esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Diversification Techniques for Legal Information Retrieval +
## 2 Legal Document Ranking Using Diversification
### Evaluation Metrics  
As the authors of [14] claim that \"there is no evaluation metric that seems to be universally accepted as the best for measuring the performance of algorithms that aim to obtain diverse rankings\", we have chosen to evaluate diversification methods using various metrics employed in TREC Diversity Tasks ([[http://trec.nist.gov/data/web10.html](http://trec.nist.gov/data/web10.html)]([http://trec.nist.gov/data/web10.html](http://trec.nist.gov/data/web10.html))). In particular, we report:  
* a-nDCG: The \(a\)-normalized discounted cumulative gain [15] metric quantifies the amount of unique aspects of the query q that are covered by the \(top-k\) ranked documents. We use \(a=0.5\), as typical in TREC evaluation.
* ERR-IA: Expected reciprocal rank-intent aware [16] is based on inter-dependent ranking. The contribution of each document is based on the relevance of documents ranked above it. The discount function is therefore not just dependent on the rank, but also on the relevance of previously ranked documents.
* S-recall: Subtopic-recall [17] is the number of unique aspects covered by the \(top-k\) results, divided by the total number of aspects. It measures the aspect coverage for a given result list at depth \(k\).
## 3 Results
### BM-MSC Expansion on PR PEF Supplemented Media  
BM-MSCs were cultured in DMEM supplemented with a selected PR PEF condition (PR PEF E and H), PL F&T, or FBS, the standard media condition used to expand these cells. Cells were cultured in these medium formulations for 6 days to understand the impact of the different medium supplements on BM-MSC growth features, namely adhesion, proliferation, and viability, as well as on the cell quality attributes including immunophenotype and multlineage differentiation potential.  
Our results showed that cell adhesion was not altered by culture in PR PEF E and H supplemented media, as adhesion levels were similar to FBS and PL F&T (Figure 3a). Moreover, no differences in cell proliferation were observed between cells expanded in PR PEF, and FBS or PL F&T. Of note, cell viability assays demonstrated that cells expanded in FBS, PR PEF E, and PL F&T exhibited the highest cell viability, but PR PEF H showed a significantly lower cell viability than FBS. Nevertheless, the lowest cell survival was observed for untreated PR (Figure 3c,d).  
Untreated PR supplement cells had a significantly lower proliferative capacity and viability than FBS, indicating that the GF release from platelets induced by PEF are essential to maintain BM-MSC proliferation (Figure 3b). Our data demonstrate that PR PEF supplemented media is an alternative to FBS, as cells maintain their proliferative and adhesion capacity, as well as viability.
# Green Hydrogen Storage in an Underground Cavern: A Case Study in Salt Diapir of Spain
## 3 Methodology and Results
### Operational Parameters
#### 3.2.2 Lithostatic or Overpressure  
Prior to opening the cavern, there are natural stresses in the ground that are assumed to be isotropic [14; 20], i.e., the horizontal pressure is equal to the vertical pressure.  
The average density of the rock is 2.30 g/cm\({}^{3}\) because most of the overburden is salt (density of 2.16 g/cm\({}^{3}\)), with a few meters near the surface consisting of gypsum, shale, and anhydrite. In addition, since this is a rock with such low permeability and porosity, hydrostatic pressure is not considered.  
As with the temperature, the pressure at the top of the cavern will be lower than that at its base. For the calculations, the lithostatic pressure at the center of the cavern is considered (Equation (5)).  
\[\mathrm{P}=\rho\times\mathrm{g}\times(\mathrm{z}-(\mathrm{H}/2)) \tag{5}\]  
This results in a lithostatic pressure of 19.6 \(\times\) 10\({}^{6}\) Pa, equivalent to 19.6 MPa (196 bar).
## 5 Significant Gaps in the Current Research  
### The Lack of a General Full-Body Biomechanical Model Supported by a Practical Number of Wearables  
Currently, the most reliable and practical full-body model for quantifying complicated human motor-skills consists of 15 segments: head, upper trunk, lower trunk, upper arms, lower arms, hands, thighs, shanks, and feet (Figure 1). This biomechanical model requires \(\sim\)40 inputs (i.e., 40 selected body points) for determining joints kinematics in order to reveal the motor control/limbs' coordination, and the model has been successfully applied in numerous 3D motion analyses of human virtuosity in sports and arts performance [69; 70; 71; 72; 73; 74; 75].  
