esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Performance Analysis of BDS-3 Integrity Concept
## 5 Results and Discussion
### Test Results for B1C and B2a Signals  
The SIS-URE of the B1C/B2a signal at the WUL is calculated based on the B1C/B2a broadcast message from 1 July 2019 to 25 September 2019, with a sampling interval of 30 s. The URE sequence of the 18 satellites of the basic BDS-3 constellation are shown in Figure 6, in which the red dots indicate the URE value and the black dots indicate the SISA value. The x-axis is in units of days of the year, and the y-axis is in meters.  
Figure 5: URE and 4.42 \(\times\) URA sequences of BDS-3 satellites form 1 January 2019 to 31 July 2019. **(a)** PRN 19; **(b)** PRN 20; **(c)** PRN 21; **(d)** PRN 22; **(e)** PRN 23; **(f)** PRN 24; **(g)** PRN 25; **(h)** PRN 26; **(i)** PRN 27; **(j)** PRN 28; **(k)** PRN 29; **(l)** PRN 30; **(m)** PRN 32; **(n)** PRN 33; **(o)** PRN 34; **(p)** PRN 35; **(q)** PRN 36; **(r)** PRN 37. SCID + Number in the titles of figures indicate the Satellite Configure Index used by the internal information processing system.  
Figure 6: _Cont._  
According to Section 2.3.1, the URE of B1C/B2a should be compared with the NTE, which is 4.42 times the SISA. The SISA can be obtained through the SISAI parameters broadcast in the B-CNAV1/B-CNAV2 navigation message, and its value may change with the change of the index parameters. It can be seen that the URE of all satellites are less than 4.42 times the SISA, and there is no satellite service failure condition of B1C and B2a signals during the test.  
In fact, there have been satellite orbit calculation and message upload anomalies in the ground segment during the assessment period, but they are detected and disposed of by the ground segment monitoring measures. This indicates the effectiveness of the fault monitoring and processing functions of the BDS-3 ground segment. We also need to be aware that the assessment of GNSS service integrity is a long-term and elaborate process. As long as the time is long enough, any potential risks that seem to have a small probability might happen. Therefore, we will continuously evaluate and analyze the integrity of the BDS-3 OS signals, and release the information and status in time. In addition, we will also work to further improve the robustness of the BDS-3 satellite and ground segment to ensure the stable operation of the system and the safe and reliability of the service.
# A Distance-Encoded Bloom Filter for Fast NDN Name Lookup
## 3 Proposed Scheme
### Update  
A binary Bloom filter does not support incremental deletion of members. Moreover, for high-speed lookups, it is important to decouple the lookup structure from the structure supporting the updates. To enable incremental updates, an auxiliary structure known as a counting Bloom filter (CBF) is commonly used. A distance-encoded Bloom filter can also be updated in a similar way using a CBF.  
When inserting a member, each hash function sets two bits in the distance-encoded Bloom filter--one for the \(sd\) value and the other for the (\(ld-sd\)) value. At the same time, the counting Bloom filter (CBF) increments the counters for those bit positions. For deletion, the CBF decrements the corresponding counters. If a counter reaches zero, the associated bit in the distance-encoded Bloom filter is cleared.  
To update the Bloom filters described above, the distance values, \(sd\) and \(ld\), of the nodes affected by the update must be recalculated. If a prefix is inserted as a leaf, its distance values are both zero, i.e., \(sd=ld=0\), and the only affected nodes are some of its ancestors, whose \(sd\) or \(ld\) values may change. For example, suppose that /a/b/c/D/E is newly inserted as a leaf prefix in Figure 7. The \(sd\) and \(ld\) of that node are \(\langle 0,0\rangle\). The intermediate node/a/b/c/D must also be inserted, and its \(\langle sd,ld\rangle\) is \(\langle 1,1\rangle\). The distance of the ancestor /a/b/c was originally \(\langle 3,4\rangle\), but it now becomes \(\langle 2,4\rangle\). For an inserted prefix at level \(k\), most \(k\) distance values are adjusted.  
