esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 PV Model Modeling and Problem Formulation
### Solar Cells
#### 2.1.2 DDM Modelling  
One limitation of SDM is its failure to consider the impact of ongoing waste recombination within the reduction zone. In seeking this objective, the researchers introduced the DDM, which is characterized by its ability to calculate the recombination current loss in the depletion region by the incorporation of an extra diode. It is believed that the DDM offers greater precision compared to the SDM [30].  
The circuit design that represents the DDM is depicted in Figure 2. In DDM, the photo-produced current source is implemented using a pair of diodes. The diode labeled as \(D_{1}\) is responsible for generating the propagation current within the \(p\)-\(n\) junction, while the diode denoted as \(D_{2}\) specifically deals with the consequences of recombination in the region of the junction associated with space freight age, as discussed in [31]. In the domain of direct current (DC) electrical systems, the current output of a cell, denoted as \(I\), can be ascertained by employing the subsequent equation:  
\[I=I_{ph}-I_{d_{1}}-I_{d_{2}}-I_{sh}, \tag{6}\]  
\[I_{d_{1}}=I_{sd_{1}}\,\left[exp\left(\frac{V+R_{s}\cdot I}{\gamma_{1}\cdot V_{ t}}\right)-1\right], \tag{7}\]  
\[I_{d_{2}}=I_{sd_{2}}\,\left[exp\left(\frac{V+R_{s}\cdot I}{\gamma_{2}\cdot V_{ t}}\right)-1\right]. \tag{8}\]  
Variables \(I_{d_{1}}\) and \(I_{d_{2}}\) denote the currents flowing through the first and second diodes, respectively. The currents responsible for propagation and saturation effects in diodes \(D_{1}\) and \(D_{2}\) are represented by symbols \(I_{sd_{1}}\) and \(I_{sd_{2}}\), respectively. The initial and subsequent abstract characteristic operators of diodes \(D_{1}\) and \(D_{2}\), respectively, are denoted as \(\gamma_{1}\) and \(\gamma_{2}\). Based on the information provided, the current output (\(I\)) of the cell as determined by the DDM can be expressed in an alternative formulation.  
\[I=I_{ph}-I_{sd_{1}}\left[exp\left(\frac{V+R_{s}\cdot I}{\gamma_{1}\cdot V_{t} }\right)-1\right]-I_{sd_{2}}\left[exp\left(\frac{V+R_{s}\cdot I}{\gamma_{2} \cdot V_{t}}\right)-1\right]-\frac{V+R_{s}\cdot I}{R_{sh}}. \tag{9}\]  
As demonstrated in Equation (9), there exist seven unidentified parameters (\(I_{ph}\), \(I_{sd_{1}}\), \(I_{sd_{2}}\), \(\gamma_{1}\), \(\gamma_{2}\), \(R_{s}\), \(R_{sh}\)) that necessitate estimation through the utilization of the measured I-V data of the solar cell.
## 5 Architecture, Optimization, and Technical Challenges in LLM4 Rec
### Performance Evaluation and Benchmarking of LLM4 Rec
#### 5.4.1 Commonly Used Datasets and Benchmarks  
The datasets used for LLM4 Rec span various domains and offer structured and unstructured data. They differ in scale, granularity, and contextual richness, making them suitable for testing the adaptability of LLMs in real-world recommendation scenarios.  
* Amazon Review Data [81]: This dataset includes user reviews, ratings, and metadata for millions of products, supporting text-based and hybrid recommendation tasks.
- Taobao Dataset [92]: This is a large-scale dataset capturing user behaviors, including clicks, purchases, and reviews, often used for sequential and session-based recommendations.
- AliExpress Dataset [93]: This dataset focuses on cross-border e-commerce, combining multilingual reviews with user interaction logs to evaluate cross-language recommendations.
* _Microsoft News Dataset (MIND)_[94]: This set contains news articles, click behaviors, and user session data, making it a benchmark for contextualized and personalized news recommendations.
- Adressa Dataset [95]: This includes user clicks and reading behaviors on Norwegian news websites, testing the multilingual capabilities of LLMs.
