esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Evaluation of IoT-Enabled Instrumentation for a Soil-Bentonite Slurry Trench Cutoff Wall
## 5 Data Storage  
The datalogger produces data organized in a tabular format with one row generated per sampling interval. The data collection process transforms this format into JSON objects which are compressed and transported to the gateway computer via the data transmission protocol. At the gateway, data is extracted from the the compressed JSON files and inserted into the time series database. The JSON files are retained as a backup. Although many specialized time series databases exist [21], such as InfluxDB and OpenTSDB, we chose to store the data in a standard MongoDB database. The two primary considerations were (1) this project wasn't expected to produce sufficiently large amounts of data to justify the use of a specialized database and (2) we are more familiar with the MongoDB API.  
Storing time series data in a document-based database takes a bit of planning to achieve good performance. We followed the suggestions from a MongoDB webinar on this topic [22] and use three levels of aggregation: hourly, weekly, and monthly.  
To insert a sample in the database, we first find or create a document for the given sensor, year, month, day, and hour. This document has two arrays containing all of the raw samples and timestamps received from the sensor in the particular hour. The hourly documents also contain the average sensor reading over the one-hour interval. This average is updated as data is inserted.  
Once the hourly document is updated, the corresponding weekly document as specified by sensor, year, month, and week number, is created or updated. The weekly document contains The sensor, year, month, week, and average reading over that week. This update is done with a single MongoDB aggregation operation that averages the matching daily average readings in the week. Although this operation accesses \(24\times 7=168\) documents, it has not caused a performance problem.  
Finally, the monthly document is created or updated. This operation takes the sensor, year, and month field and stores the aggregate of all of the weekly sensor readings in the given month. This is again performed using a single MongoDB aggregation operation to compute the average from the corresponding weekly documents. This operation accesses fewer than 5 documents in the worst case.
# Game Theory-Based Smart Mobile-Data Offloading Scheme in 5G Cellular Networks
## 3 System Model
### NRF-EH Model  
The performance of an autonomous energy harvesting communication node (EHCN) is considered to be a function of the random flow of harvested energy using an \"energy packet\" model which discretizes both the data flow and the energy flow in the sensor node based on queuing networks [57]. The arrival of energy and data packets to the nodes are both random processes: energy flows in at random through energy harvesting and data accumulates into the node, also at random, through sensing. Just as data packets are assumed to be collected into the EHCN in terms of discrete data packets, we consider that the harvested energy is also collect into the device's storage battery in discrete units of the energy packets [58; 59; 60]. Therefore, in this paper, an energy packet is defined as the minimum amount of energy needed to transmit a single data packet.  
We assume that UEs always harvest non-RF energy (e.g., solar, wind, thermal) from the environment over the whole time slot in which each UE is powered by a limited-capacity battery and each battery is recharged by an energy harvester. Each UE can update the remaining energy in its battery at the end of every time slot for using in the next time slots. We consider practical case where arrived packets of harvested energy, denoted as \(e^{\mathit{h}\upsilon}\left(t\right)\) energy packets in which \(e^{\mathit{h}\upsilon}\left(t\right)\) take its value from \(\zeta\), are a finite number of energy packets. The value that \(e^{\mathit{h}\upsilon}\left(t\right)\) has in time slot \(t\) can be described as follows:  
\[e^{\mathit{h}\upsilon}\left(t\right)\in\left\{e_{1}^{\mathit{h}\upsilon},e_{2 }^{\mathit{h}\upsilon},\ldots,e_{\zeta}^{\mathit{h}\upsilon}\right\} \tag{3}\]  
where \(0\leq e_{1}^{\mathit{h}\upsilon}<e_{2}^{\mathit{h}\upsilon}<\ldots<e_{\zeta}^{ \mathit{h}\upsilon}\leq E_{bat}\), in which the energy of the UE is stored in a battery with a finite capacity of \(E_{bat}\) energy packets.  