Notwithstanding the above successes, the development of real-time biomechanical feedback is still in his infant phase. A brief look at the following facts may reveal one of the gaps in the development. (1) the concept of applying a general full-body model (i.e., 3D skeleton) in human movement study can be traced back to the early seminal research of [PERSON] in 1973 [76], which demonstrated that a major joint positions can effectively analyse human behaviors. (2) The tremendous success of the full-body model in 3D quantification of various human movements have been widely seen in the past decades [69; 70; 71; 72; 73; 74; 75]. (3) The limited applications of real-time biomechanical feedback in sports are still using special partial-body models that are related to rowing (limbs' accelerations) [77], shooting (stability of center of gravity) [78], bicycling (the foot angle profile) [79], and swimming (temporal stroke phase identification) [80]. The above facts might indicate that, for developing a universally applicable device, the first missing piece might be a general full-body biomechanical model supported by wearables.  
Obviously, it is wise to transfer the success of the general full-body model from 3D motion capture domain to wearables domain; nevertheless, it is nonrealistic and impractical to use so many wearables for rebuilding the full-body model. The question aroused is what is the minimum wearables required for the rebuilding? So far, there is a dearth in research on this issue. For reaching a general application of real-time biomechanical feedback in sports and arts performance, studies are needed to discover ways for reducing the number of the model inputs to a doable level in order to apply wearables in practice; and such a reduction should not trade off the current accuracy of 3D motion technology.
## 6 Possible Future Work  
Future extensions of this research include the integration of reconfigurable elements or active beamforming circuits to enable dynamic beam steering and real-time adaptability. Additionally, expanding the design into 2D planar arrays would facilitate full azimuth and elevation beam control, making the architecture suitable for advanced mm-wave systems. To enhance polarization diversity and robustness in multipath environments, dual-polarized or circularly polarized configurations can be explored. The antenna could also be deployed in massive MIMO testbeds to assess system-level performance in realistic 5G scenarios. Furthermore, implementation on flexible or conformal substrates may support emerging applications in wearable and vehicular platforms.  
In terms of radiation performance, the current design adopts uniform feeding for structural simplicity, resulting in a measured side lobe level of approximately \(-12\) dB. Future iterations could incorporate side lobe suppression techniques such as amplitude tapering, element shaping, and non-uniform spacing to achieve further reductions in side lobe levels, thereby improving beam purity and interference rejection.
# DNNs Based Computation Offloading for LEO Satellite Edge Computing
## 7 Discussion  
As can be seen from the above simulation results, our proposed algorithm can converge within a small number of training steps, and the time cost is very satisfactory because we can generate offloading decisions through multiple parallel DNNs, and therefore we can obtain the ideal convergence performance and time cost. Compared with other comparative algorithms, our proposed algorithm can obtain the optimal system utility function values because we not only generate near-optimal offloading decisions through DNNs, but also derive the optimal bandwidth allocation scheme, so we can obtain the optimal system utility function values.  
This discovery gives us a deeper understanding of the research on the MEC servers mounted on the LEO satellites, verifies the feasibility of the system by simulating the satellite-to-earth channel environment, and proves the excellent performance of the proposed algorithm through simulations. This research provides theoretical support for the application of edge computing on the LEO satellites in the future.  
Our future research direction is the application of edge computing in the satellite-ground collaborative network. With the advantages of wide coverage and strong computing power of the satellite-ground collaborative network, it can provide computing services for users anywhere on the earth, quickly respond to user's computing requests, and bring better QoE to users.