- _MIND Your Language Dataset_[96]: This dataset provides multilingual news articles with user interaction data, offering content-based and cross-lingual recommendations benchmarks.
* _Movielens_[97] features user-item movie ratings, serving as a baseline for collaborative filtering and hybrid models.
- _Spotiy Dataset_[98] captures user interactions with playlists, songs, and artists, ideal for music recommendations.
- _YouTube Dataset_[99] offers insights into video watch behaviors, enabling sequential and content-based recommendation evaluations.
* _ELNet_[100] contains hierarchical data from online education platforms, enabling personalized learning pathway recommendations.
focuses on student performance in quizzes, allowing for adaptive learning recommendations. - _KDD Cup 2010 Educational Data Challenge_[102] tests knowledge tracing models by evaluating student responses to educational content.
* _Synthea_[103] simulates electronic health records (EHRs) with clinical notes, supporting health-related recommendations.
- _HealthTweets_[104] consists of health-related tweets, enabling sentiment-aware lifestyle recommendations.
- _HeartSteps Dataset_[105] tracks physical activity and contextual factors, which are useful for fitness app recommendations.  
These datasets collectively test various aspects of LLMs, including their ability to handle sequential data, contextual embeddings, and domain-specific nuances.
## 2 Antenna Modelling
### Trade-Offs and Design Considerations  
Optimizing antenna performance with modest gain and efficiency requires trade-offs and design considerations. Trade-offs: Size: High-gain antennas are larger and may not fit in small areas. Moderate gain balances performance and size for the antenna. Efficiency vs. Complexity: Complex antenna designs increase production costs. Choosing moderate efficiency simplifies the design and cuts production costs. Considerations: Design Wi-Fi routers use moderate-gain antennas for wide-area coverage. The signal range and directionality are well balanced or battery-powered devices, and antennas with high gain may use too much power. Energy-efficient moderate-gain antennas are available. Interference: Neighbouring devices and reflections can affect high-gain antennas. Moderate-gain antennas reduce interference and perform well. Optimising antenna performances with modest gain and efficiency requires trade-offs and design considerations. Trade-offs:Size: High-gain antennas are larger and may not fit in small areas. Moderate gain balances performance and size for the antenna.
# Non-Terrestrial Networks with UAVs: A Projection on Flying Ad-Hoc Networks
## 7 Challenges and Future Research Directions
### Network Coding  
Apart from feedback-based retransmission techniques in Section VIIF, the development of network coding strategies [117; 174] for FANETs where conventional channel coding schemes, e.g., cyclic redundancy check (CRC), are constrained, can be beneficial. Network coding is an alternative to feedback-based reliable transmission techniques like HARQ. Indeed, a feedback-based approach can be problematic in FANETs since UAVs can move far from terrestrial and non-terrestrial nodes and re-transmissions in ARQ/HARQ impose large latency into the system due to long RTT [90]; thus, precluding agile development. Therefore, network coding can be applied over the data packets for packet-level coding and is expected to have a better performance in terms of throughput than conventional repetition-based coding. Hence, network coding is the potential solution that the system can leverage on the benefits of non-redundant packet re-submissions due to FANET challenging fading environments and long RTT. Indeed, a proposal of network coding strategies for packet-level coding can leverage existing UAVs for cost-effective FANETs and reliable connectivity while enhancing the system throughput, reducing delays, and constructing a more robust network.
# Enhanced Wind Energy Forecasting Using an Extended Long Short-Term Memory Model
## 2 Extended Long Short-Term Memory Networks  
The architectural enhancement of xLSTM to traditional LSTM networks introduces two key improvements, i.e., exponential gating with memory mixing and a novel matrix memory structure. The architecture of the xLSTM model achieves these improvements through two variants, i.e., a scalar LSTM (sLSTM) model and a matrix LSTM (mLSTM) model. The sLSTM model enhances the traditional architecture of LSTM networks by implementing exponential gating mechanisms alongside a new memory mixing technique, enabling more effective revision of stored information through scalar cell states while maintaining the xLSTM model's tracking capability for complex time-sequence dependencies. The mLSTM model replaces scalar memory cells with matrix-valued states, incorporating a covariance update rule for key-value associations, which expands the xLSTM model's storage capacity while enabling parallel computation through matrix operations. These sLSTM and mLSTM models are integrated into residual blocks and stacked to form comprehensive architectures. The xLSTM model maintains linear computational complexity with respect to sequence length while offering enhanced capabilities in memory retention, state tracking, and long-range dependency modeling.
