esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
The method presented here offers a different approach for tailoring the motion range in CCAJs, and it is based on the use of lattice metamaterials as FHs. Here, we used previously studied honeycomb lattices (hexagonal, re-entrant, and square), and their stiffness was modified by varying some of the parameters that define them. These parameters were the unit cell dimension and the lattice orientation with respect to the principal axis of the LFHs. The size of the unit cell was modified to control the volume fraction of the LFH. To fully exploit the use of lattices and cellular metamaterials in tailoring stiffness applications, one needs to thoroughly understand their structure-property relationship. Bending-dominated lattices have a non-linear relationship between their effective mechanical properties and their volume fraction. On the contrary, stretch-dominated structures have a linear volume fraction-property relation. The unit cell orientation of the LFHs was also modified as another approach to tailoring the stiffness. Some 2D lattices have mechanical properties that are sensitive to the loading direction. Deformation mechanisms in these can change from stretch- to bending-dominated depending on the loading direction. This is attributed to their rotational symmetry; struts conforming to the lattices can be arranged from being aligned to the loading direction to a certain inclined angle. Both parameters, topology and orientation, impact the resulting stiffness. Even if the stiffest LFHs are used, i.e., the square lattice honeycomb with a 3.5 mm unit cell size and orientation of 0\({}^{\circ}\), the resulting stiffness is reduced when compared with fully solid hinges. Hence, an increment in the motion range was obtained. As a result, the solid hinges showed a limited motion range of 0.14 mm against the motion range of the stiffest LFHs, which was 0.29 mm. The results presented here reveal the reductions in stiffness that this method can achieve. We are aware that the options for lattice arrangements are vast; nevertheless, the results presented can be treated as an exploratory study that opens the possibility of further research. Other topologies could be explored to achieve different results. Combinations of topologies or functionally graded lattices could also be studied to reduce stiffness by altering the material distribution differently, depending on the type of application. When changing the lattice orientation with respect to the principal axis of the LFHs, some of the arrangements lose their symmetry. This, in turn, results in unavoidable twisting displacements, even when the CJ should respond by macro-bending. This could be seen as a defect analogous to what is obtained in parallel mechanisms with parasitic motions. These could not be considered extra degrees of freedom because they are dependent on the
# Remote Crop Mapping at Scale: Using Satellite Imagery and UAV-Acquired Data as Ground Truth
## 2 Materials and Methods
### Ground-Truth Data
For this pilot study, we limited the number of classes to four strategic crops (accounting for approximately 73% of the total cropped area in Season A), a \"catch-all\" class for other crops and grasslands, a class for trees and woodlands, and a catch-all class for non-vegetative land covers. The classes are as follows:  
1. Maize;
2. Beans (bush beans and climbing beans);
3. Cassava;
4. Bananas (all varieties);
5. Other vegetation (OtherVeg) representing grassland and crops not in class 1 through 4;
6. Trees representing small tree stands, forest, and woodlands;
7. Non-vegetative (NonVeg) land cover representing bare ground, buildings, structures, and roads. Figure 2 shows examples of labeled sections of UAV images for each of the selected classes. Each example corresponds to an area of 10 \(\times\) 10 m on the ground. The number of ground-truth examples for each of the six UAV flight sites is detailed in Table 2. Emphasis was placed on labeling points from the Kaberege, Kinyaga, Kabarama, and Cyampirita sites for which cloud-free satellite images were available during the peak of the growing season; the Ngarama and Rwakigarati sites were heavily clouded then. Within the cloud-free sites, we tried to balance the number of labeled examples. In total, we created 1251 labeled data points. These data points were originally labeled by researchers who were trained by Mr. [PERSON] who is a Rwandan agricultural expert and a co-author of this study. Mr. [PERSON] also accompanied the UAV operators to the test sites on several occasions and reviewed the labeled cells for accuracy. Furthermore, we cross-referenced the labeled cells between the December and January flight dates to ensure that the ground cover designation was consistent (note: in the February flights, it was apparent that harvest had already occurred in many areas, and we did not rely on these data for labeling). Cells not meeting this criterion were eliminated. Cells were also denoted as good, average, or poor examples of ground cover as related to the health of the vegetation, and cells labeled as poor were eliminated. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Class** & **Kbg** & **Kin** & **Kbm** & **Cym** & **Nga** & **Rwa** & **Total** \\ \hline Maize & 130 & 37 & 11 & 101 & 8 & 2 & 289 \\ Beans & 33 & 8 & 69 & 20 & 7 & 7 & 144 \\ Cassava & 0 & 17 & 53 & 47 & 6 & 12 & 135 \\ Bananas & 4 & 12 & 42 & 14 & 9 & 4 & 85 \\ OtherVeg & 12 & 46 & 29 & 47 & 0 & 9 & 143 \\ Trees & 105 & 30 & 56 & 104 & 0 & 0 & 295 \\ NonVeg & 37 & 38 & 29 & 26 & 5 & 25 & 160 \\ Total & 321 & 188 & 289 & 359 & 35 & 59 & 1251 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The number of ground-truth data points by class by site (Kbg = Kaberege, Kin = Kinyaga, Kbm = Kabarama, Cym = Cyampirita, Nga = Ngarama, Rwa = Rwakigarati).