# Multi-Focus Image Fusion Based on Decision Map and Sparse Representation
## 1 Introduction
Then, the spatial frequency method was used to further divide each point in the uncertain region of the initial decision map into focused or defocused points, and the final decision map was determined. After obtaining the fused image based on the final decision map, the transitional area of the source images was detected according to the final decision map, and the area was processed by the multi-focus image fusion algorithm based on the sparse representation to obtain the transitional area fusion result. Finally, the fused image based on the final decision map and the transitional area fused image were averaged to obtain the final fused image. In order to verify the effectiveness of the proposed method, we performed a large number of experiments using two data sets based on the three target quality indicators. The experimental results show that our method is superior to the other five methods, both in terms of visual effect and quantitative evaluation. The remainder of this paper is organized as follows. Section 2 describes the specifics of our proposed method. The experimental results, a comparison with the state-of-the-art methods and objective evaluations are demonstrated in Section 3. Finally, Section 4 is the conclusion of this paper.
## 3 The Proposed Data Integration Approach for Agricultural Open Data Platforms
### Data Decomposition Engine (DDE)  
DDE handles decomposing the raw sensor data that are generally in varied forms. Sensors are low-cost sensing solutions for measuring environmental and site-specific data in agricultural applications. As sensors measure at least one value, they can measure more as well. Considering there are varied sensors plugged on router devices, the proposed approach recommends using key value pairs to define sensors on WSNs. Suppose that one router (id = 1) has a sensor (id = 1) that measures weather humidity (key = WH), weather temperature (key = WT) and barometric pressure (key = PR) at the same time. There can be different ways for composing such a data stream. However, the proposed approach solves this issue using the following pattern.  
\[\begin{array}{l}[\text{Router Identifier}]:[\text{Value}]:[\text{Sensor Identifier}]:[\text{Value}];\\ [\text{Sensor Measurement Identifier}]:[\text{Measurement Value}];\end{array} \tag{1}\]  
According to this pattern, sensor measurement value can be formulated as RID:1; SID:1; WH:45. This means that the value 45% of weather humidity is measured by the sensor numbered 1, which is plugged to router 1. Figure 2 illustrates how to decompose sensor raw data stream. DDE decomposes the raw sensor data and extracts router definitions, sensor definitions and sensors' measurement values from this data.  
DDE is also responsible for matching the sensor measurement value to its unit using sensor information stored in the databases or any files and mapping sensors to their measurement value for creating appropriate object models of routers and sensors to utilize by visualization application tools. To meet mapping requirements, key value pairs of routers and their sensors can be passed as parameters through the queries. When sensor raw data stream is received by WSN coordinators, recognized router and sensor id are retrieved from databases or files. Then, mapping procedures are accomplished by data decomposition engine.
# Signal Detection Based on Separable CNN for OTFS Communication Systems
## 2 Related Work
### Model-Driven Detection Methods  
Model-driven techniques include linear detectors, nonlinear iterative detectors, and Turbo-aided structures.  
Among the linear approaches, MMSE and zero-forcing (MMSE-ZF) [12] and linear MMSE [13] are widely used due to their low complexity and straightforward implementation. These methods operate in the DD domain and offer initial interference mitigation. MMSE-ZF provides simple equalization, making it suitable for low-mobility scenarios, whereas LMMSE achieves a better performance-complexity trade-off. However, both suffer considerable performance degradation under high Doppler spreads or fast-fading channels.  
To enhance detection accuracy, a variety of nonlinear iterative detection algorithms have been proposed. Maximum a posteriori (MAP)-parallel interference cancellation (PIC) [14], message passing (MP) [15], GAMP [7], unitary approximate message passing (UAMP) [16], parallel message passing (PMP) [17], variational Bayesian (VB) [18], and EP [8; 9] algorithms introduce Bayesian inference and iterative updates to suppress interference and estimate transmitted symbols more precisely. Some of these methods also integrate domain information or alternate subspace structures for improved robustness. These algorithms often outperform linear detectors, but at the cost of increased computational complexity.  
Turbo detection architectures further improve performance by incorporating channel decoding into the iterative process. Turbo maximal ratio combining (MRC) [19] exploits mutual information between detection and decoding stages to refine symbol estimation. Methods like sliding window LMMSE [20] and doubly-iterative sparsified minimum mean square error (DI-S-MMSE) [21] have also been introduced to reduce latency and integrate deep learning enhancements into the iterative MMSE framework. While turbo-aided detection delivers superior bit error rate (BER) performance, it typically incurs significant processing overhead and implementation complexity.