# A Domain Adaptive Person Re-Identification Based on Dual Attention Mechanism and Camstyle Transfer
## 3 The Proposed Method
### Dual Attention Network
#### 3.6.2 Channel Attention Module  
Each channel graph of high-level features can be regarded as a class-specific response, and different semantic responses are related to each other. By taking advantage of the interdependence between channel mappings, the inter-dependent feature mapping is emphasized, and the feature representation of specific semantics is improved. Therefore, a channel attention module is used to explicitly model the inter-dependence between channels. The structure of the channel attention module is shown in Figure 2b. Different from the position attention module, this module directly calculates the channel attention map \(X\in R^{C\times C}\) from the original feature \(A\in R^{C\times H\times W}\). Specifically, \(A\) is reshaped into \(R^{C\times N}\), and then matrix multiplication is performed between \(A\) and \(A\)'s transpose. Finally, the softmax layer is applied to obtain the channel attention map \(X\in R^{C\times C}\) as follows.  
\[x_{ji}=\frac{\exp(A_{i}\times A_{j})}{\sum_{i=1}^{C}\exp(A_{i}\times A_{j})} \tag{10}\]  
where \(x_{ji}\) represents the influence of the \(i\)-th channel on the \(j\)-th channel. In addition, a matrix multiplication is performed between the transpose of \(X\) and \(A\), and the result is reshaped to \(R^{C\times H\times W}\). Then the result is multiplied by a scale parameter \(\beta\) and an element-wise summation operation is performed on \(A\) to obtain the final output \(E\in R^{C\times H\times W}\) as follows.  
\[E_{j}=\beta\sum_{i=1}^{C}\left(x_{ji}A_{i}\right)+A_{j} \tag{11}\]  
The weights are gradually learned from 0. Equation (11) shows that the final feature of each channel is the weighted sum of all channel features and the original features, thereby establishing a long-term semantic dependency between feature maps. It is conducive to improving the distinguish ability of features.  
In order to make full use of remote context information, the characteristics of these two attention modules are integrated. Specifically, the outputs of the two attention modules are transformed through the convolutional layer, and the element summation is performed to complete the feature fusion. Lastly, the final prediction map is generated through the convolutional layer.
# A New Design Method of Shield Tunnel Based on the Concept of Minimum Bending Moment
## 1 Introduction
Hence, if a kind of shield tunnel cross-section can be designed so that the theoretical bending moment for any section is zero (namely \"zero bending moment shield tunnel\"), the above-mentioned diseases of shield tunnels can be solved, which also contributes to the reduction in segment reinforcement. In this article, a new idea of cross-section design for a minimum bending moment shield tunnel is proposed. First, we put forward the concept of a zero bending moment shield. Then, based on rational and feasible hypotheses, a structural mechanical model and analytical expression of the axis for the cross-section of the zero bending moment shield tunnel were obtained, and computational formulas of internal force and key geometry parameters were given. Then, based on the case of the metro shield tunnel constructed in the Shanghai soft soil area, the zero bending moment shield tunnel was designed, and its characteristics were analyzed. Considering that only one cross-section of shield tunnel can be adopted in one metro line, the design method and procedure of the minimum bending moment shield tunnel have been put forward. Finally, taking one of the Shanghai metro lines as an example, the cross-section of the minimum bending moment shield tunnel has been designed. The pioneering work performed here shows that a rational cross-section can greatly decrease the bending moment for underground structures. For shield tunnels or buried pipe, the minimum bending moment cross-section is similar to a vertical ellipse. The research results can be used as a reference for the design of a shield tunnel or other underground structures.