Figure 12: Comparison of distance-encoded Bloom filter and conventional Bloom filter: (**a**) distance-encoded Bloom filter; (**b**) conventional Bloom filter.  
In the case of non-leaf prefix insertion, which means an intermediate (non-prefix) node becomes a prefix node, its \(sd\) value becomes 0 while its \(ld\) value remains unchanged. No descendant nodes are affected. The only affected nodes are some ancestors where only the \(sd\) values are adjusted to reflect the new distance to the inserted node; the \(ld\) values remain unchanged. For instance, suppose/a/b/c/d is newly inserted as a prefix in Figure 7. The node N\({}_{3}\) (/a/b/c/d) becomes a prefix node, and its \(sd\) value is adjusted to 0. The \(sd\) value of N\({}_{2}\) (/a/b/c) is adjusted to 1 while its \(ld\) value remains unchanged.  
In the case of prefix deletion, the nodes affected by the update are also limited to some ancestors, similar to the insertion case. Therefore, the maximum number of nodes whose \(\langle sd,ld\rangle\) values need to be updated is \(k\) when a prefix is inserted or deleted at level \(k\). Instead of reconstruction of the entire distance table, only a limited number of entries need to be updated, which enables on-the-fly incremental updates.
# Calculation Method for Electricity Price and Rebate Level in Demand Response Programs
## 4 Numerical Simulation Model
### Utility Function for Suppliers  
Thermal power generation has taken a large portion in the power supply, e.g., approximately 85% in Japan [28], and its fuel cost, as is well-known, has powerful influence on the operational costs of the power suppliers. The fuel costs of thermal power units are traditionally approximated as quadratic functions by means of generating power [29; 30; 31; 32; 33; 34]. For these reasons, the authors added the following assumptions to make the operational cost function.  
**Assumption 2**.: _Total operational cost in the power supply is approximated as the quadratic function relying on the fuel costs of thermal power units._  
**Assumption 3**.: _The power suppliers decide the electricity price to maximize their utility function at the annual average of electricity consumption (103.94 kW in this paper)._  
The normalized utility function of the power suppliers was defined as:  
\[U_{1}^{\dagger}(s_{t})=-A^{\dagger}s_{t}^{2}+\left(p_{1}^{\dagger}-B^{\dagger} \right)s_{t}-C^{\dagger},\text{ for }\forall t, \tag{28}\]  
where \(p_{1}^{\dagger}\) is the electricity price in the normalized function; \(A^{\dagger}\), \(B^{\dagger}\), and \(C^{\dagger}\) are the coefficients for the normalized function.  
With reference to [35; 36], the coefficients of the fuel cost function were set to \(2.73\times 10^{-7}\) JPY/kWh\({}^{2}\), \(2.27\) JPY/kWh, and \(1.50\times 10^{5}\) JPY. In addition, the maximum and the minimum outputs of power generation (\(G^{\text{max}}\) and \(G^{\text{min}}\)) were set to \(175\) kW and zero, respectively, based on the electricity consumption of \(500\) households in the record. With these values, the coefficients in Equation (28) were assumed to \(4.63\times 10^{-5}/\text{kWh}^{2}\), \(1.20\times 10^{-7}/\text{kWh}\), and \(0\). Owing to Assumption 3, we can calculate \(p_{1}^{\dagger}\) as:  
\[p_{1}^{\dagger}=2A^{\dagger}\overline{s}+B^{\dagger}, \tag{29}\]  
where \(\overline{s}\) is the annual average of electricity consumption.  