###### Abstract  
Recently, the application of digital cable transmission standards as a second generation digital transmission system for cable systems (DVB-C2) in addition to MPEG-2 video compression has offered ways to provide a variety of digital high definition television (HDTV) programs. In the DVB-C2 system, the use of an orthogonal frequency division multiplexing (OFDM) modulation enables the delivery of innovative video-on-demand (VOD) and HDTV services. In this paper, a computationally-efficienteast-squares estimation of carrier frequency offset and sampling frequency offset is proposed in the OFDM-based DVB-C2 system. To obtain the noise averaging effect in the synchronization process, we adopt a block-by-block correlation, where observation symbols within two correlation blocks are added and the result is correlated. The use of block-by-block correlation improves the estimation performance and relieves the computational overhead. To optimize the performance of the block-by-block estimation method, the mean squared error is theoretically calculated. It is confirmed from simulation results that the performance of the proposed method is enhanced with a low complexity, in comparison with the existing methods.  
orthogonal frequency division multiplexing (OFDM); second generation digital transmission system for cable systems (DVB-C2); least-squares estimation; frequency offset +
Footnote †: journal: _Journal of the Royal Society Interface_
## 2 Preliminaries
### Related Work  
A rotation-based protocol is a protocol for which most of the operations performed on the parties involved in the protocol are rotation operations, combined with other ultra-lightweight operations, e.g., bitwise operations such as AND, OR and XOR, and no cryptographic primitives are used in them.  
Designing an RFID authentication protocol based on rotation function began with the SASI protocol [12]. However, soon after there were attacks such as [16; 17; 18; 19] that revealed that the protocol was not safe against various attacks. After that, [PERSON] et al. tried to improve the disadvantages of SASI protocol to provide resistance against traceability and de-synchronization attacks, which led to proposing the Gossamer protocol [13]. However, it has been shown in [20] that the Gossamer protocol is vulnerable against denial of service, de-synchronization attack, and replay attacks. [PERSON] and [PERSON] in [14], following the method used by previous protocols, proposed another rotation based protocol. This time, the reports such as [21; 22] were released on the vulnerability of this protocol against various attacks. Another example is ULRMAPC protocol [15] which [23] proved its vulnerability against DoS, impersonation and de-synchronization attacks.  
Recently, in this regard, an ultra-lightweight authentication protocol named ULRAS was proposed by [PERSON] et. al. [24]. The designers of ULRAS have claimed that because of using a special rotation operation, called the RR method, and dividing the protocol secret key into four sub-keys, to update the secret key, their protocol provides forward security and resists against the known active and passive attacks, e.g., de-synchronization (DoS) attack. However, [PERSON] and [PERSON] in [25], presented reader impersonation attack and secret disclosure attack against the ULRAS protocol and then proposed a new improved protocol.  
In this paper, we will present in more depth security analysis of ULRAS protocol [24] and its improvement, proposed by [PERSON] and [PERSON] [25], and show that, same as their predecessors, they are also vulnerable.  
The long history of rotation function based protocol's vulnerabilities and also the current analysis have shown that designing an ultra-lightweight protocol which satisfies all desired security targetsmay not be feasible. On the other hand, recent advances in symmetric cryptography provided many secure primitives that could be implemented in a constrained environment such as passive RFID tags. For example, implementation of SIMON96/96 [26], which provides 96 bits security and its block length is also 96 bits, only needs 955 NAND gates equivalent (GE). Hence, we suggest employing such cryptographically-sound primitives in designing a protocol rather than attempting to design a secure ultra-lightweight protocol.
