) directly provides us with the most frequent questions, (b) is computationally cheap, as query logs need to be scanned only once for finding the most frequent queries, and they usually do not scale beyond thousands of queries, and (c) offers an optimal way for retrieving the answers from a KG--directly issue the few selected queries to the KG using the corresponding SPARQL endpoints, in order to be answered.
## 5 Conclusions  
Despite its many benefits, HIL simulation poses several challenges that must be addressed for it to be widely adopted in building systems applications. One challenge is hardware and software compatibility. HIL simulation requires specialized hardware and software, and integrating these components with existing building systems can be difficult. Another challenge is real-time data acquisition. HIL simulation requires real-time data to be acquired from the building systems being evaluated, and this data must be processed and fed back into the simulation in real time. This requires specialized data acquisition hardware and software that can operate in real time. Finally, system complexity is a major challenge associated with HIL simulation. Cities, buildings, and systems are becoming increasingly complex, and simulating these systems in real time requires significant computing power. This can be a limiting factor for smaller building systems applications where the ratio of added benefits vs. computer power and component costs may be too high.
## 4 Experiment and Results Analysis
### Ablation Study  
In order to verify the effectiveness of the interaction between the aspects and the components that maintain sentiment consistency, an ablation experiment is performed on three datasets with consistent settings.  
For verifying the validity of the interaction between aspect items, the redundant BERT layer is no longer used to extract the aspect items, and the final output of BERT is directly input into the transformer decoder module, and a transformer decoder module is reduced; only the interaction between subtasks is performed, and the objective function becomes a loss function with only the final prediction result.  
For verifying the validity of the component that maintains sentiment consistency, the component is removed, and the representation of the sentiment features that come out of the transformer decoder module is classified directly through the softmax classification layer.  
The results of the ablation experiment are shown in Table 3, and it can be seen from the table that after removing the interaction of the aspect items, the proposed model performance has a certain degree of degradation on the three datasets, with F1 decreasing by 2.21, 1.72, and 0.72%. After removing the sentiment consistency component, the degradation in model performance was small, with F1 decreasing by 1.68, 0.86, and 0.31%. From these two experiments, it can be shown that the interaction between the aspect items is effective with the emotionally consistent components. The term w/o means to remove the module, ATI represents the interaction between aspect items, and MSC represents the sentiment consistency component.
## Materials and Methods
### Optical Acquisition and Prototype Features  
The acquisitions were performed directly in the field (immediately after manually collecting the samples), without any sample preparation. For each sample, 5 single berries were analyzed to obtain a total dataset of 400 optical analyses in 2020, corresponding to 40 acquisitions for time 0 and 120 acquisitions for the other samplings. In 2021, 480 optical analyses (corresponding to 120 acquisitions per sampling date) were collected and made up the dataset.  
A first version of the prototype was described in 2021 [22] as a pre-prototype; the device was used for optical acquisitions during 2020 sampling. The new version of the device used for 2021 sampling consists of 2 optical sensors with 6 wavelengths each, for a total of 12 wavelengths, as described in Table 1.  
The prototype incorporates the two sensors just mentioned (AMS, models AS7262 visible and AS7263 NIR, Premstaetten, Austria, Europe) for spectral acquisitions in visible (vis) and in short wave near-infrared (SW-NIR) regions. The sensors have a 16-bit radiometric resolution and 12 independent on-device optical filters, from 450 to 860 nanometers. The full-width half-maximum of the vis sensor is 40 nm, while the full-width half-maximum of the SW-NIR sensor is 20 nm. The use of these commercial optical cores allows the costs of the prototype to be kept extremely low.  
The wavelengths used in the device are the same as in the pre-prototypal version [22], precisely because each wavelength corresponds to a particular absorption peak of particular interest in this type of analysis. As reported by [PERSON] et al. [8], 630 and 690 nanometers are near the characteristic peak of chlorophyll, 730 nanometers is near the third overtone of the -OH bond, and lastly, 810 and 860 nanometers are near the combination band of the -OH groups of sugars.  
Unlike the first version of the device, the two sensors are now integrated into a clamp that enables the operator to fully embrace the berry grape (Figure 1a,b) and capture all the readouts at once, without the need to repeat the analysis with the first sensor and then  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Wavelengths (nm)**} \\ \hline Sensor 1 & 450 & 500 & 550 & 570 & 600 & 650 \\ Sensor 2 & 610 & 680 & 730 & 760 & 810 & 860 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Device wavelengths.  
with the second sensor. Moreover, the instrument was configured to perform an average of 10 scans for each acquisition, trying to reduce as much as possible the experimental noise associated with the light that often creates issues with optical analyses.