By multiplying \(\frac{p^{*}}{p_{1}^{\dagger}}\) on both sides in Equation (29), we can update the coefficients as \(A\) and \(B\), and as a result, the functions of the power suppliers' utility and the operational cost were written as:  
\[U_{1}(s_{t})=-As_{t}^{2}+(p^{*}-B)s_{t},\text{ for }\forall t, \tag{30}\]  
\[E_{t}(s_{t})=As_{t}^{2}+Bs_{t}\left(=E(s_{t})\right),\text{ for }<!--\forall t, \tag{31}\]  
where \(A=\frac{p_{1}^{*}}{p_{1}^{*}}A^{\dagger}\); \(B=\frac{p_{t}^{*}}{p_{1}^{*}}B^{\dagger}\).  
Figure 1 displays the resulting operational cost function of the suppliers, and its coefficients are summarized in Table 1.
# Bloch Oscillations in the Chains of Artificial Atoms Dressed with Photons
## Appendix C Quantization of Electromagnetic Fields in Wavepackets  
For the secondary quantization in the wavepacket we use the method proposed by [PERSON] [71]. First, we consider the linearly polarized electric field to have the spatial-temporal dependence for a 1D cavity resonator [26]. We assume the cavity formed by two parallel perfectly conductive planes distanced on the length \(L\). The positive-frequency part of electric field operator is given [26]  
\[\mathbf{\hat{E}}^{(+)}(x,t)=\mathbf{e}\!\!\sum_{\
u}\sqrt{\frac{\hbar\omega_{ \
u}}{2\epsilon_{0}V}}\hat{a}_{\
u}e^{i(k_{\
u}x-\omega_{\
u}t)}, \tag{107}\]  
where \(V\) is normalization volume, \(k_{\
u}=\
u\pi/L\), \(\
u\) is an integer value, \(\omega_{\
u}=k_{\
u}c\).  
Next, following [71], we introduce the special complete system of basis classical wavepackets \(\mathbf{u}_{\alpha}(x,t)\), every one of which is given by the superposition of cavity modes  
\[\mathbf{u}_{\alpha}(x,t)=\mathbf{e}\sum_{\beta}\sqrt{\frac{\hbar\omega_{\beta}} {2\varepsilon_{0}V}}B_{\alpha\beta}e^{i(k_{\beta}x-\omega_{\beta}t)}, \tag{100}\]  
where \(B_{\alpha\beta}\) are elements of unit matrix with conventional unitary condition \(\sum_{\gamma}B_{\gamma\alpha}B_{\gamma\beta}^{*}=\delta_{\alpha\beta}\), \(\delta_{\alpha\beta}\) is the Kronecker delta. Equation (100) using (100) may be rewritten as  
\[\begin{array}{c}\mathbf{\hat{E}}^{(+)}(x,t)=\mathbf{e}\sum\limits_{\
u}\sum \limits_{\
u^{\prime}}\delta_{\
u\
u^{\prime}}\sqrt{\frac{\hbar\omega_{\beta}} {2\varepsilon_{0}V}}\hat{a}_{\
u^{\prime}}e^{i(k_{\
u}x-\omega_{\
u}t)}=\\ \mathbf{e}\sum\limits_{\
u}\sum\limits_{\
u^{\prime}}\mathcal{D}_{\eta\
u^{ \prime}}B_{\gamma\
u}^{*}\sqrt{\frac{\hbar\omega_{\beta}}{2\varepsilon_{0}V}} \hat{a}_{\
u^{\prime}}e^{i(k_{\
u}x-\omega_{\
u}t)}=\\ \sum\limits_{\
u}\sum\limits_{\gamma}B_{\gamma\
u}\hat{a}_{\
u}\mathbf{u}_{ \gamma}(x,t)\end{array} \tag{101}\]  
Using (101) and unitary condition, we obtain  
\[\mathbf{\hat{E}}^{(+)}(x,t)=\sum\limits_{\gamma}\hat{c}_{\gamma}\mathbf{u}_{ \gamma}(x,t), \tag{102}\]  
where \(\hat{c}_{\gamma}=\sum\limits_{\
u}B_{\gamma\
u}\hat{a}_{\
u}\) is a new set of creation-annihilation operators, satisfying the Bose commutative relations \(\left[\hat{c}_{\alpha},\hat{c}_{\beta}^{+}\right]=\delta_{\alpha\beta}\). The basis wavepackets are non-orthogonal:  
\[\int\limits_{V}\mathbf{u}_{p}(x,t)\cdot\mathbf{u}_{q}^{*}(x,t)dV=\sum\limits_ {\alpha}\frac{\hbar\omega_{\alpha}}{2\varepsilon_{0}}B_{\alpha}B_{\alpha}B_{ \alpha}^{*}. \tag{103}\]  
The field Hamiltonian is  
\[\begin{array}{c}\hat{H}_{ph}=\varepsilon_{0}\int\limits_{V}\Big{\{}\mathbf{ \hat{E}}  
respectively, where \(\hat{c}^{+},\hat{c}\) are annihilation-creation operators for photon in the wavepacket. The coupling factor \(g=\mathbf{d}_{ab}\cdot\mathbf{u}_{0}(t)/\hbar\) becomes time-dependent, which allows us to consider the case of driving light in the form of the rather long laser pulse (assumed that the pulse duration strongly exceeds the period of high-frequency filling, which guarantees the RWA validity in our model).
