esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Throughput of Mobile Sensors via Certificateless Signature Supporting Batch Verification
###### Abstract  
Mobile sensors enjoy the advantages of easy installation and low consumption, which have been widely adopted in many information systems. In those systems where data are generated rapidly, the throughput of the sensors is one of the most fundamental factors that determine the system functionality. For example, to guarantee data integrity, digital signature techniques can be applied. In many practical scenarios, such as the smart grid system, data are generated rapidly and, hence, the signature together with the data must also be transmitted and verified in time. This requires the mobile sensors to support a high-throughput data processing ability. In this setting, how to achieve efficient signature schemes supporting batch verification must be considered. Many signatures, such as the original national cryptographic standard, namely, the SM2 algorithm, do not support batch verification and are in a public-key infrastructure setting. In this paper, we propose a SM2-based certificateless signature scheme with batch verification, which is suitable for the aforementioned environment. The scheme extends the Chinese cryptographic standard SM2 algorithm to the certificateless setting and multiple signatures can be verified simultaneously. Another advantage of this scheme is that its signing phase does not involve any pairing operation. The verification phase only requires a constant pairing operation, which is not related to the number of signatures to be verified. The construction is generic and can be instantiated using any traditional signature scheme.  
mobile sensor; data integrity; certificateless signature; batch verification; pairing free +
Footnote †: journal: Electronics Electronics
# Blockchain-Embedded Strategic Options for Satellite Operators: Sell, SataS, or Dual?
## 3 Models excluding Blockchain Technology
### ND Model: Dual Channel without blockchain
As the launch success rate rises, users will move to the sell market when the utility of launch success outweighs the utility of lost data security. Furthermore, in order to derive the optimal strategy, this paper compares the profits of the dual-channel strategy with those of the sell and SataaS channels, respectively. From the analysis, we obtain Proposition 1, and the comparison results are summarized in Table 5.  
**Proposition 1**. : _When \(\frac{c_{r}}{s}\leq e\leq 1+c_{r}-s\), the dual channel exists:_  
1. _When_ \(\frac{c_{r}}{s}\leq e\leq 1+c_{r}-s\)_, the dual channel exists:_ 1. _When_ \(\frac{c_{r}}{s}\leq e\leq 1+c_{r}-  
Proposition 1 sheds light on the conditions for choosing the dual-channel strategy. Proposition 1(a) shows that compared with the pure SataaS strategy, the SO will always obtain a higher profit with the dual-channel strategy. Plus, consumers also benefit more with this strategy. Proposition 1(b) indicates that compared with the pure sell strategy, the important factor for choosing a channel is the fixed cost of SataaS. That is, if \(C\) is small, then the dual-channel strategy is better for the SO. Otherwise, the pure sell channel will generate a higher profit. For customers, the consumer surplus of the dual-channel strategy is always higher than that of the pure sell channel. In conclusion, if the dual-channel strategy is possible, it is always the best strategy for consumers. This is because dual channels provide two service options, allowing more consumer needs to be met. For the SO, the condition for the choice of the dual-channel strategy is that \(C\) is relatively small.
# On the Contribution of Wind Farms in Automatic Generation Control: Review and New Control Approach
## 4 Proposed Wind Farm Control Technique in AGC
### Design Procedure of SCA-Based FOPID Controller
#### 4.2.3 Sine-Cosine Algorithm  
Sine-cosine Algorithm (SCA) is a new population-based optimization approach for solving optimization problems [71]. The optimization process in SCA is based-on a set of random solutions that applies a sine and cosine functions based-on a mathematical model to fluctuate outwards or towards the best solution. The SCA has two phases known as exploration and exploitation in the optimization process. To establish exploration and exploitation of the search space to rapid achieve the optimal solution, this algorithm uses many random and adaptive variables. The position updating equation related to any search agent \(X_{i}\) can be written as follows:  
\[X_{i}^{t+1}\ =\ \left\{\begin{array}{ll}X_{i}^{t}+r_{1}\times\sin\left(r_{2} \right)\times\left|r_{3}P_{i}^{t}-X_{i}^{t}\right|&r_{4}<0.5\\ X_{i}^{t}+r_{1}\times\cos\left(r_{2}\right)\times\left|r_{3}P_{i}^{t}-X_{i}^{t }\right|&r_{4}\geq 0.5\end{array}\right.\ r_{1}\ =c\ -\ t\ \frac{c}{T}\, \tag{9}\]  
\[r_{1}\ =c\ -\ t\ \frac{c}{T}\, \tag{10}\]  
Figure 4: The general structure of the proposed fractional order proportional-integral-differential (FOPID) controller.  