# A 32-GHz Eight-Way Power Amplifier MMIC in 150 nm GaN HEMT Technology
## 4 Conclusions  
In this study, we have presented a two-stage eight-way 32 GHz high-power amplifier (HPA) in 150 nm GaN HEMT technology for Ka-band deep space satellite communication systems. To achieve a high output power with a minimum chip area, an outside back via (OSV)-type HEMT was used with the stabilizing circuitry using a parallel RC network at the gate and a bypass capacitor network with a de-Qing resistor. The values of the stabilization resistors and capacitors were selected by minimizing gain degradation. With this configuration, the whole amplifier occupies 19.35 mm\({}^{2}\), including all the RF, ground, and DC pads. The measured amplifier demonstrated an output power of 38.3 dBm and 16.4 dB small-signal gain at 32 GHz with a 3 dB gain bandwidth of 3.5 GHz between 31.3 and 34.8 GHz, which corresponds well with the simulation results.  
**Author Contributions:** Formal analysis and data curation, H.-G.P. and V.-S.T.; validation, M.-K.L. and B.-H.L.; data curation, M.-K.L., B.-H.L. and K.-I.N.; conceptualization, formal analysis, and supervision, J.-D.P.; writing--original draft, H.-G.P.; writing--review and editing, J.-D.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Dongguk University Research Fund of 2021 and supported by a grant-in-aid of HANWHA SYSTEMS.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** This work was supported by the Dongguk University Research Fund of 2021 and supported by a grant-in-aid of HANWHA SYSTEMS.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analysis, or interpretation of the data; in the writing of the manuscript; or in the decision to publish the results.
## 4 Discussion
### Limitation and Strengths of the Study  
This study has several limitations that need to be mentioned. First, the age of the participants, which only included children aged 5 to 10 years, excluding those up to 14 years old when foot bone development is complete. Additionally, it should be noted that there was a small number of 10-year-old participants. The reason for this age range was that in Spain, children transition from primary education to secondary school during this stage, and this study was conducted in a school, so we could not access older children. Furthermore, our study was based on the observation of a large, multicentric, and homogeneous sample, although limited to a single country, with the participants' ethnicity being primarily Caucasian, which prevents extrapolation of the results to other ethnicities. Moreover, although the RSscan Footscan(r) 9 (RSscan International, Olen, Belgium) pressure platform has demonstrated good intra-evaluator and inter-evaluator reliability [25], it only measures forces perpendicular to the ground, without considering forces in other planes. Finally, the fact that the children felt observed while walking could have altered their usual gait patterns. Lastly, another limitation is the low number of children with supine FPI. This meant that no significant differences or associations could be detected. Even so, these data should not be underestimated, as it may be overlooked in larger studies because it is not as common.  
Despite the limitations, this study also has significant strengths. Firstly, the evaluation of plantar pressure during dynamic movements has been established as a reliable method for analyzing foot structure and function. Secondly, it covered a broad spectrum of ages in children. Thirdly, the measurement instruments used are commonly employed in both clinical practice and research. Additionally, the data obtained and our conclusions may have significant implications for both clinical practice and public health. Finally, and to our knowledge, no studies have linked FPI with as many variables as the present study, making this the first study to analyze numerous morphofunctional variables in this population and link them with foot type.