###### Abstract  
With the increasing integration of renewable energy, the secure operation of distribution networks faces significant challenges, such as voltage limit violations and increased power losses. To address the issue of reactive power and voltage security under renewable generation uncertainty, this paper proposes a graph-based security reinforcement learning method. First, a graph-enhanced neural network is designed, to extract both topological and node-level features from the distribution network. Then, a primal-dual approach is introduced to incorporate voltage security constraints into the agent's critic network, by constructing a cost critic to guide safe policy learning. Finally, a dual-critic framework is adopted to train the actor network and derive an optimal policy. Experiments conducted on real load profiles demonstrated that the proposed method reduced the voltage violation rate to 0%, compared to 4.92% with the Deep Deterministic Policy Gradient (DDPG) algorithm and 5.14% with the Twin Delayed DDPG (TD3) algorithm. Moreover, the average node voltage deviation was effectively controlled within 0.0073 per unit.  
distribution networks; reactive power optimization; deep reinforcement learning; constrained Markov decision process
## 9 Results and Discussion
### The Impact of Various Battery Technologies on System Performance  
According to the results given in Table 4, the configuration made up of Ni-Fe battery technology provided the lowest LCC value when compared to the configurations made up of LA and Li-Ion battery technologies. The Ni-Fe battery configuration offered the optimal LCC value as 367,586 USD and its required number of PV panels and Ni-Fe batteries to electrify the study area are 511 and 351, respectively.  
The next optimal configuration to electrify the study area is the configuration made up of LA battery technology, which has provided the optimal LCC value as 607,030 USD, which is about 65% higher than the LCC value obtained with the Ni-Fe battery technology and about 41% lower than the LCC obtained with the Li-Ion battery technology. To electrify the study area using the configuration made up of LA battery technology, 474 PV panels and 136 LA batteries are required.  
The final optimal configuration to electrify the study area is the configuration made up of Li-Ion battery technology, which produced the optimal LCC value as 1,019,683 USD. Which is about 177% and 68% higher than the LCCs obtained with the configurations made up of Ni-Fe and LA battery technologies, respectively. Finally, the number of PV panels and Li-Ion batteries required to electrify the study area with this configuration are 436 and 155, respectively.  
Figure 8 depicts the annual energy production of the PV panels and biomass generator, as well as the charging and discharging process of the battery bank based on load demand.
## 3 Methodology
### Data Acquisition  
Before arranging the acquisition station, the instruments were sequenced and numbered to identify and intercept the fitting data by the post-processing software. The receiving point positions of all acquisition stations were determined by the longitude and latitude acquired by a GPS. The consistency of each receiving point was controlled by the internal synchronous clock of the GPS, and the error of each receiving point position  
Figure 6: Waves of each seismometer in huddle test.  
was less than 10\({}^{-1}\) s. Each collection station separately collected experimental data and transmitted it to the computer terminal through a 4G signal.  