# Stochastic Model Driven Performance and Availability Planning for a Mobile Edge Computing System
## 4 Evaluation of MEC Performance
### Basic SPN Model for MEC Architectures
#### 4.1.2 Numerical Analysis
As AD increases, the level of utilization declines subtly in all three cases. However, this fall only starts at AD = 3.0 ms for SC = 8 and at AD = 1.5 ms for SC = 16. Up to these points, the utilization level is around 82%, which causes the behavior of the MRT explained above. Figure 7 presents the probability of discarding new requests. For SC = 32, the discard probability is equal to 0. Therefore, if it is possible to acquire a server with 32 cores, there will be no discarding independent of the interval between request arrivals. For SC = 8 and SC = 16, only from AD = 2.0 ms and AD = 4.0, the discard probabilities tend to 0. These  
Figure 5: Master Server Utilization Level. Figure 6: Slaves Server Utilization Level. initial discard intervals are directly related to the high level of utilization presented by both servers, directly impacting the MRT. Therefore, any stochastic analysis performed with the proposed model must observe the four metrics to obtain a complete view of the system behavior. It is also possible to identify the operating limits of the system. In other words, these limits represent how many jobs can be lost without compromising the utility of the system.
## 3 Transmitter and Receiver Structure
### Precoder Design
Keeping in mind that both \(\mathbf{H}_{g}^{u}\) and \(\mathbf{F}_{u}\) have a block diagonal structure, \(\tilde{\mathbf{H}}_{g}^{u}\) will be block diagonal as well. Only a few positions of \(\mathbf{s}_{g}^{u}\) will tend to be nonzero, according to the stated system model. As a result, \(\mathbf{s}_{g}^{u}\) is a sparse vector, and its sparsity can be used to lower the transmitted power. To do this, we can use the same strategy that we considered in [39] for virtual GSM transmissions, in which an alternative signal is generated to reduce the transmitted power while assuring that the signals arriving at receivers are similar to the original ones. Although we assume fully digital precoders in this paper, it is possible to simplify the implementation complexity of the proposed solution by using a hybrid precoder design comprising a reduced digital part and an analog part supported for example on analog phase shifters (and which are the same for all subcarriers). The hybrid design can be easily accomplished following the approximation-based approach described in [40].
## 3 Building Load Type
### Classify Based on Building Type
#### 3.2.3 Office Buildings  
Office buildings are designed for government agencies, organizations, enterprises, and institutions to handle administrative affairs and carry out various business activities. These buildings serve as the central hub for intellectual workers to conduct their daily work and activities, encompassing a range of forms including administrative office buildings, specialized office buildings [89], and more. With the advancement of the times, modern office buildings are continuously evolving towards a comprehensive and integrated direction, cleverly integrating various functional spaces such as offices, meeting rooms, commercial spaces, restaurants, and garages into a single structure, thereby forming a  
Figure 7: A Schematic diagram of the process of improving energy efficiency in an educational building [86].  
diversified and integrated architectural system. For the purpose of collecting data related to energy consumption in the office building, the sensor layout is as shown in Figure 8.  