We assume that the probabilities of harvested energy packets are followed a discrete probability distribution, as shown in (4):  
\[Pr^{\mathit{h}\upsilon}\left(k\right)=\Pr\left[e^{\mathit{h}\upsilon}\left(t \right)=e_{k}^{\mathit{h}\upsilon}\right],k=1,2,\ldots,\zeta \tag{4}\]  
The harvested energy is assumed to follow a stochastic Poisson process. Subsequently, \(e^{\mathit{h}\upsilon}\left(t\right)\) is a Poisson random variable with a mean value for harvested energy \(e_{mean}^{\mathit{h}\upsilon}\). The probabilities in (4) can be rewritten as follows:  
\[Pr^{\mathit{h}\upsilon}\left(k\right)\approx\frac{e^{-e_{mean}^{\mathit{h} \upsilon}}\left(e_{mean}^{\mathit{h}\upsilon}\right)^{k}}{k!},k=1,2,\ldots,\zeta \tag{5}\]
# Autonomous Load Regulation Based Energy Balanced Routing in Reshargeable Wireless Sensor Networks
## 6 Performance Evaluation  
In order to evaluate the performance of the algorithm, we used MATLAB to simulate the algorithm. The simulation parameters are shown in Table 1. The network lifetime and mean residual energy of key nodes (MREKN) were used as the performance parameters. The network lifetime is the round number when the network is subjected to its first dead node, which can reflect the performance of the network lifetime extension. The key nodes are the nodes that can communicate with the sink directly and these nodes relay the traffic of the whole network and have a great influence on the lifetime of WSN. The MREKN when the network has its first dead node can reflect the energy efficiency of the key nodes; the energy balancing performance is better when the MREKN is smaller.
# Self-Supervised Learning across the Spectrum
## 6 Experiments and Results
### Ablation Study  
We provide ablation studies using the PASTIS-R test dataset.  
**Loss Ablation:** We measure the individual contribution of different losses used in S4. Table 4 reports the results using the PASTIS-R dataset with both radar and optical inference. In both scenarios, the benefits of jointly optimizing the contrastive and reconstruction losses are higher as the number of labels increases. This demonstrates S4's ability to provide both temporal and spatial alignment benefits in pretraining to improve downstream model performance.  
**Modality Ablation:** We measure the effect of multiple modalities in Table 5. We compare against a unimodal variant of S4, where our proposed contrastive objective operates over an optical SITS and the same optical SITS with random augmentations, similar to how contrastive loss is used in prior work. We find significant gains in performance when the radar modality is added during training. This holds true for both scenarios of inference modality.  
**Geographical Ablation:** We report ablation results on the PASTIS dataset by pretraining on our curated, unlabeled Africa dataset. Table 6 reports our results for SL (Same Location Pretraining) and DL (Different Location Pretraining). In this setup, we fix our pretraining sets to have the same number of SITS samples for fair comparison. Although we see a dip in performance due to different geographical pretraining locations, which  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Ablation** & **Inference Modality** & **mIoU** \\ \hline \(\mathcal{L}_{c}\) & Optical & 52.6 \\ \(\mathcal{L}_{r}\) & Optical & 52.5 \\ \(\mathcal{L}_{c}\) & Radar & 53.6 \\ \(\mathcal{L}_{r}\) & Radar & 53.3 \\ \hline \(\mathcal{L}_{r}+\mathcal{L}_{c}\) & Optical & 53.4 \\ \(\mathcal{L}_{r}+\mathcal{L}_{c}\) & Radar & 54.6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Loss Ablation Results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Ablation** & **Inference Modality** & **mIoU** \\ \hline Single Modal & Optical & 51.3 \\ Single Modal & Radar & 53.8 \\ \hline S4 (Multimodal) & Optical & 53.4 \\ S4 (Multimodal) & Radar & 54.6 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Modality Ablation Results.  
is less representative of the fine-tuned dataset, the performance drop is limited and still performs well compared with other self-supervised approaches even when they use SL pretraining. This demonstrates the utility of S4 even on data where the geographic location is unknown.
# Hom Antenna on Chip Operating at 180 GHz Using the SiGe CMOS Process
## 3 Simulation and Measurement
### Discuss  
In Table 4, the chip antenna proposed in this article is compared with chip antennas using different processes, and small dimension can be obtained. In addition, the bandwidth percentage of the antenna is also very competitive. Compared to [15; 16; 19], the proposed SiGe CMOS horn antenna has a benefit in bandwidth. Compared to the antennas of [15; 16; 17; 18], the proposed antenna has a dimension advantage.  
In SiGe CMOS technology, because its procedure enables the incorporation of RF front-end circuits, antennas and additional functionalities onto a single chip. Having this design feature saves space and reduces power consumption. As can be seen in Table 4, compared with other different processes, the process and design proposed in this article obviously have advantages in space and are also quite competitive in terms of bandwidth percentage. The spatial advantage of the SiGe CMOS Horn antenna in this article is evident from Table 4, thus affirming the superiority of this antenna.  
Figure 10: Measurement environment (**a**) Antenna measurement front view; (**b**) Antenna measurement environment; (**c**) Antenna measurement platform; (**d**) Schematic measurement environment.