Two survey lines were set up in this experiment: the WD1 line had 10 survey points, with a spacing of 20 m, a total length of 380 m and an actual exploration length of 190 m; the WD2 line had 8 measuring points, with a spacing of 20 m, a total length of 340 m and an actual exploration length of 170 m. Taking the WD1 line as an example, 11 acquisition stations were used to survey one point each time, and each acquisition and observation lasted for 40 min. After the acquisition, the station at the back of the line was moved to the front to realize the rolling acquisition (Figure 7).
## 6 Results and Discussion
### Sentiment Analysis  
We conducted sentiment analysis (SA) for all 108 different models, as mentioned earlier in Section 5.2. We tested the models for SA for both the Word2 vec and FastText models as well.  
Generally, both the Word2 vec and FastText models provided good performance, with FastText significantly outperforming all Word2 vec models. In particular, as illustrated in Figure 8, FastText with the CBOW architecture and Wikipedia corpus scored the best accuracy, which is 99%.  
On the other hand, the Word2 vec models provided unstable performance among the different models, as illustrated in Figure 9. Yet, they represented a good score with 91% accuracy with the Watan Corpus. In the following, we analyze the effect of the different hyperparameters for both the Word2 vec and FastText models.
# Scheduling Optimization of Prefabricated Construction Projects by Genetic Algorithm
## 5 Computational Experiments  
### Tested Instances and Parameter Setting  
In this section the single execution mode project scheduling problem data set in the international general project scheduling standard problem library PSPLIB [68] was used as a test example to verify the effectiveness of the proposed algorithm. The PSPLIB question library is generated by the ProGen program under strict parameter control. The single-execution model project scheduling problem data set consists of four data sets, namely, J30, J60, J90, and J120, which contain 32, 62, 92, and 122 activities (including start and end virtual activities), respectively. The four data sets have four types of renewable resources. Each data set of J30, J60, and J90 has a total of 480 examples, which can be divided into 48 groups according to the different parameter combinations. Every group contains 10 problem examples. The J120 data set has a total of 600 examples, which can be divided into 60 groups, and each group is also composed of 10 problem examples. The optimal solution of J30 data set was obtained by exact algorithm. J60, J90, and J120 did not yet obtain the optimal solution, but PSPLIB provides the lower bound of the solution and the best solution at present. These data can be obtained from the website [[http://www.om-db.wi.tum.de/psplib/](http://www.om-db.wi.tum.de/psplib/)]([http://www.om-db.wi.tum.de/psplib/](http://www.om-db.wi.tum.de/psplib/)) accessed on 12 April 2021. The problem sets of J30, J60, and J120 were selected for experimental calculation, and the experimental results were compared with the other algorithms in recent years. The algorithm in this section is implemented using Matlab2014a. The operating environment is a computer with Windows10 system, 8 GB memory, 1.80 GHz CPU frequency, and Intel(R) Core(TM) i5-8265U processor.  
Figure 5: Schematic of PPX performing cross-operation.  
Figure 6: Schematic of mutation operation.  
For the convenience of comparison, the number of generated scheduling schemes was set to 50,000, and the algorithm related parameters were set as shown in Table 2. _Pop_size_ represents the size of the population, _Max_gen_ is the maximum number of iterations of the algorithm, _Pc_ represents the crossover probability, and _Pmt_ represents the mutation probability.
# Enhancing Kazakh Sign Language Recognition with BiLSTM Using YOLO Keypoints and Optical Flow
## 5 Results and Discussion
### Results on 13-Class Dataset  
For the 13-class dataset (Table 2), the usage of optical flow results in a decline in accuracy from 77% (without optical flow) to 68%. Some gesture classes, such as \"Kim\" and \"Bar\", exhibit a notable decrease in recall, while the \"Tegi\" class is not recognized at all when optical flow is used. However, in certain cases, such as the \"G\" and \"Gh\" classes, the optical flow version of a system achieves more balanced results. These findings suggest that motion processing may introduce noise, reducing overall classification quality.