The energy consumption of office buildings mainly originates from service equipment such as lighting, air conditioning [90], elevators, and various office appliances. Their energy consumption characteristics are evident, primarily manifesting in relatively stable operating hours and being significantly influenced by both personnel density and climatic environmental factors.  
To more effectively manage the energy consumption of office buildings, modern technology such as machine learning and deep learning is often employed for load prediction [92; 93]. This prediction process is similar to that of other types of building load prediction, involving multiple crucial steps such as data processing, feature engineering, model establishment, performance evaluation, and practical application. Through these steps, we can predict the energy consumption of office buildings more accurately, thereby formulating more reasonable energy-saving measures and strategies.
## 5 Discussion
### The Outperformance of SIF in Capturing GPP Changes during Drought  
Our study highlights the significant advantages of SIF over NDVI and kNDVI in capturing the changes in GPP caused by drought, particularly during the extreme drought event of 2009-2010. SIF exhibited a more sensitive and rapid response to drought stress compared to NDVI and kNDVI. This heightened sensitivity of SIF is attributed to the direct impact of drought conditions on plant photosynthesis. SIF, as a direct measure of photosynthetic activity, can more acutely and promptly reflect changes resulting from environmental stresses like drought [44]. In contrast, NDVI, which primarily represents the overall greenness and biomass of vegetation, can continue to show a relatively healthy vegetative state even under conditions where plants are stressed, and photosynthetic activity is reduced [45]. This disparity arises because NDVI is more a reflection of vegetation cover and biomass, rather than a direct measure of the physiological state of the plants.  
Moreover, SIF provides a more precise and timely method for capturing the dynamics of vegetation GPP during drought events. Owing to its direct correlation with the immediate changes in plant photosynthesis, SIF demonstrates a smaller response lag to drought. This characteristic allows it to indicate short-term drought conditions more rapidly. NDVI and kNDVI, on the other hand, exhibit a time lag in reflecting the impacts of drought. This lag is likely due to the time required for significant changes in plant health and biomass to manifest, thus limiting the ability of these indices to provide a real-time indication of drought stress [46].  
It is noteworthy that the relationship between SIF and GPP may be affected by the physiological and structural information of SIF, especially under drought conditions [47; 48]. Drought stress triggers various physiological responses in plants, such as stomatal closure to reduce water loss through transpiration. This closure reduces the availability of carbon dioxide for photosynthesis, which can lead to a decrease in GPP. Additionally, the changes of canopy structure can influence the amount of light absorbed and emitted by leaves, which will affect both SIF and GPP. It is reported normalizing SIF by the near-infrared radiance of vegetation provides an efficient strategy to normalize SIF to canopy structure [49].  
Physiological and structural information of plants under drought conditions can influence the SIF-GPP relationship by affecting photosynthetic efficiency, chlorophyll fluorescence, and other related processes [50; 51]. Understanding these relationships is crucial for assessing plant responses to environmental stressors and predicting ecosystem productivity under changing climatic conditions.
# Analysis of BDS/GPS Signals' Characteristics and Navigation Accuracy for a Geostationary Satellite
## 3 BDS/GPS Signal Characteristics
### Pdop  
PDOP in common use is useful to characterize the accuracy of the position and velocity solutions. It can be computed based on the square root of the trace of the \(\left(H^{T}H\right)^{-1}\) matrix. The matrix \(H\) is called as the design matrix or the Jacobian matrix and related with the least-squares solution in single point position. The matrix of \(H\) is expressed in form [27; 28]  
\[H=\left[\begin{array}{ccccc}a_{x}^{i1}&a_{y}^{i1}&a_{z}^{i1}&1&0\\ a_{x}^{i2}&a_{y}^{i2}&a_{z}^{i2}&1&0\\ \vdots&\vdots&\vdots&\vdots&\vdots\\ a_{x}^{im}&a_{y}^{im}&a_{z}^{i2}&1&0\\ a_{x}^{i1}&a_{y}^{i1}&a_{z}^{i1}&1&1\\ a_{x}^{i2}&a_{y}^{i2}&a_{z}^{i2}&1&1\\ \vdots&\vdots&\vdots&\vdots&\vdots\\ a_{x}^{jm}&a_{y}^{jm}&a_{z}^{jm}&1&1\end{array}\right] \tag{1}\]  
where \(n\), \(m\) are the number of GPS or BDS satellites, respectively. \(a_{x}^{in},a_{y}^{in},a_{z}^{in}\) denote the direction cosine vector from the receiver position of \(x,y,z\) to the \(n\)th satellite of GPS. \(a_{x}^{jm},a_{y}^{jm},a_{z}^{jm}\) denote the direction cosine vector from the receiver position of \(x,y,z\) to the \(m\)th satellite of BDS. When using combination of GPS and BDS, the matrix \(H\) is a \(\left(n+m\right)\times\left(n+m\right)\times\left(n+m\right)\times\left(n+m \right)\times\left(n+m\right)\times\left(n+m\right  
5, otherwise the matrix \(H\) is a \(n\times 4\) and the last column in Equation (1) is ignored [27; 28]. The PDOP can be obtained from  
\[PDOP=\sqrt{(H^{T}H)_{1,1}^{-1}+(H^{T}H)_{2,2}^{-1}+(H^{T}H)_{3,3}^{-1}} \tag{2}\]  
According to the Equation (1) and Equation (2), the mean value of PDOP of GPS and GPS + BDS over two days are 10.24 and 3.91, respectively. It can obviously reduce the PDOP when we use GPS + BDS constellations, as shown in Figure 5.  