## 2 Generalized Super-Twisting Algorithm-Based Controller and Observer Design
### Nonlinear Continuous ESO Design
\begin{split}\dot{V}_{\zeta}&=\boldsymbol{\zeta}^{ T}\mathbf{P}_{\zeta}\boldsymbol{\zeta}+\boldsymbol{\zeta}^{T}\mathbf{P}_{ \zeta}\boldsymbol{\zeta}\\ &=-\frac{1}{2}\gamma_{1}|\boldsymbol{\xi}|^{-1/2}\boldsymbol{ \zeta}^{T}\mathbf{Q}_{\zeta}\boldsymbol{\zeta}-\gamma_{2}\boldsymbol{\zeta}^ {T}\mathbf{Q}_{\zeta}\boldsymbol{\zeta}++2\boldsymbol{\zeta}^{T}\mathbf{P}_ {\zeta}\boldsymbol{\mathbf{B}}_{\zeta}g(t)\end{split}  \tag{28}\]  
Combining with (26) and (27), and applying the Young's inequality, we have  
\[ \begin{split}\dot{V}_{\zeta}&\leq-\frac{1}{2} \gamma_{1}|\boldsymbol{\xi}|^{-1/2}\lambda_{\min}\big{(}\mathbf{Q}_{\zeta} \big{)}\|\boldsymbol{\xi}\|^{2}-\gamma_{2}\lambda_{\min}\big{(}\mathbf{Q}_{ \zeta}\big{)}\|\boldsymbol{\zeta}\|^{2}+\|\boldsymbol{\xi}\|^{2}+\mathbf{B}_{ \zeta}^{T}\mathbf{P}_{\zeta}^{T}\mathbf{P}_{\zeta}\mathbf{B}_{\zeta}\Delta^{2} \\ &\leq-\frac{\gamma_{1}^{2}\sqrt{\lambda_{\min}\big{(}\mathbf{P}_{ \zeta}\big{)}}\lambda_{\min}\big{(}\mathbf{Q}_{\zeta}\big{)}}{2\lambda_{\max} \big{(}\mathbf{P}_{\zeta}\big{)}}V_{\zeta}^{1/2}-\frac{\big{(}\gamma_{2} \lambda_{\min}\big{(}\mathbf{Q}_{\zeta}\big{)}-1\big{)}}{\lambda_{\max}\big{(} \mathbf{P}_{\zeta}\big{)}}V_{\zeta}+\mathbf{B}_{\zeta}^{T}\mathbf{P}_{\zeta}^ {T}\mathbf{P}_{\zeta}\mathbf{B}_{\zeta}\Delta^{2}\\ &=-\Gamma_{\zeta 1}V_{\zeta}^{1/2}-\Gamma_{\zeta 2}V_{\zeta}+C_{ \zeta}\end{split}  \tag{29}\]  
where  
\[\Gamma_{\zeta 1}=\frac{\gamma_{1}^{2}\sqrt{\lambda_{\min}\big{(}\mathbf{P}_{ \zeta}\big{)}}\lambda_{\min}\big{(}\mathbf{Q}_{\zeta}\big{)}}{2\lambda_{\max} \big{(}\mathbf{P}_{\zeta}\big{)}};\Gamma_{\zeta 2}=\frac{\big{(}\gamma_{2}\lambda_{\min}\big{(}\mathbf{Q}_{\zeta}\big{)}-1 \big{)}}{\lambda_{\max}\big{(}\mathbf{P}_{\zeta}\big{)}};C_{\zeta}=\mathbf{B}_ {\zeta}^{T}\mathbf{P}_{\zeta}^{T}\mathbf{P}_{\zeta}\mathbf{B}_{\zeta}\Delta^{2}\]  
The observer gains \(\pi_{1}\) and \(\pi_{2}\) are chosen such that the two terms \(\Gamma_{\zeta 1}\) and \(\Gamma_{\zeta 2}\) are positive. According to this, the finite-time convergence of the estimation errors is achieved. Based on Lemma 1 and (29), Lemma 3 is completely proven.
## 3 Results and Discussion
### Analysis of Relationship SM-VI-CO\({}_{2}\) under Drought Condition
#### 3.2.2 Drought Analysis and Validation during Drought Period  
For the selected drought period, we analyzed the temporal variations of SSMI and VCI at the study sites (Figure 7). The temporal patterns of the two indices show similar tendency, with the period of the most severe drought status being October 2014 to May 2015.  