where \(X_{i}^{t}\) and \(P_{i}^{t}\) are the position of the current solution and the position of the best solution in \(j\)-th dimension at the \(t\)-th iteration, respectively. Furthermore, \(T\) is the maximum number of iterations, and \(c\) is a constant value. \(r_{1},r_{2},r_{3}\) and \(r_{4}\) are random numbers. \(r_{1}\) is a control parameter that states the next position, which could be either in the space between the solution and destination or outside; \(r_{2}\) expresses how far the movement should be towards or outwards the destination. \(r_{3}\) is a random weighting parameter for emphasizing (\(r_{3}>1\)) or deemphasizing (\(r_{3}<1\)) the effect of destination to define the distance; and, \(r_{4}\) is a switching parameter, which switches between the sine and cosine components, equally.
## 5 Experimental Results and Discussion and Analysis
### Error Analysis  
Although DAPONet performs well in terms of detection performance and computational efficiency, surpassing many mainstream object detection models in terms of the balance of accuracy and speed, it still has some shortcomings. Firstly, the model's leakage and false detection problems are more prominent in small objects (e.g., potholes and cracks) and complex backgrounds, especially in pothole detection, where the model is difficult to effectively distinguish similar regions between potholes and backgrounds, resulting in lower detection accuracy. In addition, although DAPONet's multi-scale feature extraction and efficient downsampling modules (CPDA and MCD) improve the precision and efficiency, the ability to differentiate between the background and the target still needs to be strengthened in some complex scenarios. The analysis of the F1-Confidence curve shows that there is a challenge in balancing the model with different confidence levels, and how to improve the recall rate while guaranteeing the high precision is still the the focus of future optimization. Meanwhile, although the computational consumption of DAPONet has increased compared with other models with lower precision, how to further optimize the computational efficiency on resource-constrained devices is still a problem to be solved.  
To address the challenges identified in the error analysis, we propose several concrete strategies for improving the detection of small and background-similar objects, such as the class-weighted loss function. To enhance detection of small objects like potholes, we propose applying a class-weighted loss function, giving higher importance to the pothole class during training. This helps the model focus more on correctly identifying and localizing smaller objects. Transfer learning from models trained on large-scale datasets like MS COCO or ImageNet can help DAPONet learn more generalized features, improving its ability to recognize diverse types of damage. Fine-tuning a pre-trained model specifically for road damage detection allows DAPONet to leverage existing knowledge from a broader dataset while adapting to the specific characteristics of road distress. Pruning the model (removing less significant weights and parameters) and applying techniques like quantization can reduce the size and computational demands of DAPONet without significantly compromising performance. This can make the model more suitable for deployment on resource-constrained devices.
# An Efficient VQ Codebook Search Algorithm Applied to AMR-WB Speech Coding
## 3 BSS-VQ Algorithm for ISF Quantization  
The basis of the BSS-VQ algorithm is that an input vector is efficiently assigned to a subspace where a small number of codeword searches is carried out using a combination of a fast locating technique and lookup tables, as a prerequisite of a VQ codebook search. In this manner, a significant computational load reduction can be achieved.  
At the start of this algorithm, each dimension is dichotomized into two subspaces, and an input vector is then assigned to a corresponding subspace according to the entries of the input vector. This idea is illustrated in the following example. There are \(2^{9}=512\) subspaces for a 9-dimensional subvector \(\mathbf{r}_{1}(n)\) associated with codebook CB1, and an input vector can then be assigned to one of the 512 subspaces by means of a dichotomy according to each entry of the input vector. Finally, VQ encoding is performed using a prebuilt lookup table containing the statistical information on sought codewords.  
In this proposal, the lookup table in each subspace is pre-built in a way that requires lots of data for training purposes. A training as well as an encoding procedure in BSS-VQ is illustrated with the example of a 9-dimensional codebook CB1 with 256 entries in AMR-WB.
# Towards a Collection of Security and Privacy Patterns
## 3 Security Patterns
### Authentication
#### 3.6.5 Blacklist  
A Blacklist (Figure 18) is used to keep track of network addresses from where hacking attempts and other malicious actions have originated from. Requests from an address on the blacklist are ignored.  