Sky views of the GPS and BDS satellites tracked are given in Figure 6. The nadir angles range of BDS satellites tracked is larger than that of GPS satellites tracked. Especially, the nadir angles of BDS GEO and IGSO satellites tracked are in range of 45\({}^{\circ}\) to 85\({}^{\circ}\), which are larger than those of BDS MEO satellites tracked. This indicates that a positive contribution of BDS GEO and IGSO satellites to the improvement of PDOP is expected, although the number of these satellites tracked is not large in this mission.  
Figure 5: Values of PDOP over two days.  
Figure 6: Sky view of GPS and BDS satellites tracked over two days with respect to azimuth and nadir angles of the receiver.
## 4 Results
### Commercial vs. Open-Source Considerations  
Ethical considerations regarding proprietary (commercial) versus open-source models are vital in medicine. Commercial models such as GPT-4o and Gemini-1.5-pro offer high performance but are associated with recurring costs and potential data governance concerns. Their proprietary nature may limit transparency, which is critical in medical applications, where auditability and reproducibility are often regulatory requirements.  
Open-source models like Mixtral-8x22b and Qwen-2.5-72b provide greater transparency and adaptability, potentially aligning better with medical ethics and compliance standards. However, their slightly lower accuracy and performance may require compensatory measures, such as additional validation steps or integration with domain-specific heuristics, to meet medical-grade quality benchmarks.
# Efficient Prompt Optimization for Relevance Evaluation via LLM-Based Confusion Matrix Feedback
## 2 Related Works
### Relevance Evaluation in Information Retrieval  
Evaluating the relevance of retrieved passages to user queries has long been fundamental in information retrieval (IR). Traditionally, crowdsourced human assessors have been employed for this task [16; 17]. However, human-based evaluations are time-consuming, costly, and often inconsistent due to the subjectivity of human judgment [18; 19]. With advancements in machine learning, automated relevance judgment has increasingly gained traction [20]. Transformer-based neural ranking models, such as BERT, have been widely adopted for relevance evaluation tasks [21]. Nevertheless, balancing human-like precision with the scalability of automated methods remains a significant challenge.  
Recent studies have begun exploring the use of LLMs, such as GPT-3, ChatGPT, and GPT-4, as relevance assessors. [PERSON] et al. [22] and [PERSON] et al. [23] evaluated GPT-3's effectiveness in automated relevance annotation tasks. [PERSON] and [PERSON] [24] explored one-shot approaches for leveraging LLMs to assess document relevance, improving consistency in evaluations. Similarly, [PERSON] et al. [5] demonstrated that LLMs could match human annotators' accuracy at scale. Despite these promising results, concerns remain regarding the reliability and consistency of LLM-based judgments, as highlighted by [PERSON] et al. [25], who pointed out theoretical risks associated with fully replacing human assessors.  
While existing studies have leveraged LLMs for relevance evaluation, they have generally overlooked the critical role of prompts. Given that prompt quality significantly influences the performance of LLMs, our work specifically addresses optimizing prompts to enhance the accuracy and efficiency of query-passage relevance evaluation.