Table 5 represented the applicability of the agricultural drought indices through error analysis at two validation area. An error analysis is a formed array that consists of drought or wet conditions as compared to the category of drought by the SM\({}_{\text{Huxtower}}\) data. The median (50%) of the standardized SM was set as the threshold for moderate drought, while the lower quantile (25%) was set as the threshold for severe drought. When the value of the standardized SM is less than the threshold, it is considered a drought condition. In the case of the samples, it is the counting of the drought indices with a value less than the threshold. The drought accuracy for SSMI and VCI at the study sites was approximately 60% to 80% (Table 5); SSMI showed relatively high accuracy for moderate drought and VCI for severe drought. SSMI is a drought index based on soil moisture that is sensitive to current soil moisture conditions and shows high accuracy under moderate drought conditions. VCI, on the other hand, is an index that detects weak vegetation activity due to lack of soil moisture, whose accuracy increases if the drought persists for a prolonged period (as the drought becomes more severe). These indices were suitable for determining the agricultural drought conditions. Each index can be used depending on how long the agricultural droughs continue.
# Profitability Assessment of Precision Agriculture Applications--A Step Forward in Farm Management
## 3 Results and Discussion  
### System Functionality  
The new module ProFit comprises a spatial component and a non-spatial component. The spatial component is developed in the pre-existing module PreFer within the ifarma FMIS. As a result, the output maps of ProFit (i.e., Cost maps, Earnings maps, and Profit maps) follow the same standards as those of the PreFer module, i.e., a spatial resolution of 5 m (a surface unit of 25 m\({}^{2}\)), thus allowing scaling up of original and processed data on a zone-, field-, cultivar-, and farm-scale and a classification of the original values into 7 categories.  
The non-spatial components include output tables, which at the same time contain the entire input information. Thus, the user gets the whole economic picture of cultivation in a single tabular arrangement.  
Most of the existing platforms in the market are top-down, providing financial management as a parallel service to another (main) service, e.g., fleet management, crop calendar, etc. In several cases, too, they merely serve an already established market player to promote its products, services, or goods through the concepts of precision agriculture.  
Conversely, ProFit on ifarma follows a bottom-up approach, starting from the fertilization applications and taking account of all the farming details to conduct a thorough economic analysis while enabling spatially distributed outputs in terms of maps.
# Analysis of the Channel Influence to Power Line Communications Based on ITU-T G.9904 (PRIME)
## 2 Scope of the Paper  
This article is focused on analyzing how different parameters of the channel and types of noise affect one of the standard PLC technologies deployed in Europe: ITU-T G.9904 (PRIME v1.3.6) defined by the Internation Telecommunication Union (ITU) in Reference [15]. This study is based on simulations of the PRIME physical (PHY) layer using different channel characteristics and different transmission parameters:  
* Channel configurable parameters.
* Channel response function, usually defined by its response in module and phase (described in Section 4.2).
* Noise model (described in Section 4.3).
* Signal-to-Noise Ratio (SNR).
* Transmission configurable parameters.
* Modulation scheme: Differential Binary Phase-Shift Keying (DBPSK), Differential Quadrature Phase-Shift Keying (DQPSK) and Differential 8 Phase-Shift Keying (D8 PSK) with and without convolutional encoding.
* Packet Service Data Unit (PSDU) length. The purpose of each simulation is to identify the PRIME behavior under individual channel effects. The understanding of the technology based on the conclusions of this article will be used to:
* Better understand individual issues, giving the information needed to identify and compare these individual channel effects on field deployments in order to evaluate how they might affect the network performance.
* Guide deployment strategies.
* Evaluate current PLC technologies to enhance their features in future developments.
* Obtain values of parameters to compare PLC technologies and choose the one best suited to a particular environment or application.  
Although the PLC propagation channel in the field is variable in time, in most situations, it is useful to consider it quasi-stationary, or stable enough along one single PHY Packet Data Unit (PPDU). Additionally, despite the fact that the real PLC propagation channel is more complex, it is also useful to consider it as a combination of several individual channel effects. In most situations, one (or a few) of these effects will be the determining factors that impose the Frame Error Rate (FER), and their nature and results could be compared with the results provided in this paper.