## 2 Materials and Methods
### Mathematical Modeling Part
#### 2.2.3 Evaluation Metrics  
The yearly heating energy demand (\(\mathrm{E}_{\mathrm{heat}}\)) is calculated according to the next expression:  
\[\mathrm{E}_{\mathrm{heat}}=\int_{\mathrm{October}}^{\mathrm{April}}\mathrm{Q} _{\mathrm{heat}}\cdot\mathrm{dt} \tag{20}\]  
The yearly electricity demand (\(\mathrm{E}_{\mathrm{el}}\)) is calculated as below:  
\[\mathrm{E}_{\mathrm{el,tot}}=\int_{\mathrm{October}}^{\mathrm{April}}\mathrm{P} _{\mathrm{el,tot}}\cdot\mathrm{dt} \tag{21}\]  
The yearly (or seasonal) coefficient of performance (SCOP) of the system can be found as below:  
\[\mathrm{SCOP}=\frac{\mathrm{E}_{\mathrm{heat}}}{\mathrm{E}_{\mathrm{el,tot}}} \tag{22}\]The life cycle cost (LCC) of the investment is calculated according to the following expression:  
\[\text{LCC}=\text{CC}+\sum_{\text{i=1}}^{\text{N}}\frac{\text{E}_{\text{el}}\cdot \text{k}_{\text{el}}+\left(\text{O\&M}\right)}{\left(1+\text{r}\right)^{\text{i }}} \tag{23}\]  
In this work, the capital cost includes the cost of the solar field, the thermal tank and the heat pump. The specific cost of the solar field was elected at 150 EUR/m\({}^{2}\)[32], of the storage tank at 1000 EUR/m\({}^{3}\)[34] and of the heat pump at 750 EUR/kW\({}_{\text{th}}\)[35] (the maximum thermal load of the building was found at 6 kW according to the simulation in TRNSYS). Moreover, the yearly operation and maintenance cost (O&M) was chosen to be 1% of the investment cost [36]. The investment lifetime was selected at N = 25 years and the discount factor at r = 3%. The electricity was estimated at k\({}_{\text{el}}\) = 0.22 EUR/kW\({}_{\text{el}}\) by taking into consideration the new increases in the electricity price.
## 3 Materials and Methods  
The materials and methods used in this study are explained in this section. We use roughly the separation into three functional levels--device, cell and plants levels--that we also used previously in [2] as originally explained in [17].  
On the device level the real-time requirements are very strict [18]. Typical devices doing the communicating are actuators, programmable logic controllers (PLC), sensors and such. Very little amounts of data are transmitted. Data transmitted represent two categories: data transfers. which are typically cyclic and periodic are one category, and alarms another. The data transfers are composed of control and measurement data that flows between field equipments. Alarms interrupt the normal data flow [17].  
The cell level has the responsibility to coordinate various device controllers that reside in the device level. Communication typically consists of transmissions of tens of kilobytes in structured form in a client-server model [17].  
The plant level interconnects the cell controllers to enterprise resource planning (ERP) systems. It does most of its communications during non-critical times transmitting much larger amounts of data compared to the previous two levels [17]. Networks present in a factory environment should also be segmented by use of firewalls and demilitarized zones (DMZ), terminating connections coming from outside of the ICS network at these DMZ's. This segmentation into security areas should not be confused with the above functional differentiation. The security segmentation and its particularities is discussed extensively in several research papers and other documents, such as [1]. The division into separatedfunctional levels allows for the creation of separate models for anomaly detection. What is anomalous in one functional level can be normal when another level is concerned.  
Due to the architecture of the network under scrutiny and its primary communication method, the functional layer separation is unclear. This will be discussed in Section 3.1.
## 2 Preliminaries and Models
### Additively Homomorphic Encryption Based on The Keystream  
Our security property partly comes from the stream cipher. The keystream generated from the pseudo-random function satisfies the security properties of the additively homomorphic encryption in the stream cipher. The basic idea [19] is denoted as follows:  
Encryption is written as: \(c=Enc_{k}(m+K)\) mod \(M\) where \(K\) is randomly generated keystream, \(m\) is the plaintext and \(m,k\in[0,M-1]\).  
Decryption is described as: \(Dec_{k}\)= \(c-K\) mod \(M\).  
Additively homomorphic property of ciphertext are described as: \(c_{1}=Enc_{K1}(m_{1})\) and \(c_{2}=Enc_{K2}(m_{2})\); then, the aggregated ciphertext is expressed as: \(c=c_{1}+c_{2}\)mod \(M=Enc_{K}(m_{1}+m_{2})\), where \(K=K_{1}+K_{2}\) mod M.