## 6 Results and Discussion
### Model Validation  
The continuous casting-hot rolling model proposed in this paper considers the constraints of continuous casters, reheating furnace, and hot rolling mills. It not only makes the model more relevant to the actual production but also reduces unnecessary energy waste and increases the profit of the enterprise. To verify the validity of the model, this section compares the established mixed model with minimum completion time scheduling and minimum energy waste models under mixed batch types. The total number of hot-rolled batches is 12. Among them, batches 1, 3, 4, 6, 10, and 12 are the DHCR type, and batches 5, 7, 8, and 9 are the HCR type. Batches 11 and 12 are the CCR type, and the casting schedule corresponding to batch 11 is normally processed in the continuous casting machine, but its rolling schedule is in the next rolling cycle, so its hot rolling schedule is set to 0. The casting schedule corresponding to batch 12 is in the previous casting cycle, and the rolling schedule is in this rolling cycle, so its casting schedule is set to 0. Table 4 lists the castings included in each batch. Based on the discussion of the reheating furnace conversion model in Section 3, the energy waste matrix between these 12 batches can be derived as shown in Table 5. For example, the first row and second column matrix L\({}_{12}\) = 28.3, which means that the amount of gas wasted during the conversion of batch 1 and batch 2 in the reheating process is 28.3 GJ. The Gantt charts, which aim to minimize completion time, minimize energy waste, and minimize mixed models, are shown in Figures 5-7. _CC1_ and _CC2_ represent two continuous casting machines, and the numbers _CI_ to _C24_ on the continuous casting machine correspond to the casting number. The color of each casting is the same as the color of the batch it belongs to. If a casting is shown as a mixed color, it means that the casting belongs to multiple different batches. Meanwhile, the two continuous casting machines correspond to one hot rolling line _HRM_, and the numbers \(H\)1 to \(H\)12 represent the batch number. There are three reheating furnaces between the continuous casting machine and the hot rolling machine. The heating sequence is the same as the batch rolling sequence, so it is not listed \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Batches** & **Batch** & **Corresponding Castings** & **Batches** & **Batch Types** & **Corresponding Castings** \\ \hline
1 & DHCR & \(3,4\) & \(7\) & HCR & \(14,17,19\) \\
2 & DHCR & \(1,2\) & \(8\) & HCR & \(13,14\) \\
3 & DHCR & \(5,6\) & \(9\) & HCR & \(15,16,17,22\) \\
4 & DHCR & \(7,8\) & \(10\) & DHCR & \(9,10\) \\
5 & HCR & \(16,18,20,21\) & \(11\) & CCR & \(18,23,24\) \\
6 & DHCR & \(11,12\) & \(12\) & CCR & \(0\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Castings schedule included in mixed batch types.
# Chinese Fine-Grained Named Entity Recognition Based on BILTAR and GlobalPointer Modules
## 4 Experiment
### Ablation Experiments
#### 4.5.6 Ablation Experiments of the BILTAR Module  
To demonstrate the effectiveness of the BILTAR module, this study conducted five comparative experiments by adding the BILTAR module to the experimental model.  
In the comparison between BERT-ATT-BILTAR-GlobalPointer and BERT-ATT-GlobalPointer, the \(F1\) value of the model increased by 1.987% on the CLUENER2020 dataset and by 4.635% on the Weibo dataset.  
In the comparison between BERT-BILTAR-GlobalPointer and BERT-GlobalPointer, the \(F1\) value of the model increased by 0.148% on the CLUENER2020 dataset but did not improve on the Weibo dataset.  
In the comparison between BERT-BILTAR-CRF and BERT-CRF, the \(F1\) value of the model increased by 0.678% on the CLUENER2020 dataset but did not improve on the Weibo dataset.  
In the comparison between BERT-ATT-BILTAR-CRF and BERT-ATT-CRF, the \(F1\) value of the model increased by 0.530% on the CLUENER2020 dataset but did not improve on the Weibo dataset.  
Adding the BILTAR module to the model combinations did not have a significant effect on the model performance with CRF on the Weibo dataset. However, adding the BILTAR module to the BERT-ATT-GlobalPointer model improved the \(F1\) value of the model. Therefore, the BILTAR module can extract deeper semantic information from text sequences. Compared with the CRF, the BILTAR module is more suitable for the GlobalPointer module.  
Compared to CRF modules, it is better to add a GlobalPointer module after the BILTAR module under the same conditions.  
Through these four module combination experiments, it is shown that the BILTAR module can extract the deep features of the text sequence. In the BILTAR module, BILSTM can extract the connections between words, the PN module processes the forward and backward information of the sequence, respectively, the attention mechanism denoises the data, the TIME module encoder extracts the deep semantic features of the words, the residual summation method efficiently fuses the forward and backward information of the sequence, and the fused information is the deep semantic information of the sequence. Finally, the effect of the model is improved by the various submodules in the experimental results.