## 4 Simulation and Results
### Simulation Results and Discussion

Table 3: A summary of the simulation results: the network lifetime comparison. Figure 7: The simulation result of the network lifetime with an alive node graph for each round, with a maximum mobility range of up to 10 m/round: (**a**) Scenario 1 (BS = 100 m, 300 m), (**b**) Scenario 2 (BS = 100 m, 100 m). rounds, highlighting their limited scalability and lack of optimization. Under the mobility condition (Figure 9b), mobility intensifies energy imbalances across all protocols. Nevertheless, IK-MACHES remains the most effective, maintaining a balanced residual energy distribution even after 1000 rounds. PSO-based clustering and LEACH-GK exhibit moderate adaptability, though they struggle with uneven energy consumption caused by dynamic cluster reconfigurations. Classical LEACH and LEACH-B exhibit the poorest performance, with rapid energy depletion in most nodes due to their inability to account for mobility. LEACH-K and Improved-LEACH perform moderately but remain less resilient to mobility-induced challenges. When compared with Scenario 1 (Figure 8), Scenario 2 demonstrates improved energy retention for all protocols due to reduced communication distances with the centrally located BS. However, IK-MACHES consistently outperforms all other protocols across both scenarios, with its integration of mobility, residual energy, and proximity metrics ensuring a prolonged network lifetime and balanced energy consumption. These results emphasize the significance of incorporating mobility-aware mechanisms for energy-efficient clustering in dynamic WSN environments. Figure 8: Comparison of residual energy of nodes after 100 rounds, 500 rounds, and 1000 rounds for Scenario 1 (BS = 100 m, 300 m) with mobility: (**a**) no mobility and (**b**) maximum mobility range (10 m/round), depicting how energy sustainability varies by scenario.
# Research on Energy Harvesting Mechanism and Low Power Technology in Wireless Sensor Networks
###### Abstract  
Wireless sensor networks (WSNs) are widely used in various fields such as military, industrial, and transportation for real-time monitoring, sensing, and data collection of different environments or objects. However, the development of WSNs is hindered by several limitations, including energy, storage space, computing power, and data transmission rate. Among these, the availability of power energy plays a crucial role as it directly determines the lifespan of WSN. To extend the life cycle of WSN, two key approaches are power supply improvement and energy conservation. Therefore, we propose an energy harvesting system and a low-energy-consumption mechanism for WSNs. Firstly, we delved into the energy harvesting technology of WSNs, explored the utilization of solar energy and mechanical vibration energy to ensure a continuous and dependable power supply to the sensor nodes, and analyzed the voltage output characteristics of bistable piezoelectric cantilever. Secondly, we proposed a neighbor discovery mechanism that utilizes a separation beacon, is based on reply to ACK, and can facilitate the identification of neighboring nodes. This mechanism operates at a certain duty cycle ratio, significantly reduces idle listening time and results in substantial energy savings. In comparison to the Disco and U-connect protocols, our proposed mechanism achieved a remarkable reduction of 66.67% and 75% in the worst discovery delay, respectively. Furthermore, we introduced a data fusion mechanism based on integer wavelet transform. This mechanism effectively eliminates data redundancy caused by spatiotemporal correlation, resulting in a data compression rate of 5.42. Additionally, it significantly reduces energy consumption associated with data transmission by the nodes.  
wireless sensor networks; energy harvesting; neighbor discovery; data fusion 20242447. [[https://doi.org/10.3390/s24010047](https://doi.org/10.3390/s24010047)]([https://doi.org/10.3390/s24010047](https://doi.org/10.3390/s24010047))  
2
## 3 Background
### Word Embedding Techniques
#### 3.2.3 ELMo  
ELMo was introduced by [PERSON] et al. in [16] in 2018. ELMo is a bidirectional language model capable of generating contextualized vectors for a particular word by capturing both the semantics as well as the syntax for said word [19]. This means that similar words represented in different contexts are represented differently, which allows us to capture subtle differences in the language. The architecture of ELMo consists of two LSTM networks called the Forward layer and Backward layer. These layers are trained on the tasks of next-word prediction and previous-word prediction, respectively, and work in conjunction, thereby processing input in both directions and capturing meaningful relationships in both directions. The final word embedding is generated by concatenating these contextualized embeddings and scaling them with the help of a normalizing factor. These bidirectional layers are also supported by a task-specific layer on top which helps transform the embeddings from our layers into a suitable form for the task at hand, by usually projecting them into a lower dimension.  
Apart from generating contextualized vectors, ELMo offers another advantage over the previously mentioned techniques, wherein we do not necessarily have to handle out-of-vocabulary words as ELMo can create embeddings for these using character-level representations. This makes ELMo quite powerful in tasks that are highly domain-specific and may contain lots of out-of-vocabulary words. More information on ELMo can be found here [16].