Such blocking mechanisms can be deployed at the network and at the application (endpoint) level.  
Blocking at the network level: Normally, a client with an Internet Protocol address (IP) that is not on the blacklist will make a request to the server and the blocking mechanism will let the request pass. The server will respond with normal functionality. However, a suspicious request from a non-blacklisted client will cause the server to keep a record of the network address and the nature of the request. If the request is sufficiently suspicious, the server will request that the address be blacklisted. The blocking mechanism will then be configured to deny further requests from that address. Moreover, when a blacklisted client makes a request, the blocking mechanism will not just drop the request but also make a log of it. A blacklisted address may be removed from the blacklist after a period of inactivity or because of manual administrator intervention.  
Blocking mechanism within the application: Similar to the above case, when an application request is received (e.g., access to a specific exposed interface), it is first checked against the blacklist before being dispatched to the appropriate interface.  
If the Blacklist pattern is implemented effectively, it enhances the system's security level as it will dissuade or prevent attempts to misuse the system.
## 5 Conclusions  
In this study, we carried out a comprehensive investigation on multi-parameter ionospheric disturbances possibly associated with two major earthquake events in Afghanistan that occurred in 2015 and 2023, with the use of GIM, GPS, Swarm, and ionosonde data; the following phenomena and findings were obtained. * It was detected that GIM-TEC anomalies for the October 2015 Mw 7.5 earthquake started appearing from 26 days before the shock to 1 day before the shock, while for the 2023 Mw 6.3 earthquake, the Swarm anomalies could be noticed even 2 h before the earthquake happened. * After stringently examining the common anomaly days, it was found that all possible ionospheric parameters for the 2015 earthquake were 30-09, 03-10, 15-10, 17-10, 18-10, 19-10, 20-10, 24-10, and 25-10, and for the 2023 earthquake, they were 19-09, 21-09, 24-09, 26-09, 29-09, 30-09, 02-10, 06-10, 07-10, 10-10, 11-10, 13-10, 14-10, and 15-10. * Spatiotemporal coincidence was well established during the multi-parameter analysis, which confirms that the revealed ionospheric anomalies are important precursors of earthquakes. * The data validations from various sources for anomalous days also confirm the true pre-seismic activities induced in the ionosphere. To summarize, this approach for ionospheric sounding is a promising method for detecting pre-seismic activity and perhaps increasing earthquake forecasting capabilities. However, it is vital to emphasize that further research on and refining of this technique are still required to properly grasp the complex relationship between ionospheric variability and earthquakes. The study focused only on single-sphere (ionosphere) disturbances prior to earthquakes, but anomalies associated with LAI coupling are also crucial to gain a complete understanding of earthquake preparation and, hence, prediction. The observed anomalies in TEC, Ne, and Te and foF2 point to a potential link between the lithosphere and ionosphere, implying that seismic activity can influence ionospheric properties. The findings lend support to the concept that the ionosphere exhibits precursory behavior before large earthquakes, highlighting its potential as a source for earthquake forecasting. Studying ionospheric disturbances preceding earthquakes holds promise for earthquake monitoring and prediction efforts. However, it is important to note that the relationship between the ionosphere and earthquakes is complex, and not all ionospheric disturbances necessarily indicate an impending earthquake. To efficiently investigate the seismic-derived disturbances, the LCAI model needs to be evaluated, which could be carried out in the future. Further research is needed to deepen our understanding of the underlying mechanisms, as well as establish reliable methodologies for earthquake forecasting and early-warning systems using advanced detection systems.