# Enabling Horizontal Collaboration in Logistics Through Secure Multi-Party Computation
## 1 Introduction
### Related Work  
Horizontal collaboration in the transport domain is a highly active field for research and development, and we refer to [PERSON] et al. [4] for an introduction and overview.  
Regarding optimization, transport logistics [6] planning problems [7; 8] have become extremely large and complex due to, for example, the sheer problem size and the amount of involved network partners or complex optimization criteria that might be in opposition to each other (e.g., timeliness vs. load factors or carbon footprint). State-of-the-art approaches for (company-internal) optimization are built upon a number of techniques, e.g., (mixed) integer linear programming [9] like IBM's CPLEX LS2010 ([[https://www.ibm.com/products/ilog-cplex-optimization-studio](https://www.ibm.com/products/ilog-cplex-optimization-studio)]([https://www.ibm.com/products/ilog-cplex-optimization-studio](https://www.ibm.com/products/ilog-cplex-optimization-studio)) (accessed on 30 June 2025)); constraint programming [10] like IBM's CP Optimizer targeting scheduling and timetabling problems [11]; or Google's OR Tools [12], targeting vehicle routing or bin-packing problems. In addition to that, there are many existing highly effective heuristic search algorithms [13] for many classic optimization problems like scheduling, routing, or packing problems [14]. However, all of these mentioned approaches consider in-house optimization and have not been designed for federated computation in CTNs.  
A first approach toward privacy-preserving optimization has been taken by [PERSON] and [PERSON] [15] using the Artificial Bee Colony Algorithm from swarm intelligence. However, their work leaves many questions unanswered, especially related to the formal modeling of the precise information revealed to the stakeholders or the actual deployment of the algorithm, so it can only be seen as an innovative first step. We will also use the same underlying reference dataset [5] to validate the results of our heuristic.
# Removal of Cesium from Radioactive Waste Liquids Using Geomaterials
## 3 Results and Discussion
### Adsorption Isotherm Study  
The adsorption isotherm is usually used to reflect the performance of the adsorbent in the adsorption process. In order to quantify the adsorption capacity of the adsorbent to remove Cs, [PERSON] and [PERSON]'s isotherms were used in this study. Under optimal adsorption conditions (pH of 8, contact time of 24 h, adsorbent dosage of 0.6 g), the adsorption isotherm model of Cs\({}^{+}\) on the geopolymer was established at the initial concentrations of 5 ppm, 10 ppm, 20 ppm, 30 ppm, and 40 ppm. The results of the adsorption data investigated by the [PERSON] and [PERSON] models are shown in Figures 8 and 9, respectively. Other parameters, including the correlation coefficient (\(R^{2}\)), are given in Table 4. The correlation coefficient shows that the [PERSON] isotherm can describe the adsorption of Cs better than [PERSON]'s isotherm model can. This result shows that the adsorption of Cs\({}^{+}\) by geomeratials mainly occurs through a single-layer reaction, and the maximum adsorption capacity is estimated to be 89.32 mg\(\cdot\)g\({}^{-1}\). The attached isotherm parameters show that the fly ash-based geomeratials are more effective for adsorbing Cs ions.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Langmuir Isotherm**} & \multicolumn{3}{c}{**Freundlich Isotherm**} \\ \cline{2-7}
**Sample** & \(q_{\text{max}}\) & \(K_{\text{L}}\) & \(R^{2}\) & \(\mathbf{K_{F}}\) & \\  & \(\mathbf{(mg\cdot g^{-1})}\) & \(\mathbf{(L\cdot mg^{-1})}\) & & \(\mathbf{(dm^{-3}\cdot mg^{-1})^{1/n}}\) & & \\ \hline Fly ash & 89.32 & 0.1665 & 0.9948 & 10.75 & 0.01620 & 0.1309 \\ Slag & 44.52 & 0.02126 & 0.9954 & 20.55 & 0.04660 & 0.3698 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Coefficient of Langmuir and Freundlich isotherms for Cs.  
Figure 7: Pseudo-second-order linear kinetic model of Cs adsorption on the fly ash-based geomeratials and slag-based geomeratials.