# A Multichannel-Based Deep Learning Framework for Ocean SAR Scene Classification
## 2 Dataset and Data Processing
### Sentinel-1 Wave Mode  
The Sentinel-1 (S-1) mission is designed as a two-satellite constellation including polar-orbiting and sun-synchronous satellites [35; 49], namely, Sentinel-1A and Sentinel-1B, launched by the European Space Agency (ESA) in April of 2014 and 2016, respectively. In order to provide an effective 6-day repeat cycle, the two satellites share the same orbital plane offset by a 180\({}^{\circ}\) phase difference. The S-1 microwave SAR instruments operate at a wavelength of approximately 5.5 cm. These satellites are equipped with identical microwave SAR instruments that operate four imaging modes: WV, strip map, extra-wide swath, and interferometric. WV is the primary imaging mode used over open ocean, unless there are specific requests for other imaging modes. S-1 WV small SAR image scenes are acquired in a leapfrog scheme at two alternating center incidence angles of 23\({}^{\circ}\) (WV1) and 36.5\({}^{\circ}\) (WV2). The small SAR image scenes are referred to as **imagettes** in this research. Each imagette has dimensions of 20 km \(\times\) 20 km and a spatial resolution of 5 m. Considering that certain horizontal (HH) images have been acquired during special phases, the two incidence angles frequently apply in linear vertical (VV) image transmission and polarization receipt. This study mainly focuses on S-1A WV data, considering that S-1B imagettes exhibit essentially equivalent characteristics to those of S-1A imagettes [48]. The joint utilization of S-1A and S-1B satellites will enhance the coverage in both temporal and spatial dimensions for various applications related to geophysical phenomena. Furthermore, the designed classification models can also be applied to S-1B.
# Towards Domain-Specific Knowledge Graph Construction for Flight Control Aided Maintenance
## 4 Knowledge Graph Construction for Flight Control System  
### Ontology Building  
The ontology development in our work is carried out in several rounds for corrections and optimization. In each round, the development is referred to the method proposed by [PERSON] et al. [16].  
Figure 3 summarizes the ontology we built including entity types and semantic relations. The whole flight control system includes five kinds of entities that are, respectively, _Equipment_, _Computer_, _System_, _Power_, and _Function_ (Figure 3a). The _Equipment_ class represents physical units used in the system, such as \"elevator\", \"rudder\", \"flap\", etc. The _Computer_ class includes computers, such as \"slat flap control computer\", \"elevator aileron computer\", \"sec\", etc., leveraged in flight control for calculations and issuing commands. The entity class _System_ represents different kinds of subsystems, such as \"centralized fault display system\", \"mechanical trim system\", and \"efcs\". The _Power_ class represents power sources, e.g., \"electrical motor\", \"hydraulic actuator\", et al. Finally, the _Function_ class represents various functions that can be achieved in the flight control system, e.g., \"turn coordination\", \"yaw damping\", \"aileron droop\", \"pitch control\", \"roll control\",  
Figure 2: An example of texts that are considered noise.  
\"yaw control\", etc. Based on the five entity classes, we define relations according to the semantic between different entities. Figure 3b leverages directed lines to depict all possible relations appeared between a pair of entity classes. Relations defined are, respectively, _consistOf, control, achieve, sendMsgTo, connectTo, locateOn, drive,_ and _acronym. ConsistOf_ expresses that one unit is composed of several other units. _Control_ represents one unit controls multiple other units; for example, a dedicated computer controls some mechanical components. _Achieve_ is defined to express one component achieving a kind of function. _SendMsgTo_ defines one component sending commands or messages to other components. _LocatedOn_ reflects on some kind of positional relationship. _Drive_ represents power sources. Finally, _acronym_ is used to express an entity's acronym.