# The Role of Environmental Assumptions in Shaping Requirements Technical Debt
## 2 Background and Related Work
### Requirements Technical Debt
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Aspect** & **Requirement Phase** & **Implementation Phase** \\ \hline Origin & Incomplete, vague, or misunderstood requirements; missing or incorrect assumptions; neglecting user needs; failure to capture user feedback. & Poor coding practices; shortcuts; lack of refactoring, testing, architecture planning; outdated dependencies, insufficient modularization \\ \hline Impact on Development & Incomplete functionality, safety risks, rework, misalignment with business goals, or large-scale redesign. & Poor code quality, increased maintenance, localized bugs, and higher refactoring costs. \\ \hline Visibility & Often unnoticed until late-stage testing or post-deployment typically emerges during integration. & Usually detected during late development, testing, or feature addition. \\ \hline Cost of Resolution & High as it may require redesign, extensive rework, or addressing regulatory compliance. & Moderate, as it typically resolved through targeted refactoring or added tests. \\ \hline Risk Level & Strategic risk (e.g., functional failure, safety, compliance issues). & Technical or operational risk (e.g., performance degradation, maintainability issues). \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Key differences between technical debt in the requirements phase and the implementation phase [1; 2; 3; 7; 38; 39]. [PERSON] et al. [6] present a systematic literature review that investigates the root causes of RTD, current mitigation approaches, and techniques for measuring it. [PERSON] et al. [38] frame RTD as the outcome of decisions during requirements engineering that fall short of optimality. Their study surveys the existing body of RTD research, identifies knowledge gaps, and introduces the RTD Quantification Model [7], which evaluates RTD in terms of its cost implications, required effort, and potential system impact. While previous studies have broadened our understanding of RTD, including our prior work [9], the role of environmental assumptions in shaping RTD remains largely unexplored. This paper extends that initial investigation by systematically analyzing how such assumptions contribute to the emergence of RTD and offering a classification framework based on their impact.
# Scottish Passive House: Insights into Environmental Conditions in Monitored Passive Houses
## 5 Discussion
### Ventilation  
The study found inconsistencies in the capacity of the MVHR units; unbalanced supply and extract rates that were greater than the 10% allowance permissible by Passive House [19], insufficient boost flow rates; and insufficient rates for individual room demands. These inconsistencies impact the ventilation provision, heat loss, and building performance. The positive pressure created in the Site A dwellings provides potential for interstitial condensation and additional heat loss. While increased airtightness necessitates the requirement for MVHR (or controlled ventilation) to avoid poor IAQ, a study by [PERSON] _et al._ demonstrated that a dwellings ventilation provision may not be entirely met by MVHR air delivery rates; and that external factors, design, and occupant use, all affect the efficacy of natural ventilation [43].  
There was a general lack of understanding from occupants as to when to use the MVHR, resulting in non-optimal use such as: Turning on/off, which affects the background ventilation. This understanding is exacerbated by the fact that MVHR also provides heat, not just recovered heat. It is therefore hard for the occupant to know whether to turn the system up or not if they want more warmth. A lack of understanding can impact the perceptions of the occupants as to the systems in their homes. If they are perceived to be complicated or expensive to run, then people will not engage with them, potentially effecting comfort.  
Some natural ventilation is good for the building and IAQ, however, prolonged window opening, especially in the heating seasons will impact on the thermal balance of the building, particularly during winter and at certain times of day during the summer. The study found all of the households were opening windows to a certain extent to provide cooling (and consequently heat loss in winter). In some households, e.g., DA2, window opening was habitual rather than just for cooling (Figure 9). However, the dwellings in Site A would have benefited from a high level opening in the first floor hallway to enable stack-ventilation for summer heat rejection. There is a degree of uncertainty about \"mixed mode\" operation for occupants; _i.e._, whether systems can be turned off and when; and whether windows can be opened, and the impact of this on energy efficiency. These should be clarified to the occupants as part of the handover process.  
There were issues common to MVHR, including: system imbalances, uncertainty over maintenance responsibility, inadequate filter cleaning and replacement, lack of occupant understanding and installation defects [14; 59; 60] all of which affect the system performance. The homes in this study are all affordable homes where if they do not perform as intended there could be health and financial consequences for the occupants; this may impact on their disposable income, family spending, and the local economy.