# Efficient Security Scheme for Disaster Surveillance UAV Communication Networks
## 4 Preliminaries of the Security Protocol
### The Ring-LWE Encryption Scheme
#### 4.1.3 Encryption  
The message \(m\) is encoded to a polynomial \(\tilde{m}\in R_{q}\). Error polynomials \(e_{1},e_{2},e_{3}\in R_{q}\) are generated from \(\mathcal{X}_{\sigma}\) using a discrete Gaussian sampler. Then, compute the ciphertext \((\tilde{c}_{1},\tilde{c}_{2})\):  
\[\tilde{e}_{1}\gets NTT(e_{1});\tilde{e}_{2}\gets NTT(e_{2}),\]  
\[(\tilde{c}_{1},\tilde{c}_{2})\leftarrow(\tilde{a}*\tilde{c}_{1}+\tilde{e}_{2},\tilde{p}*\tilde{c}_{1}+NTT(e_{3}+\tilde{m}).\]
## 5 Results and Discussion
### Rationality of Operating the Multireservoir System  
A dry year was selected as the critical year for the hydraulic and logical analyses to avoid repeating the results of this section. Figure 3 shows that the water levels for WJD, DF, and HJD varied based on the CA during different periods. The inflow into the HJD reservoir during dry years had an initial interval of 88 m\({}^{3}\)/s (Table 2): This value was small, and if the system wants to meet the minimum required hydropower (680 MW), the outflow from the HJD reservoir should increase, which would allow the downstream reservoirs to have enough inflow for hydropower generation. Thus, the water level decreased in this reservoir, and the water level for the other reservoirs increased to reach a normal level (Figure 3). In fact, the value of the inflow into the HJD reservoir was small during the initial time interval, and the value of the inflow into the HJD reservoir increased to reach a normal water level (1140 m). The outflow from the HJD reservoir increased, which did not cause a real shortage for the downstream reservoirs: Thus, the water level decreased in the HJD reservoir to keep the water level under normal conditions for the downstream reservoirs. The water level for the reservoirs after the HJD reservoir did not decrease until the final period (Figure 3). Table 6 shows more details for the reservoirs and hydropower plants based on the CA. The outflow for the WJD during all periods was more than 100 m\({}^{3}\)/s based on Table 6: Thus, navigation security was considered for the WJD reservoir. The total hydropower output for all periods was more than 680 MW: Thus, the smallest amount of required hydropower was well supplied. Additionally, the variation in hydraulic heads during the wet seasons had an increasing trend, and it had a decreasing trend during the dry seasons, which is consistent with Figure 3. Additionally, the hydropower outputs for HJD were close to zero, because the inflow value for the HJD reservoir during these two periods was considerable: Thus, the reservoir should increase the storage value during critical periods, such as dry seasons. Therefore, the outflow of hydropower generation for the HJD reservoir decreased during these periods. Finally, the hydraulic head increased, and there was enough storage for critical periods. Figure 3: Water level for (**a**) HJD reservoir, (**b**) DF reservoir, and (**c**) WJD.
# Establishing a Generic Geographic Information Collection Platform for Heterogeneous Data
## 3 Result and Analysis
### Illustrative Examples
#### 3.2.3 Challenge 2: Flexible Collection Modes  
Our platform allows users to adopt various work modes to manage personnel scheduling and quality requirements in fieldwork. In this collection task, managers adjust the collection mode manually based on the difficulty of accessing the collection areas. The task allocation interface is shown in Figure 4(b).  
For difficult-to-reach areas, managers set the mode to partitioned collection. In this mode, only collection personnel need to gather data, and no field inspectors are required for second verification. Managers visually check the data uploaded by collection personnel in the management system.  
For easy-to-reach areas, managers set the mode to synchronized collection and review. The system assigns both collection personnel and field inspectors to these areas. Figure 6a shows the spatial data collection interface for field collectors. Field review personnel download the data uploaded by collection personnel and verify it on-site. Figure 6b shows the software interface for field inspectors.  
For areas where there are existing data that only need verification, managers set the mode to partitioned review. In this case, the system assigns inspectors to check if the existing data have changed and to update them if necessary.