## 4 Theoretical Study of the Optimal Control Model Problem
### Existence of an Optimal Control  
**Theorem 3**.: _Suppose that \(J\) is the objective functional as given in (2) and defined on the non-empty Lebesgue measurable control set \(U\), and subject to (1) such that the initial conditions, at \(t=0\), are non-negative. Therefore, there exists \((u_{1}^{*},u_{2}^{*},u_{3}^{*},u_{4}^{*})\), an optimal control quadruple, denoted by \(u^{*}\) for which_  
\[J(u_{1}^{*},u_{2}^{*},u_{3}^{*},u_{4}^{*})=min\{(J(u_{j})):u_{j}\in U,\,j=1,2,3,4)\}.\]  
Suppose that \(x=(S,D,C,L,L_{d},L_{c},A,A_{d},A_{c})\), \(U=[0,1]^{4}\) is the Lebesgue measurable admissible control set, \(\
u=(u_{1},u_{2},u_{3},u_{4})\), and \(g(t,x,\
u)\) denotes the right-hand side of (1) given by  
\[g(t,x,\
u)=\left(\begin{array}{c}\Lambda-((1-u_{2})\lambda_{T}+\mu+(1-u_{2}) \sigma_{1})S+u_{3}A+\frac{u_{1}L}{1+q_{d}L}\\ \sigma_{1}(1-u_{2})S-(\phi(1-u_{2})\lambda_{T}+\mu+(1-u_{2})\sigma_{2})D+u_{4} A_{d}+\frac{u_{1}L_{d}}{1+q_{d}L_{d}}\\ \sigma_{2}(1-u_{2})D-(\mu+\varphi(1-u_{2})\lambda_{T}+d_{c})C+u_{4}A_{c}+\frac {u_{1}L_{c}}{1+q_{d}L_{c}}\\ (1-u_{2})\lambda_{T}S-(\alpha_{1}(1-u_{2})\lambda_{T}+r_{1}+(1-u_{2}(t))\tau_{ 1}+\mu)L-\frac{u_{1}L_{d}}{1+q_{d}L}\\ \phi(1-u_{2})\lambda_{T}D+(1-u_{2}(t))\tau_{1}L-(\alpha_{2}(1-u_{2})\lambda_{T }+r_{2}+(1-u_{2})\tau_{2}+\mu)L_{d}-\frac{u_{1}L_{d}}{1+q_{d}L_{c}}\\ \varphi(1-u_{2})\lambda_{T}C+(1-u_{2})\tau_{2}L_{d}-(\mu+\alpha_{3}(1-u_{2}) \lambda_{T}+r_{3}+d_{1})L_{c}-\frac{u_{1}L_{c}}{1+q_{d}L_{c}}\\ (\alpha_{1}(1-u_{2})\lambda_{T}+r_{1})L-(\mu+\delta_{1}+u_{3}+\gamma_{1}(1-u_ {2})\sigma_{1})A\\ (\alpha_{2}(1-u_{2})\lambda_{T}+r_{2})L_{d}+\gamma_{1}(1-u_{2})\sigma_{1}A-( \mu+\delta_{2}+u_{4}+\gamma_{2}(1-u_{2})\sigma_{2})A_{d}\\ (\alpha_{3}(1-u_{2})\lambda_{T}+r_{3})L_{c}+\gamma_{2}(1-u_{2})\sigma_{2}A_{d }-(\mu+u_{4}+\delta_{3}+d_{2})A_{c}\end{array}\right). \tag{5}\]  
Let \(f(t,x,u_{j})\) for \(j=1,2,3,4\) be the integrand of \(J\), the objective functional, mentioned in (2). By Theorem 2.2 in [46], it is sufficient to prove the following properties to ensure that an optimal control exists:  
(I) The permissible control set, \(U\), is closed and convex.  
(II) The boundedness of the state system by a linear function in both the control and state variables.  
(III) For each \(j=1,2,3,4\), \(f(t,x,u_{j})\) is convex in \(u_{j}\).  
(IV) There exist positive constants \(\Psi_{1},\Psi_{2}\) and \(\Psi_{3}>1\) such that the Lagrangian is bounded below by  
\[\Psi_{1}(\left|u_{j}\right|)^{\frac{\Psi_{3}}{2}}-\Psi_{2},\,j=1,2,3,4.\]  
We establish properties (I)-(IV) as follows:  
Proof.: See Appendix B.
## 5 Discussion
### Meta Evaluator as a Control Mechanism: Strengths and Limitations  
A distinctive contribution of this research is the meta evaluator (Meta_auto), an automated system meant to assess agent outputs in parallel with expert-supervised scoring (Meta_human). Our results show that, while Meta_auto usefully flags questionable outputs, it can also exhibit systematic biases--occasionally overscoring the fine-tuned LLM or underscoring VectorGraphRAG explanations.  
From a workflow perspective, a meta evaluator serves as an early warning system, revealing where an agent's sub-scores or explanations depart from expected norms. Educators can then focus on flagged items rather than manually re-checking every question. However, the meta evaluator itself requires careful prompt-engineering and tuning; routine human oversight remains essential, especially in high-stakes or conceptually complex scenarios.